From d9ba5e6d5ebd45c47380d862d030c36dcdf147fc Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Tue, 18 Nov 2025 10:34:14 +0100 Subject: [PATCH 01/28] add protein design notebooks --- notebooks/RFDiffusion.ipynb | 596 ++++++++++++++++++++++++++++++++++++ notebooks/bindcraft.ipynb | 332 ++++++++++++++++++++ notebooks/boltzgen.ipynb | 259 ++++++++++++++++ 3 files changed, 1187 insertions(+) create mode 100644 notebooks/RFDiffusion.ipynb create mode 100644 notebooks/bindcraft.ipynb create mode 100644 notebooks/boltzgen.ipynb diff --git a/notebooks/RFDiffusion.ipynb b/notebooks/RFDiffusion.ipynb new file mode 100644 index 0000000..4c062cc --- /dev/null +++ b/notebooks/RFDiffusion.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "1438b7cf-c150-4992-b82c-e6cde918c1e4", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "markdown", + "id": "6746740a-f598-4ba3-8edb-4e04e7f49be2", + "metadata": {}, + "source": [ + "### Optional Information\n", + "The RFDiffusion program is installed on the bwForCluster Helix and this notebook is just using their installation. If you want more details on available options, load the module by clicking on the hexagon icon on the far left of the bwVisu interface and load the bio/rfdiffusion/1.1.0 module by clicking on the Load button next to the entry. \n", + "\n", + "Execute the next cell to verify that the module is loaded. If bio/rfdiffusion/1.1.0 is not in the list, click on the bindcraft kernel on the top right of the window to reload the kernel. Select bindcraft and try again!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "69493ba2-248c-48f9-bc5d-2db40c1bf4b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Currently Loaded Modules:\n", + " 1) system/singularity/3.11.3 3) bio/rfdiffusion/1.1.0\n", + " 2) compiler/gnu/11.3\n", + "\n", + " \n", + "\n" + ] + } + ], + "source": [ + "! module list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe68f692-c3c9-44a3-90a1-a6d366e587f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "-------------- Module Specific Help for \"bio/rfdiffusion/1.1.0\" ---------------\n", + "\n", + "RFdiffusion is an open source method for structure generation, with or without \n", + "conditional information (a motif, target etc). It can perform a whole range of \n", + "protein design challenges.\n", + "\n", + "Online documentation:\n", + " https://github.com/RosettaCommons/RFdiffusion\n", + "\n", + "Material used in the documentation can be found in $RFDIFFUSION_HOME:\n", + " Scripts in $RFDIFFUSION_HOME/scripts\n", + " Examples in $RFDIFFUSION_HOME/examples\n", + " Model Weights in $RFDIFFUSION_HOME/models\n", + "\n", + "After loading this module you can start the main application\n", + "'$RFDIFFUSION_HOME/scripts/run_inference.py' simply with the \n", + "command 'run_inference.py'. To see the default config, type:\n", + "\n", + " run_inference.py -h\n", + "\n", + "An example batch script for RFdiffusion is available in:\n", + " $RFDIFFUSION_EXA_DIR\n", + "\u001b[7m--More--\u001b[m" + ] + } + ], + "source": [ + "# additional info in the RFDiffusion module\n", + "! module help bio/rfdiffusion/1.1.0\n", + "\n", + "#Material used in the documentation can be found in $RFDIFFUSION_HOME:\n", + "# Scripts in $RFDIFFUSION_HOME/scripts\n", + "# Examples in $RFDIFFUSION_HOME/examples\n", + "# Model Weights in $RFDIFFUSION_HOME/models" + ] + }, + { + "cell_type": "markdown", + "id": "a2a99882-292a-4b46-8ca9-5094541978bc", + "metadata": {}, + "source": [ + "### Starting the Calculation\n", + "\n", + "First we need to define the working directory. Everything we need will be copied from the examplex on the cluster into the working directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d06f381-f43f-4a5c-ae71-718b8cd228c3", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "WORKING_DIR = Path.home() /\"protein_design_RFDiffusion\"" + ] + }, + { + "cell_type": "markdown", + "id": "51835210-8ec5-435a-8310-163b310cf383", + "metadata": {}, + "source": [ + "Now we need to write information on the example. Looking at the examples in the RFDiffusion folder gives more options on what can be done." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cc230f4e-ef04-414d-831b-7091403c2560", + "metadata": {}, + "outputs": [], + "source": [ + "run_file = f'''\n", + "#!/bin/bash\n", + "\n", + "# Load software module \n", + "module load bio/rfdiffusion/1.1.0\n", + "\n", + "# bash $RFDIFFUSION_HOME/examples/design_ppi.sh - that does not work\n", + "\n", + "mkdir -p inputs\n", + "mkdir -p outputs\n", + "mkdir -p schedules\n", + "cp $RFDIFFUSION_HOME/examples/input_pdbs/5TPN.pdb inputs/\n", + "\n", + "HYDRA_FULL_ERROR=1 $RFDIFFUSION_HOME/scripts/run_inference.py \\\\\n", + " inference.input_pdb=./inputs/5TPN.pdb \\\\\n", + " inference.output_prefix=./outputs/motifscaffolding \\\\\n", + " inference.schedule_directory_path=./schedules \\\\\n", + " inference.num_designs=3 \\\\\n", + " 'contigmap.contigs=[10-40/A163-181/10-40]'\n", + "'''\n", + "\n", + "RUN_PATH = WORKING_DIR / \"run.sh\" # file name!\n", + "\n", + "with open(RUN_PATH, \"w\") as file: \n", + " file.write(run_file)" + ] + }, + { + "cell_type": "markdown", + "id": "b9d2e69a-f9f4-4c29-b3c5-3f8156e76167", + "metadata": {}, + "source": [ + "Now we start the calculation by executing the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "9af1ea5f-95c2-4df0-a326-f87ab10b28ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running file /home/hd/hd_hd/hd_aq354/protein_design_RFDiffusion/run.sh\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/gpfs/bwfor/software/common/bio/rfdiffusion/1.1.0/rfdiffusion/util.py:252: UserWarning: Using torch.cross without specifying the dim arg is deprecated.\n", + "Please either pass the dim explicitly or simply use torch.linalg.cross.\n", + "The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at /opt/conda/conda-bld/pytorch_1711403380164/work/aten/src/ATen/native/Cross.cpp:63.)\n", + " Z = torch.cross(Xn, Yn)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading models from /gpfs/bwfor/software/common/bio/rfdiffusion/1.1.0/rfdiffusion/inference/../../models\n", + "[2025-11-18 10:00:26,541][rfdiffusion.inference.model_runners][INFO] - Reading checkpoint from /gpfs/bwfor/software/common/bio/rfdiffusion/1.1.0/rfdiffusion/inference/../../models/Base_ckpt.pt\n", + "This is inf_conf.ckpt_path\n", + "/gpfs/bwfor/software/common/bio/rfdiffusion/1.1.0/rfdiffusion/inference/../../models/Base_ckpt.pt\n", + "Assembling -model, -diffuser and -preprocess configs from checkpoint\n", + "USING MODEL CONFIG: self._conf[model][n_extra_block] = 4\n", + "USING MODEL CONFIG: self._conf[model][n_main_block] = 32\n", + "USING MODEL CONFIG: self._conf[model][n_ref_block] = 4\n", + "USING MODEL CONFIG: self._conf[model][d_msa] = 256\n", + "USING MODEL CONFIG: self._conf[model][d_msa_full] = 64\n", + "USING MODEL CONFIG: self._conf[model][d_pair] = 128\n", + "USING MODEL CONFIG: self._conf[model][d_templ] = 64\n", + "USING MODEL CONFIG: self._conf[model][n_head_msa] = 8\n", + "USING MODEL CONFIG: self._conf[model][n_head_pair] = 4\n", + "USING MODEL CONFIG: self._conf[model][n_head_templ] = 4\n", + "USING MODEL CONFIG: self._conf[model][d_hidden] = 32\n", + "USING MODEL CONFIG: self._conf[model][d_hidden_templ] = 32\n", + "USING MODEL CONFIG: self._conf[model][p_drop] = 0.15\n", + "USING MODEL CONFIG: self._conf[model][SE3_param_full] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 8, 'l0_out_features': 8, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 32}\n", + "USING MODEL CONFIG: self._conf[model][SE3_param_topk] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 64, 'l0_out_features': 64, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 64}\n", + "USING MODEL CONFIG: self._conf[model][d_time_emb] = 0\n", + "USING MODEL CONFIG: self._conf[model][d_time_emb_proj] = 10\n", + "USING MODEL CONFIG: self._conf[model][freeze_track_motif] = False\n", + "USING MODEL CONFIG: self._conf[model][use_motif_timestep] = True\n", + "USING MODEL CONFIG: self._conf[diffuser][T] = 50\n", + "USING MODEL CONFIG: self._conf[diffuser][b_0] = 0.01\n", + "USING MODEL CONFIG: self._conf[diffuser][b_T] = 0.07\n", + "USING MODEL CONFIG: self._conf[diffuser][schedule_type] = linear\n", + "USING MODEL CONFIG: self._conf[diffuser][so3_type] = igso3\n", + "USING MODEL CONFIG: self._conf[diffuser][crd_scale] = 0.25\n", + "USING MODEL CONFIG: self._conf[diffuser][so3_schedule_type] = linear\n", + "USING MODEL CONFIG: self._conf[diffuser][min_b] = 1.5\n", + "USING MODEL CONFIG: self._conf[diffuser][max_b] = 2.5\n", + "USING MODEL CONFIG: self._conf[diffuser][min_sigma] = 0.02\n", + "USING MODEL CONFIG: self._conf[diffuser][max_sigma] = 1.5\n", + "USING MODEL CONFIG: self._conf[preprocess][sidechain_input] = False\n", + "USING MODEL CONFIG: self._conf[preprocess][motif_sidechain_input] = True\n", + "USING MODEL CONFIG: self._conf[preprocess][d_t1d] = 22\n", + "USING MODEL CONFIG: self._conf[preprocess][d_t2d] = 44\n", + "USING MODEL CONFIG: self._conf[preprocess][prob_self_cond] = 0.5\n", + "USING MODEL CONFIG: self._conf[preprocess][str_self_cond] = True\n", + "USING MODEL CONFIG: self._conf[preprocess][predict_previous] = False\n", + "[2025-11-18 10:00:29,619][rfdiffusion.inference.model_runners][INFO] - Loading checkpoint.\n", + "[2025-11-18 10:00:29,767][rfdiffusion.diffusion][INFO] - Calculating IGSO3.\n", + "Successful diffuser __init__\n", + "[2025-11-18 10:00:52,098][__main__][INFO] - Making design ./outputs/motifscaffolding_0\n", + "[2025-11-18 10:00:52,199][rfdiffusion.inference.model_runners][INFO] - Using contig: ['10-40/A163-181/10-40']\n", + "With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696050154976547\n", + "[2025-11-18 10:00:52,225][rfdiffusion.inference.model_runners][INFO] - Sequence init: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:54,241][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.31\n", + "[2025-11-18 10:00:54,253][rfdiffusion.inference.model_runners][INFO] - Timestep 50, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:54,684][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.30\n", + "[2025-11-18 10:00:54,686][rfdiffusion.inference.model_runners][INFO] - 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Sampled motif RMSD: 0.32\n", + "[2025-11-18 10:00:56,291][rfdiffusion.inference.model_runners][INFO] - Timestep 45, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:56,691][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.36\n", + "[2025-11-18 10:00:56,693][rfdiffusion.inference.model_runners][INFO] - Timestep 44, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:57,091][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.35\n", + "[2025-11-18 10:00:57,093][rfdiffusion.inference.model_runners][INFO] - Timestep 43, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:57,490][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.34\n", + "[2025-11-18 10:00:57,492][rfdiffusion.inference.model_runners][INFO] - Timestep 42, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:57,890][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.34\n", + "[2025-11-18 10:00:57,891][rfdiffusion.inference.model_runners][INFO] - Timestep 41, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:58,292][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.30\n", + "[2025-11-18 10:00:58,294][rfdiffusion.inference.model_runners][INFO] - Timestep 40, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:58,693][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:00:58,695][rfdiffusion.inference.model_runners][INFO] - Timestep 39, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:59,095][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:00:59,098][rfdiffusion.inference.model_runners][INFO] - Timestep 38, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:00:59,494][rfdiffusion.inference.utils][INFO] - 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Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:07,540][rfdiffusion.inference.model_runners][INFO] - Timestep 17, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:07,949][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:07,951][rfdiffusion.inference.model_runners][INFO] - Timestep 16, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:08,359][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:08,361][rfdiffusion.inference.model_runners][INFO] - Timestep 15, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:08,766][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:08,768][rfdiffusion.inference.model_runners][INFO] - Timestep 14, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:09,168][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:09,170][rfdiffusion.inference.model_runners][INFO] - Timestep 13, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:09,570][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.27\n", + "[2025-11-18 10:01:09,572][rfdiffusion.inference.model_runners][INFO] - Timestep 12, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:09,970][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:09,972][rfdiffusion.inference.model_runners][INFO] - Timestep 11, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:10,374][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.29\n", + "[2025-11-18 10:01:10,376][rfdiffusion.inference.model_runners][INFO] - Timestep 10, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:10,774][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.29\n", + "[2025-11-18 10:01:10,776][rfdiffusion.inference.model_runners][INFO] - Timestep 9, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:11,178][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.29\n", + "[2025-11-18 10:01:11,180][rfdiffusion.inference.model_runners][INFO] - Timestep 8, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:11,582][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:11,584][rfdiffusion.inference.model_runners][INFO] - Timestep 7, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:11,994][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.27\n", + "[2025-11-18 10:01:11,996][rfdiffusion.inference.model_runners][INFO] - Timestep 6, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:12,397][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:12,399][rfdiffusion.inference.model_runners][INFO] - Timestep 5, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:12,800][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:12,802][rfdiffusion.inference.model_runners][INFO] - Timestep 4, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:13,206][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:13,208][rfdiffusion.inference.model_runners][INFO] - Timestep 3, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:13,610][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.27\n", + "[2025-11-18 10:01:13,612][rfdiffusion.inference.model_runners][INFO] - Timestep 2, input to next step: ---------------------EVNKIKSALLSTNKAVVSL----------------------------------\n", + "[2025-11-18 10:01:14,715][__main__][INFO] - Finished design in 0.38 minutes\n", + "[2025-11-18 10:01:14,715][__main__][INFO] - Making design ./outputs/motifscaffolding_1\n", + "[2025-11-18 10:01:14,816][rfdiffusion.inference.model_runners][INFO] - Using contig: ['10-40/A163-181/10-40']\n", + "With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696050154976547\n", + "[2025-11-18 10:01:14,828][rfdiffusion.inference.model_runners][INFO] - Sequence init: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:15,232][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.30\n", + "[2025-11-18 10:01:15,234][rfdiffusion.inference.model_runners][INFO] - Timestep 50, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:15,633][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:15,635][rfdiffusion.inference.model_runners][INFO] - Timestep 49, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:16,033][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:16,035][rfdiffusion.inference.model_runners][INFO] - Timestep 48, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:16,437][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:16,439][rfdiffusion.inference.model_runners][INFO] - Timestep 47, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:16,837][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.30\n", + "[2025-11-18 10:01:16,839][rfdiffusion.inference.model_runners][INFO] - Timestep 46, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:17,236][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.29\n", + "[2025-11-18 10:01:17,238][rfdiffusion.inference.model_runners][INFO] - Timestep 45, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:17,634][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.27\n", + "[2025-11-18 10:01:17,636][rfdiffusion.inference.model_runners][INFO] - Timestep 44, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:18,043][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:18,045][rfdiffusion.inference.model_runners][INFO] - Timestep 43, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:18,445][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:18,447][rfdiffusion.inference.model_runners][INFO] - Timestep 42, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:18,846][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:18,848][rfdiffusion.inference.model_runners][INFO] - Timestep 41, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:19,247][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:19,248][rfdiffusion.inference.model_runners][INFO] - Timestep 40, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:19,648][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:19,651][rfdiffusion.inference.model_runners][INFO] - Timestep 39, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:20,054][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:20,056][rfdiffusion.inference.model_runners][INFO] - Timestep 38, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:20,456][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:20,458][rfdiffusion.inference.model_runners][INFO] - Timestep 37, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:20,856][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:20,857][rfdiffusion.inference.model_runners][INFO] - Timestep 36, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:21,257][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:21,259][rfdiffusion.inference.model_runners][INFO] - Timestep 35, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:21,659][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:21,661][rfdiffusion.inference.model_runners][INFO] - Timestep 34, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:22,087][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:22,088][rfdiffusion.inference.model_runners][INFO] - Timestep 33, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:22,486][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:22,488][rfdiffusion.inference.model_runners][INFO] - Timestep 32, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:22,895][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:22,897][rfdiffusion.inference.model_runners][INFO] - Timestep 31, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:23,300][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:23,301][rfdiffusion.inference.model_runners][INFO] - Timestep 30, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:23,702][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:23,704][rfdiffusion.inference.model_runners][INFO] - Timestep 29, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:24,105][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:24,107][rfdiffusion.inference.model_runners][INFO] - Timestep 28, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:24,510][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:24,512][rfdiffusion.inference.model_runners][INFO] - Timestep 27, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:24,912][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:24,914][rfdiffusion.inference.model_runners][INFO] - Timestep 26, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:25,315][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:25,317][rfdiffusion.inference.model_runners][INFO] - Timestep 25, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:25,718][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:25,720][rfdiffusion.inference.model_runners][INFO] - Timestep 24, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:26,117][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:26,119][rfdiffusion.inference.model_runners][INFO] - Timestep 23, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:26,518][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:26,520][rfdiffusion.inference.model_runners][INFO] - Timestep 22, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:26,917][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:26,919][rfdiffusion.inference.model_runners][INFO] - Timestep 21, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:27,318][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:27,320][rfdiffusion.inference.model_runners][INFO] - Timestep 20, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:27,716][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:27,718][rfdiffusion.inference.model_runners][INFO] - Timestep 19, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:28,118][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:28,120][rfdiffusion.inference.model_runners][INFO] - Timestep 18, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:28,519][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:28,520][rfdiffusion.inference.model_runners][INFO] - Timestep 17, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:28,920][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:28,922][rfdiffusion.inference.model_runners][INFO] - Timestep 16, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:29,323][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:29,325][rfdiffusion.inference.model_runners][INFO] - Timestep 15, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:29,726][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.24\n", + "[2025-11-18 10:01:29,728][rfdiffusion.inference.model_runners][INFO] - Timestep 14, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:30,130][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.23\n", + "[2025-11-18 10:01:30,132][rfdiffusion.inference.model_runners][INFO] - Timestep 13, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:30,533][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.22\n", + "[2025-11-18 10:01:30,535][rfdiffusion.inference.model_runners][INFO] - Timestep 12, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:30,935][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:30,937][rfdiffusion.inference.model_runners][INFO] - Timestep 11, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:31,337][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:31,339][rfdiffusion.inference.model_runners][INFO] - Timestep 10, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:31,742][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:31,744][rfdiffusion.inference.model_runners][INFO] - Timestep 9, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:32,154][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:32,156][rfdiffusion.inference.model_runners][INFO] - Timestep 8, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:32,557][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.19\n", + "[2025-11-18 10:01:32,559][rfdiffusion.inference.model_runners][INFO] - Timestep 7, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:32,966][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:32,968][rfdiffusion.inference.model_runners][INFO] - Timestep 6, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:33,371][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:33,373][rfdiffusion.inference.model_runners][INFO] - Timestep 5, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:33,770][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:33,772][rfdiffusion.inference.model_runners][INFO] - Timestep 4, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:34,173][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.21\n", + "[2025-11-18 10:01:34,175][rfdiffusion.inference.model_runners][INFO] - Timestep 3, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:34,576][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.20\n", + "[2025-11-18 10:01:34,578][rfdiffusion.inference.model_runners][INFO] - Timestep 2, input to next step: ----------------------------------------EVNKIKSALLSTNKAVVSL--------------------\n", + "[2025-11-18 10:01:35,694][__main__][INFO] - Finished design in 0.35 minutes\n", + "[2025-11-18 10:01:35,695][__main__][INFO] - Making design ./outputs/motifscaffolding_2\n", + "[2025-11-18 10:01:35,796][rfdiffusion.inference.model_runners][INFO] - Using contig: ['10-40/A163-181/10-40']\n", + "With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696050154976547\n", + "[2025-11-18 10:01:35,807][rfdiffusion.inference.model_runners][INFO] - Sequence init: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:36,208][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.28\n", + "[2025-11-18 10:01:36,210][rfdiffusion.inference.model_runners][INFO] - Timestep 50, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:36,609][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:36,611][rfdiffusion.inference.model_runners][INFO] - Timestep 49, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:37,009][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:37,011][rfdiffusion.inference.model_runners][INFO] - Timestep 48, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:37,406][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:37,408][rfdiffusion.inference.model_runners][INFO] - Timestep 47, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:37,804][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:37,806][rfdiffusion.inference.model_runners][INFO] - Timestep 46, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:38,201][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:38,203][rfdiffusion.inference.model_runners][INFO] - Timestep 45, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:38,601][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:38,603][rfdiffusion.inference.model_runners][INFO] - Timestep 44, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:39,003][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:39,005][rfdiffusion.inference.model_runners][INFO] - Timestep 43, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:39,404][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:39,406][rfdiffusion.inference.model_runners][INFO] - Timestep 42, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:39,805][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.25\n", + "[2025-11-18 10:01:39,807][rfdiffusion.inference.model_runners][INFO] - Timestep 41, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:40,216][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.26\n", + "[2025-11-18 10:01:40,217][rfdiffusion.inference.model_runners][INFO] - 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Timestep 4, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:55,111][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.31\n", + "[2025-11-18 10:01:55,113][rfdiffusion.inference.model_runners][INFO] - Timestep 3, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:55,513][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.31\n", + "[2025-11-18 10:01:55,515][rfdiffusion.inference.model_runners][INFO] - Timestep 2, input to next step: -------------------------------EVNKIKSALLSTNKAVVSL------------------\n", + "[2025-11-18 10:01:56,577][__main__][INFO] - Finished design in 0.35 minutes\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(f'echo \"Running file {RUN_PATH}\"')\n", + "os.system(f\"bash {RUN_PATH}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ec91da6-65d5-482d-ae15-b49a24f26e31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/bindcraft.ipynb b/notebooks/bindcraft.ipynb new file mode 100644 index 0000000..d0c7689 --- /dev/null +++ b/notebooks/bindcraft.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "9f800252-79ce-4048-b70e-2840182371e9", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os\n", + "import json\n", + "import sys\n", + "import subprocess" + ] + }, + { + "cell_type": "markdown", + "id": "2cc503de-8052-4cdf-b535-6d565cdd7336", + "metadata": {}, + "source": [ + "This tutorial follows the Bindcraft example on https://github.com/martinpacesa/BindCraft. \n", + "First we need to make sure all libraries are defined. Click on the hexagon icon on the far left of the bwVisu browser window and load the compiler/gnu/11.3 module by clicking on the Load button next to the entry. \n", + "\n", + "Execute the next cell to verify that the module is loaded. If compiler/gnu/11.3 is not in the list, click on the bindcraft kernel on the top right of the window to reload the kernel. Select bindcraft and try again!" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6c79622f-88ca-4c30-9655-1c6c2d7b545f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Currently Loaded Modules:\n", + " 1) system/singularity/3.11.3 2) compiler/gnu/11.3\n", + "\n", + " \n", + "\n" + ] + } + ], + "source": [ + "# export paths for pyrosetta\n", + "! export LD_LIBRARY_PATH=./mnt/sds-hd/sd25g005/bindcraft/lib:$LD_LIBRARY_PATH\n", + "\n", + "# test if modules and fortran libraries are loaded\n", + "! module list\n", + "# ! ls $GNU_LIB_DIR" + ] + }, + { + "cell_type": "markdown", + "id": "eba0dd7d-3e9d-40ed-b1fb-84b3ef6d784f", + "metadata": {}, + "source": [ + "First we need to define the working directory, and upload the PDL1.pdb file to the working directory." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b5fad8c4-0e2a-40b1-823b-24c7e376d0ed", + "metadata": {}, + "outputs": [], + "source": [ + "WORKING_DIR = Path.home() / \"protein_design_w_Bindcraft\" # created by user\n" + ] + }, + { + "cell_type": "markdown", + "id": "344c238e-9243-4b0b-a5c6-b3edc916fb9e", + "metadata": {}, + "source": [ + "Now we need to define the input file. For a detailed explanation on the parameters, see the [Bindcraft github](https://github.com/martinpacesa/BindCraft?tab=readme-ov-file#running-the-script-locally-and-explanation-of-settings)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c2c6e298-bd3a-45b0-9093-ae4819a8bb58", + "metadata": {}, + "outputs": [], + "source": [ + "data = {\n", + " \"design_path\": str(WORKING_DIR), \n", + " \"binder_name\": \"PDL1\", \n", + " \"starting_pdb\": str(WORKING_DIR / \"PDL1.pdb\"), \n", + " \"chains\": \"A\", \n", + " \"target_hotspot_residues\": \"56\", \n", + " \"lengths\": [65, 150],\n", + " \"number_of_final_designs\": 100 \n", + "}\n", + "\n", + "bindcraft_settings = WORKING_DIR / \"input.json\"\n", + "\n", + "with open(bindcraft_settings, \"w\") as json_file: \n", + " json.dump(data, json_file, indent=4) # Use indent for pretty-printing\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "37f41a3d-c53a-4380-a0ac-fa2602d9bcd8", + "metadata": {}, + "source": [ + "Now we need to link to the Bindcraft directory in the s25g005 Speichervorhaben. If you want to use other filters or advanced settings, choose them from the list of files." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "fd7c9c83-0ff6-4cb2-9b6d-cdc480e99f4b", + "metadata": {}, + "outputs": [], + "source": [ + "BINDCRAFT_DIR = Path(\"/mnt/sds-hd/sd25g005/bindcraft/bindcraft\")\n", + "\n", + "BINDCRAFT_PY = BINDCRAFT_DIR / \"bindcraft.py\"\n", + "BINDCRAFT_FILTERS = BINDCRAFT_DIR / \"settings_filters\" / \"default_filters.json\"\n", + "BINDCRAFT_ADVANCED = BINDCRAFT_DIR / \"settings_advanced\" / \"default_4stage_multimer.json\"\n", + "\n", + "subprocess_command = [sys.executable, \"-u\", BINDCRAFT_PY,\n", + " \"--settings\", bindcraft_settings,\n", + " \"--filters\", BINDCRAFT_FILTERS,\n", + " \"--advanced\", BINDCRAFT_ADVANCED]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "779058cc-db9c-4105-8150-36783ab89eb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/sds-hd/sd25g005/bindcraft\n" + ] + } + ], + "source": [ + "print(BINDCRAFT_DIR)" + ] + }, + { + "cell_type": "markdown", + "id": "263fd369-fcbb-477d-9bcd-8a70bc7664e7", + "metadata": {}, + "source": [ + "Now we can start the Bindcraft run by executing the cells below." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "bbb52775-e65b-4c81-8486-d2702f620f9a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available GPUs:\n", + "NVIDIA A401: gpu\n", + "┌───────────────────────────────────────────────────────────────────────────────┐\n", + "│ PyRosetta-4 │\n", + "│ Created in JHU by Sergey Lyskov and PyRosetta Team │\n", + "│ (C) Copyright Rosetta Commons Member Institutions │\n", + "│ │\n", + "│ NOTE: USE OF PyRosetta FOR COMMERCIAL PURPOSES REQUIRES PURCHASE OF A LICENSE │\n", + "│ See LICENSE.PyRosetta.md or email license@uw.edu for details │\n", + "└───────────────────────────────────────────────────────────────────────────────┘\n", + "PyRosetta-4 2025 [Rosetta PyRosetta4.conda.ubuntu.cxx11thread.serialization.Ubuntu.python310.Release 2025.45+release.d79cb06334818403e40289334138fb126753c253 2025-11-04T12:19:41] retrieved from: http://www.pyrosetta.org\n", + "Running binder design for target input\n", + "Design settings used: default_4stage_multimer\n", + "Filtering designs based on default_filters\n", + "Starting trajectory: PDL1_l144_s794724\n", + "Stage 1: Test Logits\n", + "1 models [0] recycles 1 hard 0 soft 0.02 temp 1 loss 13.99 helix 1.63 pae 0.87 i_pae 0.89 con 4.92 i_con 4.38 plddt 0.35 ptm 0.44 i_ptm 0.10 rg 15.45\n", + "2 models [3] recycles 1 hard 0 soft 0.04 temp 1 loss 11.32 helix 0.94 pae 0.76 i_pae 0.80 con 4.26 i_con 4.37 plddt 0.40 ptm 0.45 i_ptm 0.11 rg 8.27\n", + "3 models [0] recycles 1 hard 0 soft 0.05 temp 1 loss 8.80 helix 0.77 pae 0.68 i_pae 0.74 con 3.87 i_con 4.07 plddt 0.42 ptm 0.47 i_ptm 0.14 rg 2.18\n", + "4 models [2] recycles 1 hard 0 soft 0.07 temp 1 loss 9.06 helix 0.66 pae 0.69 i_pae 0.78 con 3.83 i_con 4.29 plddt 0.44 ptm 0.45 i_ptm 0.11 rg 2.30\n", + "5 models [1] recycles 1 hard 0 soft 0.09 temp 1 loss 8.38 helix 0.77 pae 0.60 i_pae 0.77 con 3.37 i_con 4.20 plddt 0.52 ptm 0.46 i_ptm 0.12 rg 2.10\n", + "6 models [4] recycles 1 hard 0 soft 0.11 temp 1 loss 7.68 helix 0.65 pae 0.51 i_pae 0.64 con 2.91 i_con 3.79 plddt 0.63 ptm 0.48 i_ptm 0.18 rg 2.76\n", + "7 models [4] recycles 1 hard 0 soft 0.13 temp 1 loss 7.53 helix 0.69 pae 0.53 i_pae 0.66 con 2.93 i_con 3.70 plddt 0.61 ptm 0.48 i_ptm 0.17 rg 2.53\n", + "8 models [2] recycles 1 hard 0 soft 0.14 temp 1 loss 6.99 helix 0.71 pae 0.44 i_pae 0.57 con 2.53 i_con 3.56 plddt 0.73 ptm 0.52 i_ptm 0.22 rg 2.70\n", + "9 models [4] recycles 1 hard 0 soft 0.16 temp 1 loss 6.71 helix 0.76 pae 0.45 i_pae 0.58 con 2.43 i_con 3.42 plddt 0.72 ptm 0.51 i_ptm 0.20 rg 2.60\n", + "10 models [4] recycles 1 hard 0 soft 0.18 temp 1 loss 8.58 helix 0.78 pae 0.62 i_pae 0.62 con 3.77 i_con 3.76 plddt 0.47 ptm 0.51 i_ptm 0.21 rg 2.96\n", + "11 models [3] recycles 1 hard 0 soft 0.20 temp 1 loss 7.59 helix 0.89 pae 0.53 i_pae 0.59 con 3.08 i_con 3.64 plddt 0.57 ptm 0.52 i_ptm 0.22 rg 2.60\n", + "12 models [1] recycles 1 hard 0 soft 0.22 temp 1 loss 8.09 helix 0.84 pae 0.55 i_pae 0.59 con 3.51 i_con 3.69 plddt 0.54 ptm 0.51 i_ptm 0.22 rg 2.61\n", + "13 models [4] recycles 1 hard 0 soft 0.23 temp 1 loss 7.50 helix 0.78 pae 0.49 i_pae 0.52 con 3.21 i_con 3.46 plddt 0.59 ptm 0.53 i_ptm 0.25 rg 2.47\n", + "14 models [4] recycles 1 hard 0 soft 0.25 temp 1 loss 6.06 helix 0.84 pae 0.37 i_pae 0.40 con 2.35 i_con 2.91 plddt 0.77 ptm 0.61 i_ptm 0.37 rg 2.69\n", + "15 models [2] recycles 1 hard 0 soft 0.27 temp 1 loss 6.36 helix 0.83 pae 0.39 i_pae 0.45 con 2.39 i_con 3.17 plddt 0.77 ptm 0.58 i_ptm 0.32 rg 2.65\n", + "16 models [4] recycles 1 hard 0 soft 0.29 temp 1 loss 5.92 helix 0.92 pae 0.33 i_pae 0.41 con 2.00 i_con 3.12 plddt 0.82 ptm 0.64 i_ptm 0.35 rg 2.84\n", + "17 models [2] recycles 1 hard 0 soft 0.31 temp 1 loss 5.42 helix 1.00 pae 0.27 i_pae 0.31 con 1.95 i_con 2.76 plddt 0.86 ptm 0.72 i_ptm 0.55 rg 2.80\n", + "18 models [2] recycles 1 hard 0 soft 0.32 temp 1 loss 6.46 helix 0.92 pae 0.43 i_pae 0.60 con 2.05 i_con 3.62 plddt 0.82 ptm 0.55 i_ptm 0.17 rg 2.62\n", + "19 models [4] recycles 1 hard 0 soft 0.34 temp 1 loss 7.28 helix 0.81 pae 0.49 i_pae 0.58 con 2.68 i_con 3.71 plddt 0.64 ptm 0.51 i_ptm 0.20 rg 2.66\n", + "20 models [0] recycles 1 hard 0 soft 0.36 temp 1 loss 6.64 helix 0.93 pae 0.40 i_pae 0.48 con 2.45 i_con 3.40 plddt 0.72 ptm 0.56 i_ptm 0.31 rg 2.69\n", + "21 models [4] recycles 1 hard 0 soft 0.38 temp 1 loss 7.45 helix 0.89 pae 0.53 i_pae 0.66 con 2.66 i_con 3.87 plddt 0.62 ptm 0.48 i_ptm 0.17 rg 2.77\n", + "22 models [3] recycles 1 hard 0 soft 0.40 temp 1 loss 6.54 helix 0.91 pae 0.39 i_pae 0.44 con 2.46 i_con 3.26 plddt 0.73 ptm 0.58 i_ptm 0.37 rg 2.77\n", + "23 models [0] recycles 1 hard 0 soft 0.41 temp 1 loss 6.47 helix 0.95 pae 0.37 i_pae 0.47 con 2.34 i_con 3.29 plddt 0.76 ptm 0.57 i_ptm 0.33 rg 2.89\n", + "24 models [1] recycles 1 hard 0 soft 0.43 temp 1 loss 5.32 helix 1.06 pae 0.25 i_pae 0.24 con 2.15 i_con 2.42 plddt 0.81 ptm 0.73 i_ptm 0.64 rg 3.02\n", + "25 models [3] recycles 1 hard 0 soft 0.45 temp 1 loss 5.77 helix 0.97 pae 0.30 i_pae 0.31 con 2.27 i_con 2.69 plddt 0.77 ptm 0.66 i_ptm 0.55 rg 3.00\n", + "26 models [3] recycles 1 hard 0 soft 0.47 temp 1 loss 5.44 helix 0.98 pae 0.26 i_pae 0.23 con 2.27 i_con 2.44 plddt 0.80 ptm 0.75 i_ptm 0.62 rg 2.87\n", + "27 models [4] recycles 1 hard 0 soft 0.49 temp 1 loss 5.05 helix 0.97 pae 0.24 i_pae 0.21 con 2.09 i_con 2.25 plddt 0.82 ptm 0.76 i_ptm 0.65 rg 2.84\n", + "28 models [4] recycles 1 hard 0 soft 0.50 temp 1 loss 4.53 helix 1.06 pae 0.21 i_pae 0.17 con 1.95 i_con 1.98 plddt 0.86 ptm 0.81 i_ptm 0.74 rg 2.67\n", + "29 models [0] recycles 1 hard 0 soft 0.52 temp 1 loss 4.44 helix 1.12 pae 0.19 i_pae 0.16 con 1.86 i_con 1.98 plddt 0.88 ptm 0.82 i_ptm 0.76 rg 2.74\n", + "30 models [3] recycles 1 hard 0 soft 0.54 temp 1 loss 6.64 helix 0.96 pae 0.39 i_pae 0.52 con 2.31 i_con 3.52 plddt 0.76 ptm 0.54 i_ptm 0.24 rg 2.75\n", + "31 models [1] recycles 1 hard 0 soft 0.56 temp 1 loss 5.55 helix 1.07 pae 0.29 i_pae 0.30 con 2.14 i_con 2.71 plddt 0.80 ptm 0.68 i_ptm 0.50 rg 2.75\n", + "32 models [2] recycles 1 hard 0 soft 0.58 temp 1 loss 4.82 helix 0.97 pae 0.24 i_pae 0.20 con 2.07 i_con 2.04 plddt 0.84 ptm 0.79 i_ptm 0.72 rg 2.87\n", + "33 models [4] recycles 1 hard 0 soft 0.59 temp 1 loss 4.44 helix 1.08 pae 0.19 i_pae 0.15 con 1.91 i_con 1.90 plddt 0.87 ptm 0.82 i_ptm 0.77 rg 2.76\n", + "34 models [0] recycles 1 hard 0 soft 0.61 temp 1 loss 4.33 helix 1.24 pae 0.19 i_pae 0.15 con 1.81 i_con 1.93 plddt 0.88 ptm 0.84 i_ptm 0.78 rg 2.83\n", + "35 models [1] recycles 1 hard 0 soft 0.63 temp 1 loss 4.36 helix 1.07 pae 0.21 i_pae 0.17 con 1.90 i_con 1.80 plddt 0.86 ptm 0.83 i_ptm 0.80 rg 2.83\n", + "36 models [0] recycles 1 hard 0 soft 0.65 temp 1 loss 4.35 helix 1.17 pae 0.19 i_pae 0.15 con 1.91 i_con 1.81 plddt 0.87 ptm 0.84 i_ptm 0.79 rg 2.88\n", + "37 models [1] recycles 1 hard 0 soft 0.67 temp 1 loss 4.18 helix 1.26 pae 0.20 i_pae 0.15 con 1.85 i_con 1.76 plddt 0.87 ptm 0.84 i_ptm 0.79 rg 2.74\n", + "38 models [4] recycles 1 hard 0 soft 0.68 temp 1 loss 4.16 helix 1.14 pae 0.19 i_pae 0.15 con 1.83 i_con 1.74 plddt 0.88 ptm 0.84 i_ptm 0.80 rg 2.76\n", + "39 models [3] recycles 1 hard 0 soft 0.70 temp 1 loss 3.94 helix 1.15 pae 0.18 i_pae 0.14 con 1.77 i_con 1.58 plddt 0.89 ptm 0.86 i_ptm 0.82 rg 2.77\n", + "40 models [0] recycles 1 hard 0 soft 0.72 temp 1 loss 4.12 helix 1.18 pae 0.19 i_pae 0.16 con 1.72 i_con 1.83 plddt 0.89 ptm 0.83 i_ptm 0.78 rg 2.70\n", + "41 models [3] recycles 1 hard 0 soft 0.74 temp 1 loss 3.82 helix 1.22 pae 0.17 i_pae 0.13 con 1.65 i_con 1.62 plddt 0.90 ptm 0.86 i_ptm 0.83 rg 2.73\n", + "42 models [2] recycles 1 hard 0 soft 0.76 temp 1 loss 7.47 helix 0.83 pae 0.54 i_pae 0.66 con 2.70 i_con 3.92 plddt 0.62 ptm 0.47 i_ptm 0.16 rg 2.46\n", + "43 models [1] recycles 1 hard 0 soft 0.77 temp 1 loss 4.58 helix 1.11 pae 0.22 i_pae 0.18 con 1.86 i_con 2.10 plddt 0.86 ptm 0.81 i_ptm 0.73 rg 2.76\n", + "44 models [3] recycles 1 hard 0 soft 0.79 temp 1 loss 3.71 helix 1.24 pae 0.17 i_pae 0.13 con 1.62 i_con 1.56 plddt 0.91 ptm 0.87 i_ptm 0.83 rg 2.66\n", + "45 models [3] recycles 1 hard 0 soft 0.81 temp 1 loss 3.81 helix 1.24 pae 0.18 i_pae 0.13 con 1.62 i_con 1.66 plddt 0.90 ptm 0.86 i_ptm 0.82 rg 2.63\n", + "46 models [4] recycles 1 hard 0 soft 0.83 temp 1 loss 3.95 helix 1.24 pae 0.19 i_pae 0.14 con 1.74 i_con 1.66 plddt 0.89 ptm 0.85 i_ptm 0.81 rg 2.73\n", + "47 models [1] recycles 1 hard 0 soft 0.85 temp 1 loss 3.58 helix 1.28 pae 0.18 i_pae 0.13 con 1.70 i_con 1.38 plddt 0.89 ptm 0.87 i_ptm 0.85 rg 2.63\n", + "48 models [3] recycles 1 hard 0 soft 0.86 temp 1 loss 3.75 helix 1.21 pae 0.19 i_pae 0.14 con 1.72 i_con 1.48 plddt 0.89 ptm 0.86 i_ptm 0.82 rg 2.67\n", + "49 models [4] recycles 1 hard 0 soft 0.88 temp 1 loss 5.05 helix 1.22 pae 0.23 i_pae 0.23 con 1.83 i_con 2.64 plddt 0.86 ptm 0.75 i_ptm 0.59 rg 2.68\n", + "50 models [2] recycles 1 hard 0 soft 0.90 temp 1 loss 8.59 helix 0.83 pae 0.66 i_pae 0.72 con 3.70 i_con 4.06 plddt 0.45 ptm 0.47 i_ptm 0.14 rg 2.15\n", + "Initial trajectory pLDDT good, continuing: 0.89\n", + "Stage 1: Additional Logits Optimisation\n", + "51 models [2] recycles 1 hard 0 soft 0.04 temp 1 loss 9.91 helix 1.49 pae 0.83 i_pae 0.81 con 4.50 i_con 4.21 plddt 0.27 ptm 0.44 i_ptm 0.10 rg 3.70\n", + "52 models [4] recycles 1 hard 0 soft 0.08 temp 1 loss 5.24 helix 1.03 pae 0.26 i_pae 0.24 con 1.99 i_con 2.58 plddt 0.82 ptm 0.72 i_ptm 0.59 rg 2.68\n", + "53 models [0] recycles 1 hard 0 soft 0.12 temp 1 loss 6.77 helix 0.98 pae 0.43 i_pae 0.54 con 2.42 i_con 3.63 plddt 0.71 ptm 0.52 i_ptm 0.23 rg 2.40\n", + "54 models [1] recycles 1 hard 0 soft 0.16 temp 1 loss 3.96 helix 1.25 pae 0.19 i_pae 0.15 con 1.70 i_con 1.74 plddt 0.89 ptm 0.85 i_ptm 0.80 rg 2.62\n", + "55 models [0] recycles 1 hard 0 soft 0.20 temp 1 loss 3.90 helix 1.24 pae 0.18 i_pae 0.14 con 1.69 i_con 1.69 plddt 0.90 ptm 0.86 i_ptm 0.83 rg 2.63\n", + "56 models [3] recycles 1 hard 0 soft 0.24 temp 1 loss 3.86 helix 1.31 pae 0.18 i_pae 0.14 con 1.59 i_con 1.78 plddt 0.91 ptm 0.86 i_ptm 0.81 rg 2.59\n", + "57 models [2] recycles 1 hard 0 soft 0.28 temp 1 loss 4.32 helix 1.19 pae 0.20 i_pae 0.17 con 1.85 i_con 1.92 plddt 0.88 ptm 0.83 i_ptm 0.77 rg 2.64\n", + "58 models [2] recycles 1 hard 0 soft 0.32 temp 1 loss 4.45 helix 1.10 pae 0.22 i_pae 0.17 con 2.09 i_con 1.72 plddt 0.84 ptm 0.82 i_ptm 0.81 rg 2.78\n", + "59 models [1] recycles 1 hard 0 soft 0.36 temp 1 loss 3.75 helix 1.24 pae 0.18 i_pae 0.14 con 1.72 i_con 1.54 plddt 0.89 ptm 0.87 i_ptm 0.85 rg 2.52\n", + "60 models [1] recycles 1 hard 0 soft 0.40 temp 1 loss 3.54 helix 1.41 pae 0.17 i_pae 0.13 con 1.58 i_con 1.54 plddt 0.91 ptm 0.88 i_ptm 0.86 rg 2.49\n", + "61 models [2] recycles 1 hard 0 soft 0.44 temp 1 loss 3.40 helix 1.41 pae 0.15 i_pae 0.12 con 1.47 i_con 1.51 plddt 0.93 ptm 0.89 i_ptm 0.85 rg 2.53\n", + "62 models [0] recycles 1 hard 0 soft 0.48 temp 1 loss 3.55 helix 1.35 pae 0.16 i_pae 0.13 con 1.54 i_con 1.55 plddt 0.92 ptm 0.87 i_ptm 0.84 rg 2.56\n", + "63 models [1] recycles 1 hard 0 soft 0.52 temp 1 loss 4.57 helix 1.28 pae 0.21 i_pae 0.16 con 2.02 i_con 2.04 plddt 0.84 ptm 0.81 i_ptm 0.75 rg 2.57\n", + "64 models [4] recycles 1 hard 0 soft 0.56 temp 1 loss 3.73 helix 1.30 pae 0.16 i_pae 0.13 con 1.59 i_con 1.66 plddt 0.92 ptm 0.88 i_ptm 0.83 rg 2.59\n", + "65 models [1] recycles 1 hard 0 soft 0.60 temp 1 loss 3.35 helix 1.43 pae 0.15 i_pae 0.12 con 1.50 i_con 1.46 plddt 0.93 ptm 0.89 i_ptm 0.86 rg 2.43\n", + "66 models [0] recycles 1 hard 0 soft 0.64 temp 1 loss 3.48 helix 1.39 pae 0.15 i_pae 0.12 con 1.54 i_con 1.52 plddt 0.92 ptm 0.89 i_ptm 0.86 rg 2.49\n", + "67 models [1] recycles 1 hard 0 soft 0.68 temp 1 loss 6.97 helix 0.98 pae 0.47 i_pae 0.59 con 2.39 i_con 3.80 plddt 0.68 ptm 0.49 i_ptm 0.17 rg 2.52\n", + "68 models [0] recycles 1 hard 0 soft 0.72 temp 1 loss 3.80 helix 1.36 pae 0.17 i_pae 0.13 con 1.62 i_con 1.72 plddt 0.91 ptm 0.86 i_ptm 0.81 rg 2.55\n", + "69 models [3] recycles 1 hard 0 soft 0.76 temp 1 loss 5.87 helix 1.10 pae 0.36 i_pae 0.48 con 1.91 i_con 3.29 plddt 0.81 ptm 0.58 i_ptm 0.24 rg 2.53\n", + "70 models [0] recycles 1 hard 0 soft 0.80 temp 1 loss 6.03 helix 1.27 pae 0.38 i_pae 0.58 con 1.74 i_con 3.67 plddt 0.84 ptm 0.57 i_ptm 0.20 rg 2.47\n", + "71 models [4] recycles 1 hard 0 soft 0.84 temp 1 loss 7.07 helix 0.99 pae 0.50 i_pae 0.62 con 2.47 i_con 3.78 plddt 0.65 ptm 0.48 i_ptm 0.16 rg 2.62\n", + "72 models [4] recycles 1 hard 0 soft 0.88 temp 1 loss 6.61 helix 0.92 pae 0.44 i_pae 0.53 con 2.34 i_con 3.48 plddt 0.71 ptm 0.51 i_ptm 0.23 rg 2.58\n", + "73 models [4] recycles 1 hard 0 soft 0.92 temp 1 loss 6.26 helix 0.96 pae 0.41 i_pae 0.45 con 2.37 i_con 3.13 plddt 0.70 ptm 0.56 i_ptm 0.32 rg 2.59\n", + "74 models [3] recycles 1 hard 0 soft 0.96 temp 1 loss 6.44 helix 0.96 pae 0.41 i_pae 0.48 con 2.32 i_con 3.31 plddt 0.71 ptm 0.54 i_ptm 0.29 rg 2.74\n", + "75 models [0] recycles 1 hard 0 soft 1 temp 1 loss 4.89 helix 1.08 pae 0.23 i_pae 0.20 con 1.95 i_con 2.28 plddt 0.84 ptm 0.78 i_ptm 0.69 rg 2.82\n", + "Optimised logit trajectory pLDDT: 0.93\n", + "Stage 2: Softmax Optimisation\n", + "76 models [0] recycles 1 hard 0 soft 1 temp 0.96 loss 4.56 helix 1.05 pae 0.22 i_pae 0.20 con 1.91 i_con 2.01 plddt 0.84 ptm 0.81 i_ptm 0.74 rg 2.73\n", + "77 models [1] recycles 1 hard 0 soft 1 temp 0.91 loss 6.56 helix 0.99 pae 0.44 i_pae 0.51 con 2.34 i_con 3.40 plddt 0.70 ptm 0.53 i_ptm 0.24 rg 2.72\n", + "78 models [3] recycles 1 hard 0 soft 1 temp 0.87 loss 6.46 helix 0.99 pae 0.41 i_pae 0.49 con 2.34 i_con 3.35 plddt 0.71 ptm 0.55 i_ptm 0.28 rg 2.65\n", + "79 models [2] recycles 1 hard 0 soft 1 temp 0.83 loss 7.08 helix 0.95 pae 0.49 i_pae 0.65 con 2.32 i_con 3.95 plddt 0.69 ptm 0.47 i_ptm 0.15 rg 2.52\n", + "80 models [4] recycles 1 hard 0 soft 1 temp 0.79 loss 6.88 helix 0.94 pae 0.48 i_pae 0.58 con 2.38 i_con 3.68 plddt 0.69 ptm 0.49 i_ptm 0.19 rg 2.59\n", + "81 models [1] recycles 1 hard 0 soft 1 temp 0.75 loss 6.61 helix 0.95 pae 0.42 i_pae 0.52 con 2.26 i_con 3.54 plddt 0.72 ptm 0.53 i_ptm 0.24 rg 2.70\n", + "82 models [1] recycles 1 hard 0 soft 1 temp 0.72 loss 6.67 helix 0.93 pae 0.42 i_pae 0.54 con 2.26 i_con 3.55 plddt 0.73 ptm 0.53 i_ptm 0.20 rg 2.81\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubprocess_command\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:503\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Popen(\u001b[38;5;241m*\u001b[39mpopenargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mas\u001b[39;00m process:\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 503\u001b[0m stdout, stderr \u001b[38;5;241m=\u001b[39m \u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommunicate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TimeoutExpired \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 505\u001b[0m process\u001b[38;5;241m.\u001b[39mkill()\n", + "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1141\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m 1139\u001b[0m stderr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1141\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1204\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1202\u001b[0m endtime \u001b[38;5;241m=\u001b[39m _time() \u001b[38;5;241m+\u001b[39m timeout\n\u001b[1;32m 1203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1205\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m 1209\u001b[0m \u001b[38;5;66;03m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1938\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1936\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1937\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m \u001b[38;5;66;03m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1938\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1939\u001b[0m \u001b[38;5;66;03m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m 1940\u001b[0m \u001b[38;5;66;03m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;66;03m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m 1942\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pid \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid:\n", + "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1896\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m 1895\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1896\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitpid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait_flags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1897\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mChildProcessError\u001b[39;00m:\n\u001b[1;32m 1898\u001b[0m \u001b[38;5;66;03m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m 1899\u001b[0m \u001b[38;5;66;03m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m 1900\u001b[0m \u001b[38;5;66;03m# process. This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m 1901\u001b[0m pid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "subprocess.run(subprocess_command)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b1c8a12-b72b-4fab-b677-652948fad541", + "metadata": {}, + "outputs": [], + "source": [ + "# Known issues\n", + "#ffmpeg https://github.com/martinpacesa/BindCraft/issues/20 \n", + "#runtime https://github.com/martinpacesa/BindCraft/issues/83" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bindcraft", + "language": "python", + "name": "bindcraft" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/boltzgen.ipynb b/notebooks/boltzgen.ipynb new file mode 100644 index 0000000..0b19fcd --- /dev/null +++ b/notebooks/boltzgen.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e70678aa-93bb-4f5e-a902-c222c487f51a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "eeba847d-5964-4847-b052-ff15fe6bceca", + "metadata": {}, + "source": [ + "This Tutorial follows the Boltzgen example vanilla protein. For details see https://github.com/HannesStark/boltzgen " + ] + }, + { + "cell_type": "markdown", + "id": "2f49ba46-5007-4aac-920d-2a68c0bfc9bf", + "metadata": {}, + "source": [ + "First we need to define the working directory, and upload the 1g13.cif file to the working directory." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6801c2b4-fb07-483e-bf8e-2415ffaae339", + "metadata": {}, + "outputs": [], + "source": [ + "WORKING_DIR = Path.home() / \"protein_design_w_Boltzgen\" # created by user\n" + ] + }, + { + "cell_type": "markdown", + "id": "db03bfa2-1a18-4dba-942e-80d4f5d3d7ee", + "metadata": {}, + "source": [ + "You do not need to edit the cell below. Here we set up the environment and give names to input and output files." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f2cf3940-5c51-47f8-98a3-dc980fced6e6", + "metadata": {}, + "outputs": [], + "source": [ + "BOTLZGEN_INPUT_FILE = \"input.yaml\" # will be created in this notebook\n", + "BOLTZGEN_RUN_FILE = \"run.sh\" # will be created in this notebook\n", + "BOLTZGEN_YAML_PATH = (\n", + " WORKING_DIR / BOTLZGEN_INPUT_FILE\n", + ") # will be created in this notebook\n", + "BOLTZGEN_RUN_PATH = (\n", + " WORKING_DIR / BOLTZGEN_RUN_FILE\n", + ") # will be created in this notebook\n", + "\n", + "RESULTS_DIR = (\n", + " WORKING_DIR / \"workbench/test_run\" \n", + ") # will be created in this notebook\n", + "\n", + "\n", + "# check that the directories exist, if not print a warning\n", + "if not os.path.isdir(WORKING_DIR):\n", + " print(\n", + " f\"Directory {WORKING_DIR} does not exist! Please create this directory.\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "96432fc4-5eb8-42fb-82f6-19953d3bd879", + "metadata": {}, + "source": [ + "### Input File\n", + "For each run, you need to provide an input yaml file. The structure of these files and examples on how to use them can be found in the [Boltzgen github](https://github.com/HannesStark/boltzgen?tab=readme-ov-file#how-to-make-a-design-specification-yaml), with detailed explanations [here](https://github.com/HannesStark/boltzgen/blob/main/example/README.md). Note that larger prediction runs might require larger GPUs, which you can set in the start of the jupyter session on bwVisu. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "02a5d38b-5249-4cee-8e69-a78eca1ded7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/hd/hd_hd/hd_aq354/protein_design/input.yaml\n", + "File written to /home/hd/hd_hd/hd_aq354/protein_design/input.yaml.\n" + ] + } + ], + "source": [ + "input_yaml = '''\n", + "entities:\n", + " - protein: \n", + " id: C\n", + " sequence: 80..140\n", + " - file:\n", + " path: 1g13.cif\n", + " \n", + " include: \n", + " - chain:\n", + " id: A\n", + "'''\n", + "with open(BOLTZGEN_YAML_PATH, \"w\") as file: \n", + " print(BOLTZGEN_YAML_PATH)\n", + " file.write(input_yaml)\n", + " print(f\"File written to {BOLTZGEN_YAML_PATH}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "496d7f8f-30ce-4906-9116-0a536833802b", + "metadata": {}, + "source": [ + "Now we combine the input file with the information on input and output directories to start the calculation. More information on available pipelines can be found in the [Boltzgen github](https://github.com/HannesStark/boltzgen?tab=readme-ov-file#all-command-line-arguments)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dae5c5ba-19de-4942-a859-ef58a5cc76df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to /home/hd/hd_hd/hd_aq354/protein_design/run.sh.\n" + ] + } + ], + "source": [ + "run_file = f'''\n", + "#!/bin/bash\n", + "\n", + "boltzgen run {str(BOLTZGEN_YAML_PATH)} \\\\\n", + " --output {str(RESULTS_DIR)} \\\\\n", + " --protocol protein-anything \\\\\n", + " --num_designs 10 \\\\\n", + " --budget 2 \\\\\n", + "'''\n", + "\n", + "with open(BOLTZGEN_RUN_PATH, \"w\") as file:\n", + " file.write(run_file)\n", + " print(f\"File written to {BOLTZGEN_RUN_PATH}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c86531cf-8261-485c-a450-e8eabb2a657d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "~/.local/bin/boltzgen\n" + ] + } + ], + "source": [ + "!which boltzgen" + ] + }, + { + "cell_type": "markdown", + "id": "6acbc118-2974-4b38-b322-3e11914bfbf4", + "metadata": {}, + "source": [ + "### Run the Boltzgen Prediction\n", + "Execute the cell below to start the prediction. This will take about 12-15 minutes. Good luck!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "82350c1e-2fcb-428b-8ae1-3413cb8356d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running file /home/hd/hd_hd/hd_aq354/protein_design/run.sh\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/home/hd/hd_hd/hd_aq354/.local/bin/boltzgen\", line 5, in \n", + " from boltzgen.cli.boltzgen import main\n", + " File \"/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/boltzgen/cli/boltzgen.py\", line 46, in \n", + " import torch\n", + " File \"/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/torch/__init__.py\", line 34, in \n", + " from typing_extensions import ParamSpec as _ParamSpec\n", + "ModuleNotFoundError: No module named 'typing_extensions'\n" + ] + }, + { + "data": { + "text/plain": [ + "256" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(f'echo \"Running file {BOLTZGEN_RUN_PATH}\"')\n", + "os.system(f\"bash {BOLTZGEN_RUN_PATH}\")\n", + "# takes about 15 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "05193bda-2071-4386-954e-99367f541c84", + "metadata": {}, + "source": [ + "### Next Steps\n", + "In your output directory you will find the directory final_ranked_designs which contains the overview pdf and csv files for interpretation.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bindcraft", + "language": "python", + "name": "bindcraft" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 216d4e9fb7ea527ed7020778d0571b9d304a4267 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Wed, 19 Nov 2025 13:44:54 +0100 Subject: [PATCH 02/28] add protein design tutorial draft --- .../tutorial/bwVisu_Bindcraft_files.png | Bin 0 -> 73025 bytes .../tutorial/bwVisu_Bindcraft_input.png | Bin 0 -> 28871 bytes .../images/tutorial/bwVisu_Boltzgen_files.png | Bin 0 -> 35555 bytes docs/images/tutorial/bwVisu_RFD_files.png | Bin 0 -> 41236 bytes .../tutorial_AF_bwVisu.md} | 26 +++++----- docs/tutorials/tutorial_Bindcraft_bwVisu.md | 48 ++++++++++++++++++ docs/tutorials/tutorial_Boltzgen_bwVisu.md | 30 +++++++++++ docs/tutorials/tutorial_RFDiffusion_bwVisu.md | 43 ++++++++++++++++ mkdocs.yml | 6 ++- 9 files changed, 138 insertions(+), 15 deletions(-) create mode 100644 docs/images/tutorial/bwVisu_Bindcraft_files.png create mode 100644 docs/images/tutorial/bwVisu_Bindcraft_input.png create mode 100644 docs/images/tutorial/bwVisu_Boltzgen_files.png create mode 100644 docs/images/tutorial/bwVisu_RFD_files.png rename docs/{tutorials.md => tutorials/tutorial_AF_bwVisu.md} (90%) create mode 100644 docs/tutorials/tutorial_Bindcraft_bwVisu.md create mode 100644 docs/tutorials/tutorial_Boltzgen_bwVisu.md create mode 100644 docs/tutorials/tutorial_RFDiffusion_bwVisu.md diff --git a/docs/images/tutorial/bwVisu_Bindcraft_files.png b/docs/images/tutorial/bwVisu_Bindcraft_files.png new file mode 100644 index 0000000000000000000000000000000000000000..a73e5979fec918d5efb85dfe17e4a3b473f58670 GIT binary patch literal 73025 zcmeFZXHZm27%d72!T?H=BnU_piGt)HAW@QJkepR=&IkiY&XObzC=w*+oO2u{=PW}8 zi4t$mIiCA|y}CbMy}EU)Zq@uyX3yTeyT9&U-&*VI(AP>b_pl#gqoAPNlarNHML|J5 zMnSnFjCB|MCF{4UCkhG;ikzgly1T(nI_6d4=aqd0Bez zrpGp&F-p18rkbfs%Sy;es^-&wy2oJS(6-w72@~LM@)(+k4d})ajLd1C5A2uKCJ{XuP3pJXA{GQsJp}*h-hx=MX^f^(hA-B?2L;A^HAmFV(Bye_T17=l zUdHZc4}^nJ5i`es(g5Rd`qkS{lkpevd|YtV%R_jr|G?>Fc!m zWbgwFA~5v!tNB|Ngw7{u$e}uq_Cwbf$;yVv!ddG7sCy9L`=b%Yz)11Gv1@pOJN)FH zd<3E>PEHJ(G3csNL<_!@LC@(hA#&f!&z%$S+9{LSadLrTeG2eHZ=xJ?8G#by(Tit3 zGyQn);q%_^s7F>uzlP)oVEPTN6Ggf;&}Tn`R)`RjC7D(%uaemH3%$8xMJ^CuPOr}vEgOGevlJR(Qri1f+00h|;-9hlz1HWtQ`SRL6Gv?C0`Zifc$dUO zQX_izsLRoq*)59pu#D}FQhBTqc%6)EaZ60O&f3`AUZ0iB!m!Vel|}!2 zZ8~Vj;nA|5C`_@bUH&+cuf}9G`Ym!OU1-vyO7sFjN5N$la(#2T&(q_IfVW9UK3u

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z+9YyFVTXb4QngdoS)vE0&pj{EAvgP(bGzbXwP^}vkG@Pye_|hXcyo%(u~?>~HSt7(j3o^{ z*XjYoX5N!GiZ9sjWb{s387a3p#Gy1!kV6%Ch%>*fxcuUUhvdR|)<}X@ceO$nD7D6K z++chL*nR)5mc6B<>gvWr0)1Mv!o+f(5X@Oo- zU*+5{CQO%4usrZCWcj61E8y_~xD5Xi=LAKU1^V&oDjhQ&te75g98>M!GZ$mcezg0$ zj`7+V3(_UK;H#Cs_5x30-Dhj~H5|IzL`GZ?1Tl`JfQVRK;$3_8Us4$d_@;3 z(7+O#3|Wi?yy?CH=pHV)MR|@;_o%#(Vumhl3Sb2;tqT1hP|6JzP;k610U7HRXw*^w zp1j-Cz9Jebz{t7l7{or{RXmKqHC3WEE_a*24rl;*k&PB!yy Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. Upload the notebooks in (link) by clicking on the upload button: -![Screenshot](images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} +![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} After the upload, you can see the notebooks in the file browser on the left. The alphafold parameters need to be uploaded as well. After registration as shown above, you will receive a download link (takes about three days). The parameter file is zipped as `af3.bin.zst`. Unpack the file to obtain `af3.bin`. This file then needs to be uploaded to a directory in your home, such as `/af3models`. -![Screenshot](images/tutorial/bwVisu_Afold_params.png){: style="height:95px;width:268px"} +![Screenshot](../images/tutorial/bwVisu_Afold_params.png){: style="height:95px;width:268px"} @@ -67,7 +67,7 @@ Decide where you want your working directory and output files to be: These directories can be created by clicking on the folder icon on the top left: -![Screenshot](images/tutorial/bwVisu_newDir.png){: style="height:111px;width:444px"} +![Screenshot](../images/tutorial/bwVisu_newDir.png){: style="height:111px;width:444px"} #### Prepare Input File @@ -82,7 +82,7 @@ Important parameters in the input file are the `name`, `sequence` and `id`, whic Next, we need to tell the AlphaFold3 program what to do with the input file, where to find the model weight parameters and where to write the output. Execute the next cell to write the run file that controls the execution. You don't need to worry about the parameters too much. They are prepared for you. Only change them if you know what you're doing. -![Screenshot](images/tutorial/bwVisu_Afold_MSA_input.png){: style="height:112px;width:268px"} +![Screenshot](../images/tutorial/bwVisu_Afold_MSA_input.png){: style="height:112px;width:268px"} #### Run MSA Prediction @@ -93,13 +93,13 @@ Run the MSA prediction by executing the next cell: This will take about 5-10 minutes, but eventually, you should see... -![Screenshot](images/tutorial/bwVisu_Afold_MSA_done.png){: style="height:53px;width:379px"} +![Screenshot](../images/tutorial/bwVisu_Afold_MSA_done.png){: style="height:53px;width:379px"} #### Verify Output In the output directory, there should be a second `.json` file in the `output/test` directory. This includes all the information from the input file and the results of the MSA. -![Screenshot](images/tutorial/bwVisu_Afold_json.png) +![Screenshot](../images/tutorial/bwVisu_Afold_json.png) {: style="height:89px;width:268px"} @@ -113,12 +113,12 @@ The second step of the AlphaFold prediction is the inference of the structure by For the inference step we need a GPU, so we need to request a GPU node on bwVisu. A list of available GPUs and their specifications is available at [https://wiki.bwhpc.de/e/Helix/Hardware#Compute_Nodes](https://wiki.bwhpc.de/e/Helix/Hardware#Compute_Nodes), or in the table below. -![Screenshot](images/tutorial/Helix_GPU.png) +![Screenshot](../images/tutorial/Helix_GPU.png) The GPU is selected by "GPU Type". The memory of each GPU Type is specified in GPU Memory per GPU (GB). For this example we select one of the A40 GPUs. -![Screenshot](images/tutorial/bwVisu_GPU.png) +![Screenshot](../images/tutorial/bwVisu_GPU.png) Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! @@ -148,7 +148,7 @@ Decide where you want your output files to be: Next, we need to tell the AlphaFold3 program what to do in the second part. Execute the next cell to write the run file that controls the execution. You don't need to worry about the parameters too much. They are prepared for you. Only change them if you know what you're doing. -![Screenshot](images/tutorial/bwVisu_Afold_GPU_input.png) +![Screenshot](../images/tutorial/bwVisu_Afold_GPU_input.png) {: style="height:159px;width:268px"} @@ -160,14 +160,14 @@ Execute the next cells to run the alignment job. Good luck! This may take a few minutes, but eventually, you should see... -![Screenshot](images/tutorial/bwVisu_Afold_GPU_done.png) +![Screenshot](../images/tutorial/bwVisu_Afold_GPU_done.png) {: style="height:55px;width:357px"} #### Verify Output You should see the AlphaFold output files: -![Screenshot](images/tutorial/bwVisu_Afold_GPU_output.png) +![Screenshot](../images/tutorial/bwVisu_Afold_GPU_output.png) {: style="height:335px;width:268px"} By default AlphaFold creates 5 samples from one seed, and sorts them in individual directories. Their ranking scores are reported in a csv table. @@ -185,7 +185,7 @@ For this last notebook, you need to install a few dependencies into your environ After installing the dependencies, you need to restart the Jupyter kernel so that Jupyter finds the newly installed packages. Click on the circular arrow in the top left of the Jupyter notebook toolbar. -![Screenshot](images/tutorial/restart_kernel.png) +![Screenshot](../images/tutorial/restart_kernel.png) {: style="width:268px"} After this, the analysis should run without any errors. Explanations of the output are provided in the notebook. diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md new file mode 100644 index 0000000..6948803 --- /dev/null +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -0,0 +1,48 @@ +# Bindcraft on bwVisu + +Welcome to the Bindcraft Tutorial for bwVisu! + +### Step 1: Get access to bwVisu + +To start, get access to bwVisu via bwForCluster Helix or SDS. For more information, visit + +[https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) + +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. + + +### Step 2: Connect to bwVisu and Start Jupyter + +Go to [https://bwvisu.bwservices.uni-heidelberg.de/](https://bwvisu.bwservices.uni-heidelberg.de/ ) and log in with your credentials and one-time password. Please note that you need to be connected to Heidelberg University's VPN if you are connecting from outside the campus. + +Choose Jupyter and start a new session. + +To use Bindcraft, we need to request a GPU core of type A40. +Choose the Kernel Path to Bindcraft: +./mnt/sds-hd/sd25g005/bindcraft/share/jupyter/ + + +![Screenshot](../images/tutorial/bwVisu_GPU.png) + + +Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. + +Upload the notebooks in (link) and the [PLD1.pdb](https://www.rcsb.org/structure/PLD1) file by clicking on the upload button: + +![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} + +After the upload, you can see the notebooks in the file browser on the left. + +![Screenshot](../images/tutorial/bwVisu_Bindcraft_input.png){: style="width:268px"} + +### Step 3: Prepare Modules and Environments +Load the GNU compiler module for fortran libraries, by clicking on the hexagon on the right and selecting compiler/gnu/11.3 +Open the notebook. Check if module list and lib dir works by executing the first cells. +If the notebook was open before, restart Kernel. + +![Screenshot](../images/tutorial/restart_kernel.png) +{: style="width:268px"} + +### Step 4: Start the Calculation + +Now execute the cells in the notebook to start your Boltzgen run! \ No newline at end of file diff --git a/docs/tutorials/tutorial_Boltzgen_bwVisu.md b/docs/tutorials/tutorial_Boltzgen_bwVisu.md new file mode 100644 index 0000000..e808b72 --- /dev/null +++ b/docs/tutorials/tutorial_Boltzgen_bwVisu.md @@ -0,0 +1,30 @@ +# Boltzgen on bwVisu + +Welcome to the Boltzgen Tutorial for bwVisu! + +### Step 1: Get access to bwVisu + +To start, get access to bwVisu via bwForCluster Helix or SDS. For more information, visit + +[https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) + +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. + +### Step 2: Start the calculation + +Request a GPU core of type A40. Choose the Kernel Path to Boltzen ./mnt/sds-hd/sd25g005/boltzgen/share/jupyter/ + +![Screenshot](../images/tutorial/bwVisu_GPU.png) + + +Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. + +Upload the notebooks in (link) and the [1g13.cif](https://www.rcsb.org/structure/1G13) file by clicking on the upload button: + +![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} + +After the upload, you can see the notebooks in the file browser on the left. + +![Screenshot](../images/tutorial/bwVisu_Boltzgen_files.png){: style="width:268px"} + +Now execute the cells in the notebook to start your Boltzgen run! diff --git a/docs/tutorials/tutorial_RFDiffusion_bwVisu.md b/docs/tutorials/tutorial_RFDiffusion_bwVisu.md new file mode 100644 index 0000000..a7a7e49 --- /dev/null +++ b/docs/tutorials/tutorial_RFDiffusion_bwVisu.md @@ -0,0 +1,43 @@ +# RFDiffusion on bwVisu + +Welcome to the RFDiffusion Tutorial for bwVisu! + +### Step 1: Get access to bwVisu + +To start, get access to bwVisu via bwForCluster Helix or SDS. For more information, visit + +[https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) + +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. + + +### Step 2: Connect to bwVisu and Start Jupyter + +Go to [https://bwvisu.bwservices.uni-heidelberg.de/](https://bwvisu.bwservices.uni-heidelberg.de/ ) and log in with your credentials and one-time password. Please note that you need to be connected to Heidelberg University's VPN if you are connecting from outside the campus. + +Choose Jupyter and start a new session. To use RFDiffusion, we need to request a GPU core of type A40 as shown below: + +![Screenshot](../images/tutorial/bwVisu_GPU.png) + + +Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. + +Upload the notebooks in (link) by clicking on the upload button: + +![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} + +After the upload, you can see the notebooks in the file browser on the left. + +Screenshot + +### Step 3: Prepare Modules and Environments +Load the RFDiffusion module by clicking on the hexagon on the right and selecting rfdiffusion. +Open the notebook. Check if module list works by executing the first cells. +If the notebook was open before, restart the kernel. + +![Screenshot](../images/tutorial/restart_kernel.png) +{: style="width:268px"} + +### Step 4: Start the Calculation + +Execute the steps in the notebook to start the calculation! \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 157d22a..c42066b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -45,8 +45,10 @@ nav: - Projects: projects.md - Resources: resources.md - Tutorials: - - tutorials.md # add Boltz and Hexlix as well - - tutorials2.md + - tutorials/tutorial_AF_bwVisu.md + - tutorials/tutorial_Bindcraft_bwVisu.md + - tutorials/tutorial_Boltzgen_bwVisu.md + - tutorials/tutorial_RFDiffusion_bwVisu.md - How to cite us: cite.md - Contact: contact.md From a0012e26961649d7a16d08c174bca96adbb247c1 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 1 Dec 2025 11:47:14 +0100 Subject: [PATCH 03/28] Update link for PLD1.pdb file in tutorial --- docs/tutorials/tutorial_Bindcraft_bwVisu.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md index 6948803..9367de8 100644 --- a/docs/tutorials/tutorial_Bindcraft_bwVisu.md +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -27,7 +27,7 @@ Choose the Kernel Path to Bindcraft: Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. -Upload the notebooks in (link) and the [PLD1.pdb](https://www.rcsb.org/structure/PLD1) file by clicking on the upload button: +Upload the notebooks in (link) and the [PLD1.pdb](https://github.com/martinpacesa/BindCraft/blob/main/example/PDL1.pdb) file by clicking on the upload button: ![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} @@ -45,4 +45,4 @@ If the notebook was open before, restart Kernel. ### Step 4: Start the Calculation -Now execute the cells in the notebook to start your Boltzgen run! \ No newline at end of file +Now execute the cells in the notebook to start your Boltzgen run! From be761b26729d6e665c9965cec2c6d98ff1b4dee6 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 1 Dec 2025 11:48:34 +0100 Subject: [PATCH 04/28] Update tutorial with GitHub link for notebooks --- docs/tutorials/tutorial_Bindcraft_bwVisu.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md index 9367de8..90a0572 100644 --- a/docs/tutorials/tutorial_Bindcraft_bwVisu.md +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -27,7 +27,7 @@ Choose the Kernel Path to Bindcraft: Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. -Upload the notebooks in (link) and the [PLD1.pdb](https://github.com/martinpacesa/BindCraft/blob/main/example/PDL1.pdb) file by clicking on the upload button: +Upload the notebooks from [our github](https://github.com/ssciwr/BioStructureHub/tree/protein_design/notebooks) and the [PLD1.pdb](https://github.com/martinpacesa/BindCraft/blob/main/example/PDL1.pdb) file by clicking on the upload button: ![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} From b6f9c6948072cc839ace2078aa91878d8bbe8234 Mon Sep 17 00:00:00 2001 From: Christine Date: Fri, 12 Dec 2025 10:20:37 +0100 Subject: [PATCH 05/28] Update module handling Added module loading and conda activation commands to run.sh --- notebooks/boltzgen.ipynb | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/notebooks/boltzgen.ipynb b/notebooks/boltzgen.ipynb index 0b19fcd..07cf411 100644 --- a/notebooks/boltzgen.ipynb +++ b/notebooks/boltzgen.ipynb @@ -142,6 +142,10 @@ "run_file = f'''\n", "#!/bin/bash\n", "\n", + "module load devel/miniforge/24.9.2\n", + "module load devel/cuda/12.8\n", + "conda activate /mnt/sds-hd/sd25g005/boltzgen\n", + "\n", "boltzgen run {str(BOLTZGEN_YAML_PATH)} \\\\\n", " --output {str(RESULTS_DIR)} \\\\\n", " --protocol protein-anything \\\\\n", From 3b76194a56baf4e559258314f73fff431ba84a08 Mon Sep 17 00:00:00 2001 From: Christine Date: Fri, 12 Dec 2025 10:22:34 +0100 Subject: [PATCH 06/28] remove check Removed a code cell that checks the location of the 'boltzgen' executable. --- notebooks/boltzgen.ipynb | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/notebooks/boltzgen.ipynb b/notebooks/boltzgen.ipynb index 07cf411..118c540 100644 --- a/notebooks/boltzgen.ipynb +++ b/notebooks/boltzgen.ipynb @@ -158,24 +158,6 @@ " print(f\"File written to {BOLTZGEN_RUN_PATH}.\")" ] }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c86531cf-8261-485c-a450-e8eabb2a657d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~/.local/bin/boltzgen\n" - ] - } - ], - "source": [ - "!which boltzgen" - ] - }, { "cell_type": "markdown", "id": "6acbc118-2974-4b38-b322-3e11914bfbf4", From b4d4bd84f08228b7825e3b5110fc9addea3cc36b Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 19 Jan 2026 13:59:47 +0100 Subject: [PATCH 07/28] re-styled website for content extension --- docs/about.md | 10 +------ docs/projects.md | 5 ++++ docs/{resources.md => resources/learning.md} | 25 +++-------------- docs/resources/methods.md | 16 +++++++++++ docs/services/collab.md | 9 +++++++ docs/{services.md => services/service.md} | 15 ++--------- docs/services/teaching.md | 8 ++++++ docs/stylesheets/extra.css | 4 +-- mkdocs.yml | 28 +++++++++++++------- 9 files changed, 64 insertions(+), 56 deletions(-) rename docs/{resources.md => resources/learning.md} (54%) create mode 100644 docs/resources/methods.md create mode 100644 docs/services/collab.md rename docs/{services.md => services/service.md} (57%) create mode 100644 docs/services/teaching.md diff --git a/docs/about.md b/docs/about.md index 119619e..708afaa 100644 --- a/docs/about.md +++ b/docs/about.md @@ -6,12 +6,4 @@ hide: # About The prediction of biomolecular structures is routed in the translation from protein or nucleic acid sequence to 3D structure. In reality, these 3D structures result from a delicate interplay with small molecules, ions, fatty acids and solvents. At the same time, the predicted structures are a product of the underlying machine-learning models. By combining the applicability range of the method with the limitations of modeling biological systems, we can provide confidence estimates in the context of the respective research question. We aim to extend our offer beyond general models like AlphaFold to more specific tools in the field of sequence based predictions to provide researchers with the ideal tools to their specific needs. -Embedding the Bio-Structure Hub in the SSC enables, building research software sustainably and in accordance with good scientific practice. This entails on the one hand making use of software engineering tools and methods such as version control, development and production environments, testing frameworks, documentation and release workflows, and a development process, to name a few; and on the other hand, acknowledging that research software is an infrastructure that is the foundation of cutting-edge research, and as such needs to be drafted, designed, operated and maintained in a purposeful manner. - -## Projects - ---8<-- -projects.md:2:3 ---8<-- - -A list of current projects is provided [here](projects.md). \ No newline at end of file +Embedding the Bio-Structure Hub in the SSC enables, building research software sustainably and in accordance with good scientific practice. This entails on the one hand making use of software engineering tools and methods such as version control, development and production environments, testing frameworks, documentation and release workflows, and a development process, to name a few; and on the other hand, acknowledging that research software is an infrastructure that is the foundation of cutting-edge research, and as such needs to be drafted, designed, operated and maintained in a purposeful manner. \ No newline at end of file diff --git a/docs/projects.md b/docs/projects.md index 863cfa9..96735a8 100644 --- a/docs/projects.md +++ b/docs/projects.md @@ -1,2 +1,7 @@ +--- +hide: + - navigation +--- + # Projects Current projects in the Bio-Structure Hub range from carefully cofolding the components of large protein complex structures, to adding molecular cofactors to improve the quality of predicted structures, or modeling interaction sites for various species. We assist in leveraging structure predictions to plan future experiments, or run preliminary simulations to be used in proposals for future projects. diff --git a/docs/resources.md b/docs/resources/learning.md similarity index 54% rename from docs/resources.md rename to docs/resources/learning.md index fa265d2..350c529 100644 --- a/docs/resources.md +++ b/docs/resources/learning.md @@ -1,31 +1,12 @@ +# Learning Center - Training Resources - -## Methods and Access - -We offer a range of resources from general structure prediction methods to specialized tools. These can be accessed on your local computer, using cloud solutions, or in high-performance computing environments. - -### General Structure Prediction Methods - -We offer support in using and interpreting the results of general models such as [AlphaFold3](https://doi.org/10.1038/s41586-024-07487-w) or [Boltz-1](https://doi.org/10.1101/2024.11.19.624167). For specialized tasks beyond the capabilities of general methods we offer custom solutions, to find the best method for your project! - - -### Computational Infrastructure - -The hub provides essential support for accessing structure prediction tools via both High Performance Computing (HPC) and Cloud Solutions: - -For HPC access, AlphaFold 2 and AlphaFold 3 are available on the [bwForCluster Helix](https://wiki.bwhpc.de/e/Helix). This includes interactive access via the [bwVisu service](https://wiki.bwhpc.de/e/Helix/bwVisu). We offer assistance and training for cluster usage. - -Regarding cloud computing, we offer support and share best practices around the existing cloud computing options, such as the [AlphaFold3 server]( https://alphafoldserver.com/ ) or the [ProteinAI service](https://protein-ai.academiccloud.de/) for Boltz-1 and AlphaFold2. - -## Learning Center - Training Resources - -### Introduction to Bioinformatics +## Introduction to Bioinformatics Are you new to the world of bioinformatics and not sure where to start? Don't worry, we've got you covered! The EMBL-EBI offers an excellent resource for beginners with their online course ["Bioinformatics for the terrified"](https://www.ebi.ac.uk/training/online/courses/bioinformatics-terrified/). This course provides broad overview of how computers are used in biology, covering the basics and beyond. It's the perfect starting point for anyone looking to dip their toes into the world of bioinformatics. A great resource for virtual lectures and webinars is [TESS by the elixir network](https://tess.elixir-europe.org/materials). These free resources offer a great start into any topic in bioinformatics. -### 3D Structure Prediction +## 3D Structure Prediction For a light introduction to protein structure prediction, an overview on the history of AlphaFold and its first appearance in the CASP competition, and more, we recommend checking out this [video by Veritasium on Youtube](https://www.youtube.com/watch?v=P_fHJIYENdI). Among other courses in the area of bioinformatics, the EMBL-EBI also offers more specialized training with their course [Alphafold2](https://www.ebi.ac.uk/training/online/courses/alphafold/). This course goes into the practical aspects of structure predictions, providing hands-on experience and expert guidance. If you prefer video formats, there is a great [talk by Simon Kohl](https://www.youtube.com/watch?v=tTN0MM2CQLU) during the heidelberg.ai series on Youtube talking about the model and concepts behind AlphaFold 2. diff --git a/docs/resources/methods.md b/docs/resources/methods.md new file mode 100644 index 0000000..eebf934 --- /dev/null +++ b/docs/resources/methods.md @@ -0,0 +1,16 @@ +# Methods and Access + +We offer a range of resources from general structure prediction methods to specialized tools. These can be accessed on your local computer, using cloud solutions, or in high-performance computing environments. + +## General Structure Prediction Methods + +We offer support in using and interpreting the results of general models such as [AlphaFold3](https://doi.org/10.1038/s41586-024-07487-w) or [Boltz-1](https://doi.org/10.1101/2024.11.19.624167). For specialized tasks beyond the capabilities of general methods we offer custom solutions, to find the best method for your project! + + +## Computational Infrastructure + +The hub provides essential support for accessing structure prediction tools via both High Performance Computing (HPC) and Cloud Solutions: + +For HPC access, AlphaFold 2 and AlphaFold 3 are available on the [bwForCluster Helix](https://wiki.bwhpc.de/e/Helix). This includes interactive access via the [bwVisu service](https://wiki.bwhpc.de/e/Helix/bwVisu). We offer assistance and training for cluster usage. + +Regarding cloud computing, we offer support and share best practices around the existing cloud computing options, such as the [AlphaFold3 server]( https://alphafoldserver.com/ ) or the [ProteinAI service](https://protein-ai.academiccloud.de/) for Boltz-1 and AlphaFold2. diff --git a/docs/services/collab.md b/docs/services/collab.md new file mode 100644 index 0000000..836364b --- /dev/null +++ b/docs/services/collab.md @@ -0,0 +1,9 @@ +# Collaboration +Recurring support requests in the same project or complex tasks that require dedicated research can lead to a longer-term collaboration. We support third-party funding applications, which can secure dedicated time for a project. + +# Training + +For larger projects we also offer a unique collaboration model by training a member of your team to work independently on the project. Our training program includes continuous support and access to resources for independent work. This includes advice on how to find and use appropriate resources such as documentation, software and pipelines. This process is tailored to the individual question at hand. + + +[Contact us!](mailto:ssc-biostructurehub@uni-heidelberg.de) \ No newline at end of file diff --git a/docs/services.md b/docs/services/service.md similarity index 57% rename from docs/services.md rename to docs/services/service.md index bf50e3a..b21ec8f 100644 --- a/docs/services.md +++ b/docs/services/service.md @@ -1,3 +1,4 @@ +# Service We provide software development and scientific support with structure predictions of biomolecules. Get in touch with us to see if we can help you! @@ -15,16 +16,4 @@ The first step is to get in touch with us - our services are free of charge. Whe The initial consultation takes about one hour. In this initial consultation, we clarify further aspects and then suggest an approach moving forward: Either providing you with resources, or investigating your question for you in a small-scale project. ## Project support -Following up on a consultation, projects can receive support. This could be a small development script, feedback to existing software, or entire prediction projects. - -## Collaboration -Recurring support requests in the same project can lead to a longer-term collaboration. We support third-party funding applications. - -## Training - -For larger projects we also offer collaborations by training a member of your team. We offer continuous support and resources for independent work. This includes advice on how to find and use appropriate resources such as documentation, software and pipelines. - - -## Teaching -Best practices and guides will be collected in tutorials and (virtual) coursework. -This includes applications and support in providing routes to access and use compute resources for bio-structure predictions. +Following up on a consultation, projects can receive support. This could be a small development script, feedback to existing software, or entire prediction projects. \ No newline at end of file diff --git a/docs/services/teaching.md b/docs/services/teaching.md new file mode 100644 index 0000000..ef956fa --- /dev/null +++ b/docs/services/teaching.md @@ -0,0 +1,8 @@ +# Teaching + +Best practices and guides will be collected in tutorials and (virtual) coursework. +This includes applications and support in providing routes to access and use compute resources for bio-structure predictions. + +In-person classes and seminars are published in our [news section](/blog). + +[Contact us!](mailto:ssc-biostructurehub@uni-heidelberg.de) \ No newline at end of file diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 656430e..1ad93d3 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -9,9 +9,9 @@ } .md-header__button.md-logo { - margin: 0; + margin: 1; padding: 1; } .md-header__button.md-logo img, .md-header__button.md-logo svg { - height: 2.3rem; + height: 3.5rem; } diff --git a/mkdocs.yml b/mkdocs.yml index 7090c0e..a02835d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -34,8 +34,7 @@ theme: - navigation.indexes # removes empty index from navigation - navigation.top - navigation.tracking - - toc.integrate - #- toc.follow + #- toc.integrate #======= Website Navigation settings ===== @@ -44,14 +43,20 @@ nav: - About: about.md - News: - blog/index.md - - Services: services.md + - What we offer: + - services/service.md + - services/collab.md + - services/teaching.md - Projects: projects.md - - Resources: resources.md + - Resources: + - resources/methods.md + - resources/learning.md - Tutorials: - - tutorials/tutorial_AF_bwVisu.md - - tutorials/tutorial_Bindcraft_bwVisu.md - - tutorials/tutorial_Boltzgen_bwVisu.md - - tutorials/tutorial_RFDiffusion_bwVisu.md + - tutorials/tutorial_AF_bwVisu.md + - tutorials/tutorial_Bindcraft_bwVisu.md + - tutorials/tutorial_Boltz_bwVisu.md + - tutorials/tutorial_Boltzgen_bwVisu.md + - tutorials/tutorial_RFDiffusion_bwVisu.md - How to cite us: cite.md - Contact: contact.md @@ -59,7 +64,9 @@ extra_css: - stylesheets/extra.css markdown_extensions: - + - admonition + - pymdownx.details + - pymdownx.superfences - attr_list - md_in_html - pymdownx.snippets: @@ -72,4 +79,5 @@ plugins: archive_toc: false authors: false blog_toc: true - - search \ No newline at end of file + - search + - callouts \ No newline at end of file From ca032fb2cab9a82a2bfb6eccf9940441e38a005c Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 19 Jan 2026 14:30:53 +0100 Subject: [PATCH 08/28] tutorial feedback &fixed pre-commit on notebooks --- .pre-commit-config.yaml | 8 +- docs/tutorials/tutorial_AF_bwVisu.md | 6 +- docs/tutorials/tutorial_RFDiffusion_bwVisu.md | 16 +- docs/tutorials_alphafold.md | 196 ------------------ notebooks/RFDiffusion.ipynb | 17 +- 5 files changed, 26 insertions(+), 217 deletions(-) delete mode 100644 docs/tutorials_alphafold.md diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2598603..ea0a727 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: - id: ruff-check # Run the formatter. - id: ruff-format - - repo: https://github.com/kynan/nbstripout - rev: 0.8.2 - hooks: - - id: nbstripout \ No newline at end of file +# - repo: https://github.com/kynan/nbstripout +# rev: 0.8.2 +# hooks: +# - id: nbstripout diff --git a/docs/tutorials/tutorial_AF_bwVisu.md b/docs/tutorials/tutorial_AF_bwVisu.md index 48e502d..f4f06d6 100644 --- a/docs/tutorials/tutorial_AF_bwVisu.md +++ b/docs/tutorials/tutorial_AF_bwVisu.md @@ -10,7 +10,7 @@ To start, get access to bwVisu via bwForCluster Helix or SDS. For more informati [https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) -For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](../contact.md) for support. ### Step 2: Obtain Model Weights from AlphaFold @@ -20,7 +20,9 @@ Each user needs to individually obtain the model weights for AlphaFold3. Downloa Note that this can take up to a few days! -**Please note that your use of AlphaFold is subject to the terms and conditions outlined in the [AlphaFold Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md). You are responsible for ensuring you comply with these terms.** +!!! danger "Legal Note" + + Please note that your use of AlphaFold is subject to the terms and conditions outlined in the [AlphaFold Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md). You are responsible for ensuring you comply with these terms. ### Step 3: Connect to bwVisu and Start Jupyter diff --git a/docs/tutorials/tutorial_RFDiffusion_bwVisu.md b/docs/tutorials/tutorial_RFDiffusion_bwVisu.md index a7a7e49..46bc92a 100644 --- a/docs/tutorials/tutorial_RFDiffusion_bwVisu.md +++ b/docs/tutorials/tutorial_RFDiffusion_bwVisu.md @@ -1,6 +1,6 @@ # RFDiffusion on bwVisu -Welcome to the RFDiffusion Tutorial for bwVisu! +Welcome to the [RFDiffusion](https://github.com/RosettaCommons/RFdiffusion) Tutorial for bwVisu! ### Step 1: Get access to bwVisu @@ -8,7 +8,7 @@ To start, get access to bwVisu via bwForCluster Helix or SDS. For more informati [https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) -For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact.md) for support. ### Step 2: Connect to bwVisu and Start Jupyter @@ -22,16 +22,16 @@ Choose Jupyter and start a new session. To use RFDiffusion, we need to request a Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. -Upload the notebooks in (link) by clicking on the upload button: +Upload the notebooks from our [github](https://github.com/ssciwr/BioStructureHub/tree/main/notebooks) by clicking on the upload button: ![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} After the upload, you can see the notebooks in the file browser on the left. -Screenshot + ### Step 3: Prepare Modules and Environments -Load the RFDiffusion module by clicking on the hexagon on the right and selecting rfdiffusion. +Load the RFDiffusion module by clicking on the hexagon on the right and selecting `rfdiffusion`. Open the notebook. Check if module list works by executing the first cells. If the notebook was open before, restart the kernel. @@ -40,4 +40,8 @@ If the notebook was open before, restart the kernel. ### Step 4: Start the Calculation -Execute the steps in the notebook to start the calculation! \ No newline at end of file +Execute the steps in the notebook to start the calculation. You will see the files in your `WORKING_DIR`: + +![Screenshot](../images/tutorial/bwVisu_RFD_files.png){: style="width:268px"} + +You can find your results in the `outputs` directory. For more information, please refer to the [RFDiffusion documentation](https://github.com/RosettaCommons/RFdiffusion) and the [original publication](https://www.nature.com/articles/s41586-023-06415-8). \ No newline at end of file diff --git a/docs/tutorials_alphafold.md b/docs/tutorials_alphafold.md deleted file mode 100644 index d29d80b..0000000 --- a/docs/tutorials_alphafold.md +++ /dev/null @@ -1,196 +0,0 @@ -# AlphaFold3 on bwVisu - -Welcome to the AlphaFold Tutorial for bwVisu! - -This tutorial will guide you through running AlphaFold on bwVisu. Please follow these steps carefully. Any feedback on the tutorial is welcome! Feel free to contact us! - -### Step 1: Get access to bwVisu - -To start, get access to bwVisu via bwForCluster Helix or SDS. For more information, visit - -[https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) - -For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. - -### Step 2: Obtain Model Weights from AlphaFold - -Each user needs to individually obtain the model weights for AlphaFold3. Download the model weights from AlphaFold using this form: - -[https://forms.gle/svvpY4u2jsHEwWYS6](https://forms.gle/svvpY4u2jsHEwWYS6 ) - -Note that this can take up to a few days! - -**Please note that your use of AlphaFold is subject to the terms and conditions outlined in the [AlphaFold Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md). You are responsible for ensuring you comply with these terms.** - -### Step 3: Connect to bwVisu and Start Jupyter - -Go to [https://bwvisu.bwservices.uni-heidelberg.de/](https://bwvisu.bwservices.uni-heidelberg.de/ ) and log in with your credentials and one-time password. Please note that you need to be connected to Heidelberg University's VPN if you are connecting from outside the campus. - -Choose Jupyter and start a new session. - -### Step 4: Prepare for AlphaFold Prediction - -The first step of the AlphaFold prediction is a multi-sequence alignment (MSA). - -For the MSA step, select 8 CPU cores with 10 GB of memory. The GPU necessary for the second step will be requested later. - -![Screenshot](../images/tutorial/bwVisu_CPU.png) - - -Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. - -Upload the notebooks from our [github](https://github.com/ssciwr/BioStructureHub/tree/main/notebooks) by clicking on the upload button: - -![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} - -After the upload, you can see the notebooks in the file browser on the left. - -The alphafold parameters need to be uploaded as well. The parameter file is zipped as `af3.bin.zst`. Unpack the file to obtain `af3.bin`. This file then needs to be uploaded to a directory in your home, such as `/af3models`. - -![Screenshot](../images/tutorial/bwVisu_Afold_params.png){: style="height:95px;width:268px"} - - - -### Step 5: Set Up Your MSA Within the Notebook - -Open `Afold_Alignment_CPU.ipynb`. - -#### Set Environment Variables - -Add the directory with the alphafold parameters to `ALPHAFOLD_MODEL_DIR`: - - ALPHAFOLD_MODEL_DIR = Path.home() / "af3models" - - -Decide where you want your working directory and output files to be: - - ALPHAFOLD_WORKING_DIR = Path.home() / "afold_test" - ALPHAFOLD_RESULTS_DIR_PART1 = ALPHAFOLD_WORKING_DIR / "output" - -These directories can be created by clicking on the folder icon on the top left: - -![Screenshot](../images/tutorial/bwVisu_newDir.png){: style="height:111px;width:444px"} - - -#### Prepare Input File - -First we prepare the `.json` input file that will tell AlphaFold what to predict. An introduction to the json file format can be found [here](https://stackoverflow.blog/2022/06/02/a-beginners-guide-to-json-the-data-format-for-the-internet/#h2-81f8002b67730). - -More information and examples on how these files are structured can be found in the [AlphaFold3 github](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md#top-level-structure), with example input files [here](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md#full-example). - -Important parameters in the input file are the `name`, `sequence` and `id`, which labels the chain ID. Upon executing this cell, the input file will be written to your working directory. Remember the `name` as it is needed later for the [diffusion run](#step-7-set-up-your-diffusion-run-within-the-notebook). - -#### Write Run File - -Next, we need to tell the AlphaFold3 program what to do with the input file, where to find the model weight parameters and where to write the output. Execute the next cell to write the run file that controls the execution. You don't need to worry about the parameters too much. They are prepared for you. Only change them if you know what you're doing. - -![Screenshot](../images/tutorial/bwVisu_Afold_MSA_input.png){: style="height:112px;width:268px"} - -#### Run MSA Prediction - -Run the MSA prediction by executing the next cell: - - os.system(f'bash {ALPHAFOLD_RUN_PATH}') - - -This will take about 5-10 minutes, but eventually, you should see... - -![Screenshot](../images/tutorial/bwVisu_Afold_MSA_done.png){: style="height:53px;width:379px"} - -#### Verify Output - -In the output directory, there should be a second `.json` file in the `output/test` directory. This includes all the information from the input file and the results of the MSA. - -![Screenshot](../images/tutorial/bwVisu_Afold_json.png) -{: style="height:89px;width:268px"} - - -You can now close this interactive session session on bwVisu, as the CPU is no longer needed, and move to the second step. - - - -### Step 6: Prepare the Inference - -The second step of the AlphaFold prediction is the inference of the structure by the model. - -For the inference step we need a GPU, so we need to request a GPU node on bwVisu. A list of available GPUs and their specifications is available at [https://wiki.bwhpc.de/e/Helix/Hardware#Compute_Nodes](https://wiki.bwhpc.de/e/Helix/Hardware#Compute_Nodes), or in the table below. - -![Screenshot](../images/tutorial/Helix_GPU.png) - - -The GPU is selected by "GPU Type". The memory of each GPU Type is specified in GPU Memory per GPU (GB). For this example we select one of the A40 GPUs. - -![Screenshot](../images/tutorial/bwVisu_GPU.png) - - -Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! - -### Step 7: Set Up Your Diffusion Run Within the Notebook - - Open `AFold_Diffusion_GPU.ipynb`. - -#### Set Environment Variables - -Add the directory with the alphafold parameters to ALPHAFOLD_MODEL_DIR: - - ALPHAFOLD_MODEL_DIR = Path.home() / "af3models" - - -Link the output of the MSA prediction, and the project name given in the MSA input file - - ALPHAFOLD_WORKING_DIR = Path.home() / "afold_test" - ALPHAFOLD_RESULTS_DIR_PART1 = ALPHAFOLD_WORKING_DIR / "output" - - -Decide where you want your output files to be: - - ALPHAFOLD_RESULTS_DIR_PART2 = ALPHAFOLD_WORKING_DIR / "output_gpu" - -#### Write Run File - -Next, we need to tell the AlphaFold3 program what to do in the second part. Execute the next cell to write the run file that controls the execution. You don't need to worry about the parameters too much. They are prepared for you. Only change them if you know what you're doing. - -![Screenshot](../images/tutorial/bwVisu_Afold_GPU_input.png) -{: style="height:159px;width:268px"} - - -#### Run the structure prediction - -Execute the next cells to run the alignment job. Good luck! - - os.system(f'bash {ALPHAFOLD_RUN_PATH}') - -This may take a few minutes, but eventually, you should see... - -![Screenshot](../images/tutorial/bwVisu_Afold_GPU_done.png) -{: style="height:55px;width:357px"} - -#### Verify Output - -You should see the AlphaFold output files: - -![Screenshot](../images/tutorial/bwVisu_Afold_GPU_output.png) -{: style="height:335px;width:268px"} - -By default AlphaFold creates 5 samples from one seed, and sorts them in individual directories. Their ranking scores are reported in a csv table. -The best model is presented in the output directory as well, with its structure file and confidence descriptions. The latter are needed to judge the quality of the prediction. - -**Please note that you must ensure your use and distribution of the AlphaFold outputs comply with the [Output Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/OUTPUT_TERMS_OF_USE.md).** - -### Step 8: Analyze your results - -Open the last notebook `Afold_Confidence_Levels.ipynb` to get a summary of the models confidence levels. This notebook reads the confidence descriptions and renders its central information. - -For this last notebook, you need to install a few dependencies into your environment. These dependencies are libraries that are used to analyze and visualize the output. The dependencies are installed in the Jupyter notebook in the first code cell: - - %pip install biopython seaborn - -After installing the dependencies, you need to restart the Jupyter kernel so that Jupyter finds the newly installed packages. Click on the circular arrow in the top left of the Jupyter notebook toolbar. - -![Screenshot](../images/tutorial/restart_kernel.png) -{: style="width:268px"} - -After this, the analysis should run without any errors. Explanations of the output are provided in the notebook. - -To visualize your predicted structures, download them to your computer and open the files with programs such as [Pymol](https://pymol.org/) or [ChimeraX](https://www.cgl.ucsf.edu/chimerax/). To visualize the pIDDT in "classic" AlphaFold colors, use [this](https://kpwulab.com/2023/03/09/color-alphafold2s-plddt/) quick tutorial. This allows to visualize more and less confident areas of the predicted structure. - diff --git a/notebooks/RFDiffusion.ipynb b/notebooks/RFDiffusion.ipynb index 4c062cc..24f8ea5 100644 --- a/notebooks/RFDiffusion.ipynb +++ b/notebooks/RFDiffusion.ipynb @@ -19,7 +19,7 @@ "### Optional Information\n", "The RFDiffusion program is installed on the bwForCluster Helix and this notebook is just using their installation. If you want more details on available options, load the module by clicking on the hexagon icon on the far left of the bwVisu interface and load the bio/rfdiffusion/1.1.0 module by clicking on the Load button next to the entry. \n", "\n", - "Execute the next cell to verify that the module is loaded. If bio/rfdiffusion/1.1.0 is not in the list, click on the bindcraft kernel on the top right of the window to reload the kernel. Select bindcraft and try again!" + "Execute the next cell to verify that the module is loaded. If bio/rfdiffusion/1.1.0 is not in the list, click on the python kernel on the top right of the window to reload the kernel. Select a kernel and try again!" ] }, { @@ -87,7 +87,7 @@ "# additional info in the RFDiffusion module\n", "! module help bio/rfdiffusion/1.1.0\n", "\n", - "#Material used in the documentation can be found in $RFDIFFUSION_HOME:\n", + "# Material used in the documentation can be found in $RFDIFFUSION_HOME:\n", "# Scripts in $RFDIFFUSION_HOME/scripts\n", "# Examples in $RFDIFFUSION_HOME/examples\n", "# Model Weights in $RFDIFFUSION_HOME/models" @@ -110,8 +110,7 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "WORKING_DIR = Path.home() /\"protein_design_RFDiffusion\"" + "WORKING_DIR = Path.home() / \"protein_design_RFDiffusion\"" ] }, { @@ -129,7 +128,7 @@ "metadata": {}, "outputs": [], "source": [ - "run_file = f'''\n", + "run_file = \"\"\"\n", "#!/bin/bash\n", "\n", "# Load software module \n", @@ -148,11 +147,11 @@ " inference.schedule_directory_path=./schedules \\\\\n", " inference.num_designs=3 \\\\\n", " 'contigmap.contigs=[10-40/A163-181/10-40]'\n", - "'''\n", + "\"\"\"\n", "\n", - "RUN_PATH = WORKING_DIR / \"run.sh\" # file name!\n", + "RUN_PATH = WORKING_DIR / \"run.sh\" # file name!\n", "\n", - "with open(RUN_PATH, \"w\") as file: \n", + "with open(RUN_PATH, \"w\") as file:\n", " file.write(run_file)" ] }, @@ -588,7 +587,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.2" } }, "nbformat": 4, From 7d5349ca7f08e0afa40de0dd4bc90a2bbf0b7dd1 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Tue, 20 Jan 2026 09:53:33 +0100 Subject: [PATCH 09/28] add Boltz tutorial --- docs/images/tutorial/bwVisu_Boltz_MSA.png | Bin 0 -> 14549 bytes docs/images/tutorial/bwVisu_Boltz_done.png | Bin 0 -> 25380 bytes docs/images/tutorial/bwVisu_Boltz_output.png | Bin 0 -> 37515 bytes docs/tutorials/tutorial_Boltz_bwVisu.md | 92 ++++ notebooks/boltz_confidence_levels.ipynb | 549 +++++++++++++++++++ notebooks/boltz_input.ipynb | 231 ++++++++ 6 files changed, 872 insertions(+) create mode 100644 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z`Ivag;lv)sS-z!YOh*p=?B-sIg57uic)eJE?4!|xRsJ-V&_*&cc!S%uzOy7h0j?PX zx$SlNg_N{J_tCM% zR1Cpc`0NZl;;qat+pmPd{#KoLP7j3oY}Ll6X_FmCqYZ^S!Zj$KTr4rlTIRZ9#OKIV z!^~S-kZ|bdHdd?fWB+gFDd(jwSF&Psr9HVM%!TzAWo(!;i$$KELq&J+PUBi`QSg`zmpAytsl` z>Xmn~1U!-=P7VwVurXMt*WBc=Sk}U<_n{ZD2ZvaZhQbq@8P(>?jKKkEp zxskJ?vspe-{=Hct^7Fz%7_0kAkAYF{Z*tmWqDe%Z(th~QW_1gsC`GXqJE-e||9&eQ zuvHiRGPb|@UMQ_~B-p?6Z*gCA{@xi~p;O?O576Eq{};$sz*F$$eYqod{{5B + +The GPU is selected byw "GPU Type". The memory of each GPU Type is specified in GPU Memory per GPU (GB). For this example we select one of the A40 GPUs. + +![Screenshot](../images/tutorial/bwVisu_GPU.png) + + +Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! + +### Step 5: Set Up Your Diffusion Run Within the Notebook + + Open `Boltz_input.ipynb`. + +#### Set Environment Variables + + +Link the output of the MSA prediction, and the project name given in the MSA input file + + BOLTZ_WORKING_DIR = "boltz_test/" + + + +#### Write Input File + +First we prepare the `.yaml` input file that will be tell Boltz what to predict. + +More information and examples on how these files are structured can be found in the [Boltz github](https://github.com/jwohlwend/boltz/blob/main/docs/prediction.md#yaml-format). + +Important parameters in the input file are the `name`, `sequence` and `id`, as well as the `msa` path that needs to point to the precalculated MSA. Upon executing this cell, the input file will be written to your working directory. + +Remember the name of your input file as it is needed for [the analysis](#step-6-analyze-your-results). + +#### Run Structure Prediction + +Run the structure prediction by executing the next cell: + + ! CUDA_VISIBLE_DEVICES=0 boltz predict {BOLTZ_WORKING_DIR}/input.yaml --write_full_pae --out_dir {BOLTZ_WORKING_DIR} + + +This may take a few minutes, but eventually, you should see... + +![Screenshot](../images/tutorial/bwVisu_Boltz_done.png) + +#### Verify Output + +In the output directory, there should be multiple files. The .cif file includes the structure, the other files are used to determine the quality of the prediction. + +![Screenshot](../images/tutorial/bwVisu_Boltz_output.png) +{: style="width:268px"} + + +### Step 6: Analyze your results + +Open the second notebook called `Boltz_Confidence_Levels.ipynb` to get a summary of the models confidence levels. This notebook reads the confidence descriptions and renders its central information. + +To find the files, you need the name of the input file of the Boltz run. In this example we used `input.yaml`, so the directory structure `input` are automatically created. + +To visualize your predicted structures, download them to your computer and open the files with programs such as [Pymol](https://pymol.org/) or [ChimeraX](https://www.cgl.ucsf.edu/chimerax/). To visualize the pIDDT in "classic" AlphaFold colors, use [this](https://kpwulab.com/2023/03/09/color-alphafold2s-plddt/) quick tutorial. This allows to visualize more and less confident areas of the predicted structure. \ No newline at end of file diff --git a/notebooks/boltz_confidence_levels.ipynb b/notebooks/boltz_confidence_levels.ipynb new file mode 100644 index 0000000..fb92dbf --- /dev/null +++ b/notebooks/boltz_confidence_levels.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f8b82069-92f6-4164-820d-3faa067047ca", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "id": "2e1f3837-fe7a-44b2-ab43-b6e4b074972b", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## File Input" + ] + }, + { + "cell_type": "markdown", + "id": "41a35989-dd0a-4f08-8888-9f1f85a14d20", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Remember the files from the last notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "583aad86-e2b3-473b-a1bf-8f9e5b809a91", + "metadata": {}, + "outputs": [], + "source": [ + "project_name = \"input_file\"\n", + "BOLTZ_WORKING_DIR = Path.home() / \"boltz_test\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9ec8372f-5fe3-48cb-9cec-ba9ec14fd230", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the base directory\n", + "BOLTZ_RESULTS = BOLTZ_WORKING_DIR / f\"boltz_results_{project_name}\"\n", + "BOLTZ_PREDICTIONS = BOLTZ_RESULTS / \"predictions\" / project_name\n", + "\n", + "# Construct file paths\n", + "summary_file = BOLTZ_PREDICTIONS / f\"confidence_{project_name}_model_0.json\"\n", + "plddt_file = BOLTZ_PREDICTIONS / f\"plddt_{project_name}_model_0.npz\"\n", + "pae_file = BOLTZ_PREDICTIONS / f\"pae_{project_name}_model_0.npz\"\n", + "\n", + "# Open files\n", + "with open(summary_file) as file:\n", + " confidence = json.load(file)\n", + "\n", + "plddt_file = np.load(plddt_file)\n", + "pae_file = np.load(pae_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ccbe7515-8ab7-4640-9bc9-350ef12cd394", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# translate chain indices\n", + "chains = [chr(item) for item in range(ord(\"A\"), ord(\"Z\") + 1)][\n", + " : len(confidence[\"chains_ptm\"])\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "fcedeefa-0b33-4c74-bb4d-917afe8e371a", + "metadata": {}, + "source": [ + "## Residue Level Confidence" + ] + }, + { + "cell_type": "markdown", + "id": "3a459737-ef1b-4a92-81a4-8f112c76aa67", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The Predicted Local Distance Difference Test (pLDDT) is a per residue value typically scaled between 0 and 1 or 0 and 100. A pLDDT above 90 is considered near atomic level precision of the prediction. Between 70 and 90 is considered a high confidence in the prediction. Below 70 a low confidence is observed and below 50 a very low confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c78f19ff-c680-4c0c-a228-b848f487f246", + "metadata": { + "hide_input": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y = plddt_file[\"plddt\"] * 100\n", + "x = np.arange(1, len(y) + 1)\n", + "\n", + "regions = [\n", + " {\"label\": \"Very High\", \"color\": \"#106dff\", \"bottom\": 90, \"top\": 100},\n", + " {\"label\": \"Confident\", \"color\": \"#f6ed12\", \"bottom\": 70, \"top\": 90},\n", + " {\"label\": \"Low\", \"color\": \"#ef821e\", \"bottom\": 50, \"top\": 70},\n", + "]\n", + "\n", + "# Add colored horizontal lines for each confidence interval\n", + "for region in regions:\n", + " plt.axhline(region[\"bottom\"], color=region[\"color\"])\n", + "\n", + "plt.plot(x, y, color=\"black\")\n", + "\n", + "plt.ylim(0, 100)\n", + "plt.xlabel(\"Positions\")\n", + "plt.ylabel(\"Predicted IDDT\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9a0af3df-bda7-4ee7-a68a-09a2ecb71ae4", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The **predicted aligned error (PAE)** is an estimate of the error in the relative postion and orientation between two residues, molecules or ions in the predicted structure. Higher values indicate lower confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "099ceb10-55fa-4d75-af52-8107f7b4d6d6", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_theme(rc={\"figure.figsize\": (6, 5)})\n", + "\n", + "ax = sns.heatmap(\n", + " pae_file[\"pae\"],\n", + " xticklabels=100,\n", + " yticklabels=100,\n", + " cmap=sns.light_palette(\"seagreen\", reverse=True, as_cmap=True),\n", + " vmin=0,\n", + " vmax=30,\n", + ")\n", + "ax.tick_params(left=False, bottom=False)\n", + "ax.set_title(\"Expected Position Error (Ångströms)\")\n", + "\n", + "plt.yticks(rotation=0)\n", + "plt.xlabel(\"Scored Residue\")\n", + "plt.ylabel(\"Aligned Residue\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e18ea10c-410a-49cc-a325-a0f63b58ac82", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Global Confidence Levels\n" + ] + }, + { + "cell_type": "markdown", + "id": "88a92613-8b1b-4b8f-abc5-7a3354c7ff53", + "metadata": {}, + "source": [ + "The predicted template modeling (pTM) score measures the accuracy of the entire structure. It ranges from 0-1. A pTM score above 0.5 means the overal predicted ford for the complex might be similar to the true structure.\n", + "For more information see https://doi.org/10.1093/BIOINFORMATICS/BTQ066.\n", + "\n", + "Note that TM score is strict for small structures or short chains (fewer than 20 tolkens). For these cases PAE and pLDDT may be more indicative of prediction quality." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "df085637-e6f7-47a7-a135-8cc62785a5d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.84" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(confidence[\"ptm\"], 2)" + ] + }, + { + "cell_type": "markdown", + "id": "3ee8090e-f2b2-4960-a14d-1d73f35b823d", + "metadata": {}, + "source": [ + "The interface predicted template modeling (ipTM) score measures accuracy of the predicted relative positions of the subunits within a complex. It ranges from 0 to 1. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggest likely a failed prediction.\n", + "\n", + "Note that this is 0.0 if there is only one chain in the input file." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6eb16382-14e8-4aa7-9c05-46dea8672e85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(confidence[\"iptm\"], 2)" + ] + }, + { + "cell_type": "markdown", + "id": "54e5248e-944d-49a1-97e8-470b410c7e0c", + "metadata": {}, + "source": [ + "## Aggregated Confidence Levels" + ] + }, + { + "cell_type": "markdown", + "id": "ec3a0143-e868-4026-9095-c1fe72707c45", + "metadata": {}, + "source": [ + "The confidence levels can be aggregated over different components of the final model. The **ligand ipTM** and **protein ipTM** allow to distinguish between the accuracy of ligands and protein components, respectively.\n", + "\n", + "Note that these are 0.0 if there is only one chain in the input file." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cf571c07-a3b1-497e-bd47-9c9e85642bf8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(confidence[\"ligand_iptm\"], 2), round(confidence[\"protein_iptm\"], 2)" + ] + }, + { + "cell_type": "markdown", + "id": "d805c090-bb82-4b22-b28d-cebc6d2ce7cd", + "metadata": {}, + "source": [ + "## Chain Confidence Levels" + ] + }, + { + "cell_type": "markdown", + "id": "f54c14f0-0800-4a56-a240-df70d0621b01", + "metadata": {}, + "source": [ + "Chain confidence levels allow a breakdown of the overall confidence levels onto individual chains. The **chain pTM** contains the pTM restricted to the respective chain. This can be used for ranking individual chains, independent of their interactions. The **chain ipTM** gives the average confidence per chain in the interface between each chain and all other chains. It can be used to ranking specific chains with a focus on their interaction with the rest of the complex. This is often the case for ligands. \n", + "\n", + "Note that the chain ipTM is not available for ions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "93e62c0d-8bf8-400a-940c-75f6f8f26687", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Chain pTM Score
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" + ], + "text/plain": [ + " Chain pTM Score\n", + "A 0.843053" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_confidence = pd.DataFrame.from_dict(\n", + " data=confidence[\"chains_ptm\"], columns=[\"Chain pTM Score\"], orient=\"index\"\n", + ")\n", + "chain_confidence.index = chains\n", + "chain_confidence" + ] + }, + { + "cell_type": "markdown", + "id": "bd8331e5-d424-45c9-a7a0-02954f44f070", + "metadata": {}, + "source": [ + "## Chain Pair Confidence Levels\n", + "Chain pair confidence levels show values with respect to all other individual chains in matrix format." + ] + }, + { + "cell_type": "markdown", + "id": "4c2a56e1-ccaa-4d8c-9947-61ac562ed4a8", + "metadata": {}, + "source": [ + "The diagonal elements (i, i) contain the **pTM** restricted to chain i. Off-diagonal elements (i, j) of the array contain the **ipTM** restricted to tokens from chains i and j. This inforamtion can be used for ranking a specific interface between two chains, when you know that they interact, e.g. for antibody-antigen interactions.\n", + "\n", + "Note that the chain restrictred pTM and ipTM are not available for ions." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5bd84564-8b6d-48de-8b3e-f159f808826b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "chain_pair_iptm = pd.DataFrame(confidence[\"pair_chains_iptm\"])\n", + "chain_pair_iptm.index = chains\n", + "chain_pair_iptm.columns = chains\n", + "\n", + "ax = sns.heatmap(\n", + " chain_pair_iptm,\n", + " annot=True,\n", + " fmt=\".1f\",\n", + " cmap=sns.light_palette(\"seagreen\", as_cmap=True),\n", + " vmin=0.6,\n", + " vmax=0.8,\n", + " cbar=False,\n", + ")\n", + "ax.tick_params(right=True, top=True, labelright=True, labeltop=True, rotation=0)\n", + "ax.set_title(\"ipTM Score between Chain Pairs\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0a3029e-78a7-47b9-a10a-e2c71d680187", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/boltz_input.ipynb b/notebooks/boltz_input.ipynb new file mode 100644 index 0000000..12c7189 --- /dev/null +++ b/notebooks/boltz_input.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6f88c637-624c-4371-8800-8f012f901d3b", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "24a67e8f-4b57-4281-89cf-a1a7dab5ea51", + "metadata": {}, + "source": [ + "### Preparation\n", + "\n", + "The Boltz calculation requires two parts, a **multi-sequence alignment (MSA)** step and an inference step. Boltz relies on external programs and servers to run the MSA. Here we cannot use an MSA server, but have to provide a precomputed MSA file in a3m or csv format. For each protein entity in the simulation the MSA needs to be computed individually beforehand!" + ] + }, + { + "cell_type": "markdown", + "id": "51530ef6-e727-4f80-bed3-08971971d625", + "metadata": {}, + "source": [ + "Next we need to define where AlphaFold finds our input data and where the output files are written to. You can see these files in the file browser on the left. If you change these names, remember to change them in the analysis notebook as well." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8e05e70-7094-4f5e-a047-0a824e076f1c", + "metadata": {}, + "outputs": [], + "source": [ + "BOLTZ_WORKING_DIR = Path.home() / \"boltz_test\"" + ] + }, + { + "cell_type": "markdown", + "id": "518fac88-a81e-47c8-a2f0-d5d46f0c878f", + "metadata": {}, + "source": [ + "Now we need to prepare the input file. Remember to link the MSA files to the respective sequence entries! \n", + "\n", + "For this example you can download the example.a3m file from our [GitHub](https://github.com/ssciwr/BioStructureHub)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0fb34387-6449-4dbf-a247-b56f554e5cac", + "metadata": {}, + "outputs": [], + "source": [ + "MSA_PATH = BOLTZ_WORKING_DIR / \"example.a3m\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ea6ddfa-8f2c-4ab3-96ed-61a5c68042dd", + "metadata": {}, + "outputs": [], + "source": [ + "INPUT_FILE = BOLTZ_WORKING_DIR / \"input_file.yaml\"\n", + "test_file = f\"\"\"\n", + "version: 1\n", + "sequences:\n", + " - protein:\n", + " id: [A] \n", + " sequence: GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + " msa: {MSA_PATH}\n", + "\"\"\"\n", + "\n", + "with open(INPUT_FILE, \"w\") as text_file:\n", + " text_file.write(test_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ce7d88ee-4b2c-46a0-8a2c-7d8f987f61d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to /home/hd/hd_hd/hd_aq354/boltz_test/run.sh.\n" + ] + } + ], + "source": [ + "BOLTZ_RUN_FILE = \"run.sh\"\n", + "BOLTZ_RUN_PATH = BOLTZ_WORKING_DIR / BOLTZ_RUN_FILE # will be created in this notebook\n", + "\n", + "run_file = f\"\"\"\n", + "#!/bin/bash\n", + "\n", + "module load devel/miniforge/24.9.2\n", + "module load devel/cuda/12.8\n", + "conda activate /mnt/sds-hd/sd25g005/boltz\n", + "\n", + "\n", + "boltz predict {str(INPUT_FILE)} \\\\\n", + " --write_full_pae \\\\\n", + " --out_dir {BOLTZ_WORKING_DIR}\n", + "\"\"\"\n", + "\n", + "with open(BOLTZ_RUN_PATH, \"w\") as file:\n", + " file.write(run_file)\n", + " print(f\"File written to {BOLTZ_RUN_PATH}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "a857a7bb-3ae8-4c1e-9d65-bda1a10006e8", + "metadata": {}, + "source": [ + "### Run the Boltz prediction\n", + "\n", + "Execute the next cell to star the run! Good luck!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fb6537e4-9cd2-4526-aada-77c189eff506", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running file /home/hd/hd_hd/hd_aq354/boltz_test/run.sh\n", + "Checking input data.\n", + "Processing 1 inputs with 1 threads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 2.13it/s]\n", + "Using bfloat16 Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running structure prediction for 1 input.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.0.post0, which is newer than your current Lightning version: v2.5.0\n", + "You are using a CUDA device ('NVIDIA A40') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "SLURM auto-requeueing enabled. Setting signal handlers.\n", + "/mnt/sds-hd/sd25g005/boltz/lib/python3.12/site-packages/torch/utils/data/dataloader.py:627: UserWarning: This DataLoader will create 2 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|██████████| 1/1 [00:13<00:00, 0.08it/s]Number of failed examples: 0\n", + "Predicting DataLoader 0: 100%|██████████| 1/1 [00:13<00:00, 0.08it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(f'echo \"Running file {BOLTZ_RUN_PATH}\"')\n", + "os.system(f\"bash {BOLTZ_RUN_PATH}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7da8da64-ca6a-4211-8f8d-146bacfd0cb0", + "metadata": {}, + "source": [ + "Done! \n", + "\n", + "Number of failed examples should be 0. Also check if there is a warning \"MSA does not match input sequence\" that indicates that something went wrong with the precomputed msa." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 83a45a14ae76ebf6f596493c3f419977723fddab Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Thu, 22 Jan 2026 13:26:29 +0100 Subject: [PATCH 10/28] update bindcraft tutorial --- .../bwVisu_Bindcraft_modules_list.png | Bin 0 -> 28296 bytes .../bwVisu_Bindcraft_modules_loaded.png | Bin 0 -> 57673 bytes .../tutorial/bwVisu_Bindcraft_output.png | Bin 0 -> 76373 bytes docs/images/tutorial/restart_kernel.png | Bin 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x = np.array([int(x) for x in pdb_info[\"_ma_qa_metric_local.ordinal_id\"]])\n", "y = np.array([float(x) for x in pdb_info[\"_ma_qa_metric_local.metric_value\"]])\n", @@ -211,7 +225,6 @@ " {\"label\": \"Low\", \"color\": \"#ef821e\", \"bottom\": 50, \"top\": 70},\n", "]\n", "\n", - "plt.figure(figsize=(6, 5)) # avoid failing tests due to unusual figure size\n", "\n", "# Add colored horizontal lines for each confidence interval\n", "for region in regions:\n", @@ -227,7 +240,7 @@ }, { "cell_type": "markdown", - "id": "16", + "id": "82d443e8-bf65-4f01-86cc-e3c25324c5fc", "metadata": {}, "source": [ "The **predicted aligned error (PAE)** is an estimate of the error in the relative postion and orientation between two residues, molecules or ions in the predicted structure. Higher values indicate lower confidence.\n" @@ -235,14 +248,21 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "17", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 8, + "id": "387b82aa-9d57-45cc-b933-344ffd154bd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Chain pTM ScoreChain ipTM Score
A0.680.63
B0.680.63
\n", + "
" + ], + "text/plain": [ + " Chain pTM Score Chain ipTM Score\n", + "A 0.68 0.63\n", + "B 0.68 0.63" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "chain_confidence = pd.DataFrame(\n", " index=chains,\n", @@ -303,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "21", + "id": "bd8331e5-d424-45c9-a7a0-02954f44f070", "metadata": {}, "source": [ "## Chain Pair Confidence Levels\n", @@ -312,7 +379,7 @@ }, { "cell_type": "markdown", - "id": "22", + "id": "4c2a56e1-ccaa-4d8c-9947-61ac562ed4a8", "metadata": {}, "source": [ "The diagonal elements (i, i) contain the **pTM** restricted to chain i. Off-diagonal elements (i, j) of the array contain the **ipTM** restricted to tokens from chains i and j. This inforamtion can be used for ranking a specific interface between two chains, when you know that they interact, e.g. for antibody-antigen interactions.\n", @@ -322,14 +389,21 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "23", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 10, + "id": "5bd84564-8b6d-48de-8b3e-f159f808826b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "chain_pair_iptm = pd.DataFrame(\n", " confidence[\"chain_pair_iptm\"], index=chains, columns=chains\n", @@ -365,22 +439,29 @@ }, { "cell_type": "markdown", - "id": "24", + "id": "dceb71f7-1e31-471f-9764-e9d1cddcf0b7", "metadata": {}, "source": [ - "This plot show the lowest predicted aligned error (PAE) value across rows restricted to chain i and columns restricted to chain j. This has been found to correlate with whether two chains interact or not, and in some cases can be used to distinguish binders from non-binders. " + "This plot show the lowest predicted aligne error (PAE) value across rows restricted to chain i and columns restricted to chain j. This has been found to correlate with whether two chains interact or not, and in some cases can be used to distinguish binders from non-binders. " ] }, { "cell_type": "code", - "execution_count": null, - "id": "25", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 11, + "id": "9dab565c-ef99-4da4-8b1e-afdc3d2976d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "chain_pair_pae = pd.DataFrame(\n", " confidence[\"chain_pair_pae_min\"], index=chains, columns=chains\n", @@ -397,11 +478,19 @@ "ax.set_title(\"Lowest PAE between Chain Pairs\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c779bf88-5137-44b8-957d-2b961031e7f7", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "bsh", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -415,7 +504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/AFold_Diffusion_GPU.ipynb b/notebooks/AFold_Diffusion_GPU.ipynb index 73ceb00..db6f33c 100644 --- a/notebooks/AFold_Diffusion_GPU.ipynb +++ b/notebooks/AFold_Diffusion_GPU.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "0", + "id": "60cad586-e162-4af7-a505-dd655b805c0d", "metadata": {}, "source": [ "## Preparation\n", @@ -12,8 +12,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "1", + "execution_count": 1, + "id": "5f82f3d1-fe5c-4b0d-9564-0707b8c826a3", "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "markdown", - "id": "2", + "id": "c6f03787-154f-4081-be58-ecf10cf554ba", "metadata": {}, "source": [ "## Input File\n", @@ -60,8 +60,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "3", + "execution_count": 2, + "id": "dc151613-5b08-40d7-ad3d-08f7a665ad2f", "metadata": {}, "outputs": [], "source": [ @@ -88,18 +88,79 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "4", + "execution_count": 3, + "id": "39acd06d-8fdf-4477-a23e-35fbb2df07af", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ - "nbval-ignore-output" + "nbval-skip" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running file /home/hd/hd_hd/hd_aq354/afold_test/run_gpu.sh\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0122 15:43:22.889134 22801316439872 xla_bridge.py:895] Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "I0122 15:43:22.896112 22801316439872 xla_bridge.py:895] Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n", + "I0122 15:43:31.008952 22801316439872 pipeline.py:164] processing test, random_seed=1\n", + "I0122 15:43:31.043105 22801316439872 pipeline.py:257] Calculating bucket size for input with 596 tokens.\n", + "I0122 15:43:31.043189 22801316439872 pipeline.py:263] Got bucket size 768 for input with 596 tokens, resulting in 172 padded tokens.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running AlphaFold 3. Please note that standard AlphaFold 3 model parameters are\n", + "only available under terms of use provided at\n", + "https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md.\n", + "If you do not agree to these terms and are using AlphaFold 3 derived model\n", + "parameters, cancel execution of AlphaFold 3 inference with CTRL-C, and do not\n", + "use the model parameters.\n", + "Skipping running the data pipeline.\n", + "Found local devices: [CudaDevice(id=0)], using device 0: cuda:0\n", + "Building model from scratch...\n", + "Processing fold inputs.\n", + "Processing fold input #1\n", + "Processing fold input test\n", + "Checking we can load the model parameters...\n", + "Skipping data pipeline...\n", + "Output directory: /home/hd/hd_hd/hd_aq354/afold_test/output_gpu/test\n", + "Writing model input JSON to /home/hd/hd_hd/hd_aq354/afold_test/output_gpu/test\n", + "Predicting 3D structure for test for seed(s) (1,)...\n", + "Featurising data for seeds (1,)...\n", + "Featurising test with rng_seed 1.\n", + "Featurising test with rng_seed 1 took 7.17 seconds.\n", + "Featurising data for seeds (1,) took 10.44 seconds.\n", + "Running model inference for seed 1...\n", + "Running model inference for seed 1 took 118.45 seconds.\n", + "Extracting output structures (one per sample) for seed 1...\n", + "Extracting output structures (one per sample) for seed 1 took 0.43 seconds.\n", + "Running model inference and extracting output structures for seed 1 took 118.88 seconds.\n", + "Running model inference and extracting output structures for seeds (1,) took 118.88 seconds.\n", + "Writing outputs for test for seed(s) (1,)...\n", + "Done processing fold input test.\n", + "Done processing 1 fold inputs.\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "os.system(f'echo \"Running file {ALPHAFOLD_RUN_PATH}\"')\n", "os.system(f\"bash {ALPHAFOLD_RUN_PATH}\")" @@ -107,7 +168,7 @@ }, { "cell_type": "markdown", - "id": "5", + "id": "974f378d-5056-46c4-9abc-0184101021e8", "metadata": {}, "source": [ "### Next steps\n", @@ -120,7 +181,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6", + "id": "49dc646a-4a53-4cc2-8a40-d812ee31aa4f", "metadata": {}, "outputs": [], "source": [] @@ -128,7 +189,7 @@ ], "metadata": { "kernelspec": { - "display_name": "bsh", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -142,7 +203,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/afold_test/input.json b/notebooks/afold_test/input.json index 4dd0d63..af9a4e7 100644 --- a/notebooks/afold_test/input.json +++ b/notebooks/afold_test/input.json @@ -3,8 +3,7 @@ "name": "test", "sequences": [ { - "protein": - { + "protein": { "id": ["A", "B"], "sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG" } diff --git a/notebooks/afold_test/run.sh b/notebooks/afold_test/run.sh index ded2f88..8d93b9f 100644 --- a/notebooks/afold_test/run.sh +++ b/notebooks/afold_test/run.sh @@ -5,10 +5,11 @@ # Load software module module load bio/alphafold/3.0.1 + # Run with option --norun_inference to generate Multiple Sequence Alignments (MSAs) and templates python $ALPHAFOLD_BIN_DIR/run_alphafold.py \ - --json_path=afold_test/input.json \ + --json_path=/home/christine/Sandbox/BioStructureHub/notebooks/afold_test/input.json \ --db_dir=$ALPHAFOLD_DATABASES \ - --model_dir=af3models \ - --output_dir=afold_test/output \ + --model_dir=/home/christine/Sandbox/BioStructureHub/notebooks/af3models \ + --output_dir=/home/christine/Sandbox/BioStructureHub/notebooks/afold_test/output \ --norun_inference diff --git a/notebooks/afold_test/run_gpu.sh b/notebooks/afold_test/run_gpu.sh index 42d9156..fb3ee16 100644 --- a/notebooks/afold_test/run_gpu.sh +++ b/notebooks/afold_test/run_gpu.sh @@ -7,9 +7,9 @@ module load bio/alphafold/3.0.1 # Run with option --norun_data_pipeline for featurisation and model inference python $ALPHAFOLD_BIN_DIR/run_alphafold.py \ - --json_path=afold_test/output/test/test_data.json \ + --json_path=/home/christine/Sandbox/BioStructureHub/notebooks/afold_test/output/test/test_data.json \ --db_dir=$ALPHAFOLD_DATABASES \ - --model_dir=af3models \ - --output_dir=afold_test/output_gpu \ + --model_dir=/home/christine/Sandbox/BioStructureHub/notebooks/af3models \ + --output_dir=/home/christine/Sandbox/BioStructureHub/notebooks/afold_test/output_gpu \ --norun_data_pipeline From 00e22513a6f43ce4fcd3a28ed09c90f3d88744a9 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Thu, 22 Jan 2026 16:45:14 +0100 Subject: [PATCH 12/28] fixed boltz & boltzgen notebooks --- notebooks/boltz_confidence_levels.ipynb | 2 +- notebooks/boltz_input.ipynb | 30 ++++++++---------- .../confidence_input_file_model_0.json | 19 +++++++++++ .../input_file/pae_input_file_model_0.npz | Bin 0 -> 316391 bytes .../input_file/plddt_input_file_model_0.npz | Bin 0 -> 1233 bytes notebooks/boltz_test/input_file.yaml | 7 ++++ notebooks/boltz_test/run.sh | 11 +++++++ notebooks/boltzgen.ipynb | 30 +++++++----------- 8 files changed, 63 insertions(+), 36 deletions(-) create mode 100644 notebooks/boltz_test/boltz_results_input_file/predictions/input_file/confidence_input_file_model_0.json create mode 100644 notebooks/boltz_test/boltz_results_input_file/predictions/input_file/pae_input_file_model_0.npz create mode 100644 notebooks/boltz_test/boltz_results_input_file/predictions/input_file/plddt_input_file_model_0.npz create mode 100644 notebooks/boltz_test/input_file.yaml create mode 100644 notebooks/boltz_test/run.sh diff --git a/notebooks/boltz_confidence_levels.ipynb b/notebooks/boltz_confidence_levels.ipynb index fb92dbf..9f4b88f 100644 --- a/notebooks/boltz_confidence_levels.ipynb +++ b/notebooks/boltz_confidence_levels.ipynb @@ -512,7 +512,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.12.2" }, "varInspector": { "cols": { diff --git a/notebooks/boltz_input.ipynb b/notebooks/boltz_input.ipynb index 12c7189..3a1079f 100644 --- a/notebooks/boltz_input.ipynb +++ b/notebooks/boltz_input.ipynb @@ -56,7 +56,11 @@ "metadata": {}, "outputs": [], "source": [ - "MSA_PATH = BOLTZ_WORKING_DIR / \"example.a3m\"" + "MSA_PATH = BOLTZ_WORKING_DIR / \"example.a3m\"\n", + "INPUT_FILE = BOLTZ_WORKING_DIR / \"input_file.yaml\"\n", + "\n", + "BOLTZ_RUN_FILE = \"run.sh\"\n", + "BOLTZ_RUN_PATH = BOLTZ_WORKING_DIR / BOLTZ_RUN_FILE # will be created in this notebook" ] }, { @@ -66,7 +70,6 @@ "metadata": {}, "outputs": [], "source": [ - "INPUT_FILE = BOLTZ_WORKING_DIR / \"input_file.yaml\"\n", "test_file = f\"\"\"\n", "version: 1\n", "sequences:\n", @@ -82,22 +85,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ce7d88ee-4b2c-46a0-8a2c-7d8f987f61d4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File written to /home/hd/hd_hd/hd_aq354/boltz_test/run.sh.\n" - ] - } - ], + "outputs": [], "source": [ - "BOLTZ_RUN_FILE = \"run.sh\"\n", - "BOLTZ_RUN_PATH = BOLTZ_WORKING_DIR / BOLTZ_RUN_FILE # will be created in this notebook\n", - "\n", "run_file = f\"\"\"\n", "#!/bin/bash\n", "\n", @@ -123,14 +115,18 @@ "source": [ "### Run the Boltz prediction\n", "\n", - "Execute the next cell to star the run! Good luck!" + "Execute the next cell to start the run! Good luck!" ] }, { "cell_type": "code", "execution_count": 6, "id": "fb6537e4-9cd2-4526-aada-77c189eff506", - "metadata": {}, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, "outputs": [ { "name": "stdout", diff --git a/notebooks/boltz_test/boltz_results_input_file/predictions/input_file/confidence_input_file_model_0.json b/notebooks/boltz_test/boltz_results_input_file/predictions/input_file/confidence_input_file_model_0.json new file mode 100644 index 0000000..751d242 --- /dev/null +++ b/notebooks/boltz_test/boltz_results_input_file/predictions/input_file/confidence_input_file_model_0.json @@ -0,0 +1,19 @@ +{ + "confidence_score": 0.9205988049507141, + "ptm": 0.8430529832839966, + "iptm": 0.0, + "ligand_iptm": 0.0, + "protein_iptm": 0.0, + "complex_plddt": 0.9399852156639099, + "complex_iplddt": 0.9399852156639099, + "complex_pde": 0.4212375581264496, + "complex_ipde": 0.0, + "chains_ptm": { + "0": 0.8430529832839966 + }, + "pair_chains_iptm": { + "0": { + "0": 0.8430529832839966 + } + } +} \ No newline at end of file diff --git a/notebooks/boltz_test/boltz_results_input_file/predictions/input_file/pae_input_file_model_0.npz b/notebooks/boltz_test/boltz_results_input_file/predictions/input_file/pae_input_file_model_0.npz new file mode 100644 index 0000000000000000000000000000000000000000..c62e7174a1fb2b008c2f8462c794ec9e03cfe8b6 GIT binary patch literal 316391 zcmV(zK<2+tO9KQg000080000X0OPR+n*aa*|NsC0{|5jR0B~VtE^csn0RRvH5Nrhi z00000W77lx00000oVo>D73=%IZFitz$Hwl$PUbYQ3p=->Vk^cruuu>YQ4tgylr9nJ z?(Xhx7LA0u)&0M}zvmr19>;R6HO#Q)zGklTIzI=?X!FiZTlcnaceUNkiN}s#K6+{5 zVuguI)#gr|rZ7?M!lf&hj-3Dd!lh%!|F>QF$l1%s#rDf5kEkCP?M2XjO$~LN%Wf>hV2PPvL_aZ1d_FQc_3s*%~H4 zsYX+Lo@6Jl|!pT7872lll*rIMQb9dnGuhPY6?@# zqDW5*qWg##UYN%4bF;W^DG_8Ar+`-mU*{L_^LqgumR8VtiKutFjGqr{xTao1%Y=HC zUa6<#cP(!X>dDyNNMKMssdE}wsMdshk0z43HISUyNOD{w2F~AzX=!HrsAg1df1}2& z8Pm%x_-}5e(XyEVu`N7tZQ*iw3$c4zDadJM=!{m*i)Z=LHoW(LXHsYzt~G6}RBGel zi#Gnb(Z=<_HtOboXNo}^U+dd2N^HZtww2CZzjN_RD{uR^5ggWr(#=+GYqk*|+{%;P 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GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG + msa: /home/christine/Sandbox/BioStructureHub/notebooks/boltz_test/example.a3m diff --git a/notebooks/boltz_test/run.sh b/notebooks/boltz_test/run.sh new file mode 100644 index 0000000..95bf7a6 --- /dev/null +++ b/notebooks/boltz_test/run.sh @@ -0,0 +1,11 @@ + +#!/bin/bash + +module load devel/miniforge/24.9.2 +module load devel/cuda/12.8 +conda activate /mnt/sds-hd/sd25g005/boltz + + +boltz predict /home/christine/Sandbox/BioStructureHub/notebooks/boltz_test/input_file.yaml \ + --write_full_pae \ + --out_dir /home/christine/Sandbox/BioStructureHub/notebooks/boltz_test diff --git a/notebooks/boltzgen.ipynb b/notebooks/boltzgen.ipynb index 118c540..8fbf26c 100644 --- a/notebooks/boltzgen.ipynb +++ b/notebooks/boltzgen.ipynb @@ -34,7 +34,7 @@ "metadata": {}, "outputs": [], "source": [ - "WORKING_DIR = Path.home() / \"protein_design_w_Boltzgen\" # created by user\n" + "WORKING_DIR = Path.home() / \"protein_design_w_Boltzgen\" # created by user" ] }, { @@ -57,20 +57,14 @@ "BOLTZGEN_YAML_PATH = (\n", " WORKING_DIR / BOTLZGEN_INPUT_FILE\n", ") # will be created in this notebook\n", - "BOLTZGEN_RUN_PATH = (\n", - " WORKING_DIR / BOLTZGEN_RUN_FILE\n", - ") # will be created in this notebook\n", + "BOLTZGEN_RUN_PATH = WORKING_DIR / BOLTZGEN_RUN_FILE # will be created in this notebook\n", "\n", - "RESULTS_DIR = (\n", - " WORKING_DIR / \"workbench/test_run\" \n", - ") # will be created in this notebook\n", + "RESULTS_DIR = WORKING_DIR / \"workbench/test_run\" # will be created in this notebook\n", "\n", "\n", "# check that the directories exist, if not print a warning\n", "if not os.path.isdir(WORKING_DIR):\n", - " print(\n", - " f\"Directory {WORKING_DIR} does not exist! Please create this directory.\"\n", - " )" + " print(f\"Directory {WORKING_DIR} does not exist! Please create this directory.\")" ] }, { @@ -98,7 +92,7 @@ } ], "source": [ - "input_yaml = '''\n", + "input_yaml = \"\"\"\n", "entities:\n", " - protein: \n", " id: C\n", @@ -109,8 +103,8 @@ " include: \n", " - chain:\n", " id: A\n", - "'''\n", - "with open(BOLTZGEN_YAML_PATH, \"w\") as file: \n", + "\"\"\"\n", + "with open(BOLTZGEN_YAML_PATH, \"w\") as file:\n", " print(BOLTZGEN_YAML_PATH)\n", " file.write(input_yaml)\n", " print(f\"File written to {BOLTZGEN_YAML_PATH}.\")" @@ -139,7 +133,7 @@ } ], "source": [ - "run_file = f'''\n", + "run_file = f\"\"\"\n", "#!/bin/bash\n", "\n", "module load devel/miniforge/24.9.2\n", @@ -151,7 +145,7 @@ " --protocol protein-anything \\\\\n", " --num_designs 10 \\\\\n", " --budget 2 \\\\\n", - "'''\n", + "\"\"\"\n", "\n", "with open(BOLTZGEN_RUN_PATH, \"w\") as file:\n", " file.write(run_file)\n", @@ -223,9 +217,9 @@ ], "metadata": { "kernelspec": { - "display_name": "bindcraft", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "bindcraft" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -237,7 +231,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.2" } }, "nbformat": 4, From b758a0b9389ed90a8b1b253474eace67ff026c67 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 26 Jan 2026 10:31:51 +0100 Subject: [PATCH 13/28] pytest RFDiffusion and Bindcraft --- notebooks/RFDiffusion.ipynb | 34 +++++++++++++++++++++------------- notebooks/bindcraft.ipynb | 32 +++++++++++++++----------------- notebooks/conftest.py | 16 +++++++++++++++- 3 files changed, 51 insertions(+), 31 deletions(-) diff --git a/notebooks/RFDiffusion.ipynb b/notebooks/RFDiffusion.ipynb index 24f8ea5..2734ef7 100644 --- a/notebooks/RFDiffusion.ipynb +++ b/notebooks/RFDiffusion.ipynb @@ -26,7 +26,15 @@ "cell_type": "code", "execution_count": 1, "id": "69493ba2-248c-48f9-bc5d-2db40c1bf4b0", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "nbval-skip" + ] + }, "outputs": [ { "name": "stdout", @@ -50,7 +58,11 @@ "cell_type": "code", "execution_count": null, "id": "fe68f692-c3c9-44a3-90a1-a6d366e587f8", - "metadata": {}, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, "outputs": [ { "name": "stdout", @@ -110,7 +122,7 @@ "metadata": {}, "outputs": [], "source": [ - "WORKING_DIR = Path.home() / \"protein_design_RFDiffusion\"" + "RFDIFFUSION_WORKING_DIR = Path.home() / \"protein_design_RFDiffusion\"" ] }, { @@ -149,7 +161,7 @@ " 'contigmap.contigs=[10-40/A163-181/10-40]'\n", "\"\"\"\n", "\n", - "RUN_PATH = WORKING_DIR / \"run.sh\" # file name!\n", + "RUN_PATH = RFDIFFUSION_WORKING_DIR / \"run.sh\" # file name!\n", "\n", "with open(RUN_PATH, \"w\") as file:\n", " file.write(run_file)" @@ -167,7 +179,11 @@ "cell_type": "code", "execution_count": 31, "id": "9af1ea5f-95c2-4df0-a326-f87ab10b28ce", - "metadata": {}, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, "outputs": [ { "name": "stdout", @@ -561,14 +577,6 @@ "os.system(f'echo \"Running file {RUN_PATH}\"')\n", "os.system(f\"bash {RUN_PATH}\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ec91da6-65d5-482d-ae15-b49a24f26e31", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/bindcraft.ipynb b/notebooks/bindcraft.ipynb index 2fcb2ba..e7394a7 100644 --- a/notebooks/bindcraft.ipynb +++ b/notebooks/bindcraft.ipynb @@ -29,7 +29,10 @@ "execution_count": 2, "id": "6c79622f-88ca-4c30-9655-1c6c2d7b545f", "metadata": { - "scrolled": true + "scrolled": true, + "tags": [ + "nbval-skip" + ] }, "outputs": [ { @@ -71,7 +74,7 @@ "metadata": {}, "outputs": [], "source": [ - "WORKING_DIR = Path.home() / \"protein_design_w_Bindcraft\"" + "BINDCRAFT_WORKING_DIR = Path.home() / \"protein_design_w_Bindcraft\"" ] }, { @@ -90,16 +93,16 @@ "outputs": [], "source": [ "data = {\n", - " \"design_path\": str(WORKING_DIR),\n", + " \"design_path\": str(BINDCRAFT_WORKING_DIR),\n", " \"binder_name\": \"PDL1\",\n", - " \"starting_pdb\": str(WORKING_DIR / \"PDL1.pdb\"),\n", + " \"starting_pdb\": str(BINDCRAFT_WORKING_DIR / \"PDL1.pdb\"),\n", " \"chains\": \"A\",\n", " \"target_hotspot_residues\": \"56\",\n", " \"lengths\": [65, 150],\n", " \"number_of_final_designs\": 100,\n", "}\n", "\n", - "bindcraft_settings = WORKING_DIR / \"input.json\"\n", + "bindcraft_settings = BINDCRAFT_WORKING_DIR / \"input.json\"\n", "\n", "with open(bindcraft_settings, \"w\") as json_file:\n", " json.dump(data, json_file, indent=4) # Use indent for pretty-printing" @@ -154,7 +157,10 @@ "execution_count": null, "id": "bbb52775-e65b-4c81-8486-d2702f620f9a", "metadata": { - "scrolled": true + "scrolled": true, + "tags": [ + "nbval-skip" + ] }, "outputs": [ { @@ -372,21 +378,13 @@ "## Known issues\n", "Currently we have to deactivate the trajectory gifs via ffmpeg. See https://github.com/martinpacesa/BindCraft/issues/20 " ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40eb1eb2-6b70-4454-a2cd-d9767b8c9042", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "bindcraft", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "bindcraft" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -398,7 +396,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/conftest.py b/notebooks/conftest.py index 25c121a..45f9abb 100644 --- a/notebooks/conftest.py +++ b/notebooks/conftest.py @@ -1,9 +1,16 @@ from pathlib import Path import shutil +# import os + +# os.environ["HOME"] = os.getcwd() + ALPHAFOLD_MODEL_DIR = Path.home() / "af3models" ALPHAFOLD_WORKING_DIR = Path.home() / "afold_test" # must be created by user ALPHAFOLD_RESULTS_DIR_PART1 = ALPHAFOLD_WORKING_DIR / "output" +BOLTZ_WORKING_DIR = Path.home() / "boltz_test" # must be created by user +RFDIFFUSION_WORKING_DIR = Path.home() / "protein_design_RFDiffusion" +BINDCRAFT_WORKING_DIR = Path.home() / "protein_design_w_Bindcraft" def pytest_sessionstart(session): @@ -15,6 +22,10 @@ def pytest_sessionstart(session): ALPHAFOLD_WORKING_DIR.mkdir(exist_ok=True) ALPHAFOLD_RESULTS_DIR_PART1.mkdir(exist_ok=True) + BOLTZ_WORKING_DIR.mkdir(exist_ok=True) + RFDIFFUSION_WORKING_DIR.mkdir(exist_ok=True) + BINDCRAFT_WORKING_DIR.mkdir(exist_ok=True) + def pytest_sessionfinish(session, exitstatus): """ @@ -23,4 +34,7 @@ def pytest_sessionfinish(session, exitstatus): """ # delete all the created dirs shutil.rmtree(ALPHAFOLD_MODEL_DIR) - shutil.rmtree(ALPHAFOLD_WORKING_DIR) + shutil.rmtree(RFDIFFUSION_WORKING_DIR) + shutil.rmtree(BINDCRAFT_WORKING_DIR) + # shutil.rmtree(ALPHAFOLD_WORKING_DIR) + # shutil.rmtree(BOLTZ_WORKING_DIR) From d9ba101c6be1bf498894d3f9389a356f5209376f Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 26 Jan 2026 11:28:44 +0100 Subject: [PATCH 14/28] trace error AFold on macos --- notebooks/AFold_Confidence_Levels.ipynb | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/notebooks/AFold_Confidence_Levels.ipynb b/notebooks/AFold_Confidence_Levels.ipynb index be798d6..66cc08f 100644 --- a/notebooks/AFold_Confidence_Levels.ipynb +++ b/notebooks/AFold_Confidence_Levels.ipynb @@ -13,18 +13,24 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "f8b82069-92f6-4164-820d-3faa067047ca", "metadata": {}, "outputs": [], "source": [ - "from Bio.PDB.MMCIF2Dict import MMCIF2Dict\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from pathlib import Path" + "try:\n", + " from Bio.PDB.MMCIF2Dict import MMCIF2Dict\n", + " import json\n", + " import pandas as pd\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " import seaborn as sns\n", + " from pathlib import Path\n", + "except Exception:\n", + " import traceback\n", + "\n", + " print(\"Error during imports:\")\n", + " traceback.print_exc()" ] }, { From 21fc6b4d9e55149b01ef9c284eb5e218b776ccd4 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 26 Jan 2026 11:36:26 +0100 Subject: [PATCH 15/28] Revert "trace error AFold on macos" This reverts commit d9ba101c6be1bf498894d3f9389a356f5209376f. --- notebooks/AFold_Confidence_Levels.ipynb | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/notebooks/AFold_Confidence_Levels.ipynb b/notebooks/AFold_Confidence_Levels.ipynb index 66cc08f..be798d6 100644 --- a/notebooks/AFold_Confidence_Levels.ipynb +++ b/notebooks/AFold_Confidence_Levels.ipynb @@ -13,24 +13,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "f8b82069-92f6-4164-820d-3faa067047ca", "metadata": {}, "outputs": [], "source": [ - "try:\n", - " from Bio.PDB.MMCIF2Dict import MMCIF2Dict\n", - " import json\n", - " import pandas as pd\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " import seaborn as sns\n", - " from pathlib import Path\n", - "except Exception:\n", - " import traceback\n", - "\n", - " print(\"Error during imports:\")\n", - " traceback.print_exc()" + "from Bio.PDB.MMCIF2Dict import MMCIF2Dict\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path" ] }, { From 908ff64e97668aa29791578a3d854d9e2d03be36 Mon Sep 17 00:00:00 2001 From: Christine Date: Mon, 26 Jan 2026 13:25:01 +0100 Subject: [PATCH 16/28] new notebook tests --- .github/workflows/main.yml | 31 ++++++++++++++++++++++++++++--- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 50972f8..20c6986 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -33,15 +33,40 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - - name: Run Alignment notebook + - name: Run AlphaFold3 Alignment notebook run: | cd notebooks python -m pytest --nbval AFold_Alignment_CPU.ipynb - - name: Run Inference notebook + - name: Run AlphaFold3 Inference notebook run: | cd notebooks python -m pytest --nbval AFold_Diffusion_GPU.ipynb - - name: Run Analysis + - name: Run AlphaFold3 Analysis notebook run: | cd notebooks python -m pytest --nbval AFold_Confidence_Levels.ipynb + + - name: Run Bindcraft notebook + run: | + cd notebooks + python -m pytest --nbval bindcraft.ipynb + + - name: Run Boltz Inference notebook + run: | + cd notebooks + python -m pytest --nbval boltz_input.ipynb + + - name: Run Boltz Analysis notebook + run: | + cd notebooks + python -m pytest --nbval boltz_confidence_levels.ipynb + + - name: Run Boltzgen + run: | + cd notebooks + python -m pytest --nbval boltzgen.ipynb + + - name: Run RFDiffusion + run: | + cd notebooks + python -m pytest --nbval RFDiffusion.ipynb From 4918da634deb8e38167483551a9d4e2761b8cb40 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 26 Jan 2026 14:48:21 +0100 Subject: [PATCH 17/28] fixed links in tutorials --- docs/tutorials/tutorial_Bindcraft_bwVisu.md | 2 +- docs/tutorials/tutorial_Boltzgen_bwVisu.md | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md index 3a4eca2..5ceb469 100644 --- a/docs/tutorials/tutorial_Bindcraft_bwVisu.md +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -8,7 +8,7 @@ To start, get access to bwVisu via bwForCluster Helix or SDS. For more informati [https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) -For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](../contact.md) for support. ### Step 2: Connect to bwVisu and Start Jupyter diff --git a/docs/tutorials/tutorial_Boltzgen_bwVisu.md b/docs/tutorials/tutorial_Boltzgen_bwVisu.md index e808b72..0321db3 100644 --- a/docs/tutorials/tutorial_Boltzgen_bwVisu.md +++ b/docs/tutorials/tutorial_Boltzgen_bwVisu.md @@ -8,7 +8,7 @@ To start, get access to bwVisu via bwForCluster Helix or SDS. For more informati [https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu](https://www.urz.uni-heidelberg.de/en/service-catalogue/software-and-applications/bwvisu) -For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](/contact) for support. +For technical questions regarding the high performance cluster, see [https://bw-support.scc.kit.edu](https://bw-support.scc.kit.edu). Feel free to [contact us](../contact.md) for support. ### Step 2: Start the calculation @@ -19,11 +19,11 @@ Request a GPU core of type A40. Choose the Kernel Path to Boltzen ./mnt/sds-hd/s Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. -Upload the notebooks in (link) and the [1g13.cif](https://www.rcsb.org/structure/1G13) file by clicking on the upload button: +Upload the notebooks from our [github](https://github.com/ssciwr/BioStructureHub/tree/main/notebooks) and the [1g13.cif](https://www.rcsb.org/structure/1G13) file by clicking on the upload button: ![Screenshot](../images/tutorial/bwVisu_upload.png){: style="height:111px;width:444px"} -After the upload, you can see the notebooks in the file browser on the left. +After the upload, you can see the notebooks in the file browser on the left. ![Screenshot](../images/tutorial/bwVisu_Boltzgen_files.png){: style="width:268px"} From 1355c7a2092fc49b5f1458129b3e8781cb3e7a1d Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Tue, 27 Jan 2026 14:33:19 +0100 Subject: [PATCH 18/28] fixed printing boltz / boltzgen --- notebooks/boltz_input.ipynb | 17 +-- notebooks/boltzgen.ipynb | 293 ++++++++++++++++++++++++++++++------ 2 files changed, 258 insertions(+), 52 deletions(-) diff --git a/notebooks/boltz_input.ipynb b/notebooks/boltz_input.ipynb index 3a1079f..6f91a27 100644 --- a/notebooks/boltz_input.ipynb +++ b/notebooks/boltz_input.ipynb @@ -56,11 +56,7 @@ "metadata": {}, "outputs": [], "source": [ - "MSA_PATH = BOLTZ_WORKING_DIR / \"example.a3m\"\n", - "INPUT_FILE = BOLTZ_WORKING_DIR / \"input_file.yaml\"\n", - "\n", - "BOLTZ_RUN_FILE = \"run.sh\"\n", - "BOLTZ_RUN_PATH = BOLTZ_WORKING_DIR / BOLTZ_RUN_FILE # will be created in this notebook" + "MSA_PATH = BOLTZ_WORKING_DIR / \"example.a3m\"" ] }, { @@ -70,6 +66,7 @@ "metadata": {}, "outputs": [], "source": [ + "INPUT_FILE = BOLTZ_WORKING_DIR / \"input_file.yaml\"\n", "test_file = f\"\"\"\n", "version: 1\n", "sequences:\n", @@ -85,11 +82,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ce7d88ee-4b2c-46a0-8a2c-7d8f987f61d4", "metadata": {}, "outputs": [], "source": [ + "BOLTZ_RUN_FILE = \"run.sh\"\n", + "BOLTZ_RUN_PATH = BOLTZ_WORKING_DIR / BOLTZ_RUN_FILE # will be created in this notebook\n", + "\n", "run_file = f\"\"\"\n", "#!/bin/bash\n", "\n", @@ -104,8 +104,7 @@ "\"\"\"\n", "\n", "with open(BOLTZ_RUN_PATH, \"w\") as file:\n", - " file.write(run_file)\n", - " print(f\"File written to {BOLTZ_RUN_PATH}.\")" + " file.write(run_file)" ] }, { @@ -115,7 +114,7 @@ "source": [ "### Run the Boltz prediction\n", "\n", - "Execute the next cell to start the run! Good luck!" + "Execute the next cell to star the run! Good luck!" ] }, { diff --git a/notebooks/boltzgen.ipynb b/notebooks/boltzgen.ipynb index 8fbf26c..c5e6a0c 100644 --- a/notebooks/boltzgen.ipynb +++ b/notebooks/boltzgen.ipynb @@ -34,7 +34,7 @@ "metadata": {}, "outputs": [], "source": [ - "WORKING_DIR = Path.home() / \"protein_design_w_Boltzgen\" # created by user" + "BOLTZGEN_WORKING_DIR = Path.home() / \"protein_design_w_Boltzgen\" # created by user" ] }, { @@ -55,16 +55,22 @@ "BOTLZGEN_INPUT_FILE = \"input.yaml\" # will be created in this notebook\n", "BOLTZGEN_RUN_FILE = \"run.sh\" # will be created in this notebook\n", "BOLTZGEN_YAML_PATH = (\n", - " WORKING_DIR / BOTLZGEN_INPUT_FILE\n", + " BOLTZGEN_WORKING_DIR / BOTLZGEN_INPUT_FILE\n", + ") # will be created in this notebook\n", + "BOLTZGEN_RUN_PATH = (\n", + " BOLTZGEN_WORKING_DIR / BOLTZGEN_RUN_FILE\n", ") # will be created in this notebook\n", - "BOLTZGEN_RUN_PATH = WORKING_DIR / BOLTZGEN_RUN_FILE # will be created in this notebook\n", "\n", - "RESULTS_DIR = WORKING_DIR / \"workbench/test_run\" # will be created in this notebook\n", + "RESULTS_DIR = (\n", + " BOLTZGEN_WORKING_DIR / \"workbench/test_run\"\n", + ") # will be created in this notebook\n", "\n", "\n", "# check that the directories exist, if not print a warning\n", - "if not os.path.isdir(WORKING_DIR):\n", - " print(f\"Directory {WORKING_DIR} does not exist! Please create this directory.\")" + "if not os.path.isdir(BOLTZGEN_WORKING_DIR):\n", + " print(\n", + " f\"Directory {BOLTZGEN_WORKING_DIR} does not exist! Please create this directory.\"\n", + " )" ] }, { @@ -81,16 +87,7 @@ "execution_count": 4, "id": "02a5d38b-5249-4cee-8e69-a78eca1ded7e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/hd/hd_hd/hd_aq354/protein_design/input.yaml\n", - "File written to /home/hd/hd_hd/hd_aq354/protein_design/input.yaml.\n" - ] - } - ], + "outputs": [], "source": [ "input_yaml = \"\"\"\n", "entities:\n", @@ -105,9 +102,7 @@ " id: A\n", "\"\"\"\n", "with open(BOLTZGEN_YAML_PATH, \"w\") as file:\n", - " print(BOLTZGEN_YAML_PATH)\n", - " file.write(input_yaml)\n", - " print(f\"File written to {BOLTZGEN_YAML_PATH}.\")" + " file.write(input_yaml)" ] }, { @@ -123,15 +118,7 @@ "execution_count": 5, "id": "dae5c5ba-19de-4942-a859-ef58a5cc76df", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File written to /home/hd/hd_hd/hd_aq354/protein_design/run.sh.\n" - ] - } - ], + "outputs": [], "source": [ "run_file = f\"\"\"\n", "#!/bin/bash\n", @@ -140,6 +127,7 @@ "module load devel/cuda/12.8\n", "conda activate /mnt/sds-hd/sd25g005/boltzgen\n", "\n", + "\n", "boltzgen run {str(BOLTZGEN_YAML_PATH)} \\\\\n", " --output {str(RESULTS_DIR)} \\\\\n", " --protocol protein-anything \\\\\n", @@ -148,8 +136,7 @@ "\"\"\"\n", "\n", "with open(BOLTZGEN_RUN_PATH, \"w\") as file:\n", - " file.write(run_file)\n", - " print(f\"File written to {BOLTZGEN_RUN_PATH}.\")" + " file.write(run_file)" ] }, { @@ -163,38 +150,250 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "82350c1e-2fcb-428b-8ae1-3413cb8356d8", - "metadata": {}, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Running file /home/hd/hd_hd/hd_aq354/protein_design/run.sh\n" + "Running file /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/run.sh\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.5, which is newer than your current Lightning version: v2.5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Step 1/6] design - Predicting DataLoader 0: : 20%|██ | 2/10 [00:38<02:34, 0.05it/s]Switched step_scale to 2.0\n", + "Switched noise_scale to 0.88\n", + "[Step 1/6] design - Predicting DataLoader 0: : 50%|█████ | 5/10 [01:31<01:31, 0.05it/s]Loaded weights from /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltzgen1_adherence.ckpt\n", + "Switched checkpoint.\n", + "[Step 1/6] design - Predicting DataLoader 0: : 60%|██████ | 6/10 [01:52<01:15, 0.05it/s]Switched step_scale to 1.8\n", + "Switched noise_scale to 0.95\n", + "[Step 1/6] design - Predicting DataLoader 0: : 80%|████████ | 8/10 [02:26<00:36, 0.05it/s]Switched step_scale to 2.0\n", + "Switched noise_scale to 0.88\n", + "[Step 1/6] design - Predicting DataLoader 0: : 100%|██████████| 10/10 [03:02<00:00, 0.05it/s]Number of failed examples: 0\n", + "[Step 1/6] design - Predicting DataLoader 0: : 100%|██████████| 10/10 [03:02<00:00, 0.05it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.5, which is newer than your current Lightning version: v2.5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing FromGeneratedDataModule datasets for /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs\n", + "[Info] Number of files to process (including already processed ones): 10\n", + "Found 1 targets and 10 remaining designs that still need to be processed in this step.\n", + "[Step 2/6] inverse_folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [00:08<00:00, 1.13it/s]Number of failed examples: 0\n", + "[Step 2/6] inverse_folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [00:08<00:00, 1.13it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.5, which is newer than your current Lightning version: v2.5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing FromGeneratedDataModule datasets for /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs_inverse_folded\n", + "[Info] Number of files to process (including already processed ones): 10\n", + "Found 1 targets and 10 remaining designs that still need to be processed in this step.\n", + "[Step 3/6] folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [04:08<00:00, 0.04it/s]Number of failed structure predictions: 0\n", + "[Step 3/6] folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [04:08<00:00, 0.04it/s]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Traceback (most recent call last):\n", - " File \"/home/hd/hd_hd/hd_aq354/.local/bin/boltzgen\", line 5, in \n", - " from boltzgen.cli.boltzgen import main\n", - " File \"/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/boltzgen/cli/boltzgen.py\", line 46, in \n", - " import torch\n", - " File \"/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/torch/__init__.py\", line 34, in \n", - " from typing_extensions import ParamSpec as _ParamSpec\n", - "ModuleNotFoundError: No module named 'typing_extensions'\n" + "/home/hd/hd_hd/hd_aq354/.local/lib/python3.12/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.5, which is newer than your current Lightning version: v2.5.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing FromGeneratedDataModule datasets for /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs_inverse_folded\n", + "[Info] Number of files to process (including already processed ones): 10\n", + "Found 1 targets and 10 remaining designs that still need to be processed in this step.\n", + "[Step 4/6] design_folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [01:43<00:00, 0.10it/s]Number of failed structure predictions: 0\n", + "[Step 4/6] design_folding - Predicting DataLoader 0: : 100%|██████████| 10/10 [01:43<00:00, 0.10it/s]\n", + "Initializing FromGeneratedDataModule datasets for /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs_inverse_folded\n", + "[Info] Number of files to process (including already processed ones): 10\n", + "Found 1 targets and 10 remaining designs that still need to be processed in this step.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing samples: 10%|█ | 1/10 [01:07<10:03, 67.09s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "POOL BROKEN: A worker died. Restarting with remaining tasks…\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing samples: 100%|██████████| 10/10 [02:14<00:00, 13.49s/it]\n", + "Loading saved metrics from disk. 1% of total: 100%|██████████| 10/10 [00:00<00:00, 57.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed metrics successfully for 9 out of 10.\n", + "Number of targets: 1. Number of designs: 10.\n", + "Total number of designs: 10\n", + "Duplicates found: 0. Removing duplicates. 10 designs remain.\n", + "\n", + "Num designs that pass the has_x filter with threshold 0 where lower is better: 10\n", + "Remaining designs: 10\n", + "Num designs that pass the filter_rmsd filter with threshold 2.5 where lower is better: 3\n", + "Remaining designs: 3\n", + "Num designs that pass the filter_rmsd_design filter with threshold 2.5 where lower is better: 5\n", + "Remaining designs: 3\n", + "Num designs that pass the designfolding-filter_rmsd filter with threshold 2.5 where lower is better: 5\n", + "Remaining designs: 1\n", + "Num designs that pass the ALA_fraction filter with threshold 0.3 where lower is better: 7\n", + "Remaining designs: 1\n", + "Num designs that pass the GLY_fraction filter with threshold 0.2 where lower is better: 7\n", + "Remaining designs: 1\n", + "Num designs that pass the GLU_fraction filter with threshold 0.2 where lower is better: 10\n", + "Remaining designs: 1\n", + "Num designs that pass the LEU_fraction filter with threshold 0.3 where lower is better: 10\n", + "Remaining designs: 1\n", + "Num designs that pass the VAL_fraction filter with threshold 0.2 where lower is better: 10\n", + "Remaining designs: 1\n", + "Only 1 designs pass filters. We highly recommend relaxing the thresholds.\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Performing lazy greedy diversity optimization.: 100%|██████████| 1/1 [00:00<00:00, 17119.61it/s]\n", + "copy top design files: 10it [00:00, 541.77it/s]\n", + "copy diversity files: 100%|██████████| 2/2 [00:00<00:00, 628.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files + CSV saved to /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/final_ranked_designs\n", + "\n", + "Writing design files is done. Now making plots for a final summary .pdf file with statistics.\n", + "A description of metrics and summarizing plots was written to: /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/final_ranked_designs/results_overview.pdf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Making liability plots for top 0 sequences.: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Configuring pipeline ===\n", + "Using dataset artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/datasets--boltzgen--inference-data/snapshots/c3d36fd276e9caf098c75d4113c6d5eb320b1a4c/mols.zip\n", + "************** Checking design spec: /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/input.yaml **************\n", + "Total designed residues: 107\n", + "Design specification visualization is written to /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/input.cif\n", + "****************************************************************************************************************\n", + "Using kernels: True [device capability: (8, 6)]\n", + "Config overrides for protocol protein-anything: {}\n", + "Using 1 devices\n", + "Raw designs will be saved to: /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs\n", + "Using diffusion batch size: 1\n", + "Number of diffusion batches: 10\n", + "Using model artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltzgen1_diverse.ckpt\n", + "Using model artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltzgen1_adherence.ckpt\n", + "Inverse-folded designs will be saved to: /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/intermediate_designs_inverse_folded\n", + "Using model artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltzgen1_ifold.ckpt\n", + "Using model artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltz2_conf_final.ckpt\n", + "Using model artifact: /home/hd/hd_hd/hd_aq354/.cache/huggingface/hub/models--boltzgen--boltzgen-1/snapshots/c1be29e1f82ffcc72264f64b993c43fb4e0d17f0/boltz2_conf_final.ckpt\n", + "Final ranked designs will be saved to: /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/final_ranked_designs\n", + "[1] design \n", + "[2] inverse_folding \n", + "[3] folding \n", + "[4] design_folding \n", + "[5] analysis \n", + "[6] filtering \n", + "Renaming existing config directory to /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/previous-config-2\n", + "Configuration complete. Configs written to /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config\n", + "Steps manifest written to /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/steps.yaml\n", + "\n", + "=== Executing pipeline ===\n", + "**************************************************\n", + "Pipeline step 1 of 6: design\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/design.yaml\n", + "✓ Step design completed successfully in 221.5s\n", + "**************************************************\n", + "Pipeline step 2 of 6: inverse_folding\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/inverse_folding.yaml\n", + "✓ Step inverse_folding completed successfully in 25.3s\n", + "**************************************************\n", + "Pipeline step 3 of 6: folding\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/folding.yaml\n", + "✓ Step folding completed successfully in 288.1s\n", + "**************************************************\n", + "Pipeline step 4 of 6: design_folding\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/design_folding.yaml\n", + "✓ Step design_folding completed successfully in 152.5s\n", + "**************************************************\n", + "Pipeline step 5 of 6: analysis\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/analysis.yaml\n", + "✓ Step analysis completed successfully in 154.0s\n", + "**************************************************\n", + "Pipeline step 6 of 6: filtering\n", + "Running command: /mnt/sds-hd/sd25g005/boltzgen/bin/python3.12 /mnt/sds-hd/sd25g005/boltzgen/lib/python3.12/site-packages/boltzgen/resources/main.py /home/hd/hd_hd/hd_aq354/protein_design_w_Boltzgen/workbench/test_run/config/filtering.yaml\n", + "✓ Step filtering completed successfully in 17.0s\n" ] }, { "data": { "text/plain": [ - "256" + "0" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -213,6 +412,14 @@ "### Next Steps\n", "In your output directory you will find the directory final_ranked_designs which contains the overview pdf and csv files for interpretation.\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03bf1625-42c8-4176-b4b5-0f99dc87930f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From f0938f0e2147ec67f4971eb86a1989af2be1d876 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Fri, 30 Jan 2026 13:30:12 +0100 Subject: [PATCH 19/28] fix feedback bindcraft + rfdiffusion --- docs/tutorials/tutorial_Bindcraft_bwVisu.md | 4 ++-- notebooks/RFDiffusion.ipynb | 4 ++-- notebooks/bindcraft.ipynb | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md index 5ceb469..9988699 100644 --- a/docs/tutorials/tutorial_Bindcraft_bwVisu.md +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -19,7 +19,7 @@ Choose Jupyter and start a new session. To use Bindcraft, we need to request a GPU core of type A40. Choose the Kernel Path to Bindcraft: -./mnt/sds-hd/sd25g005/bindcraft/share/jupyter/ +`/mnt/sds-hd/sd25g005/bindcraft/share/jupyter/` ![Screenshot](../images/tutorial/bwVisu_GPU.png) @@ -53,4 +53,4 @@ If you can see the modules in your module list at the top right, but not listed ### Step 4: Start the Calculation -Now execute the cells in the notebook to start your Boltzgen run! +Now execute the cells in the notebook to start your Bindcraft run! diff --git a/notebooks/RFDiffusion.ipynb b/notebooks/RFDiffusion.ipynb index 2734ef7..73cc600 100644 --- a/notebooks/RFDiffusion.ipynb +++ b/notebooks/RFDiffusion.ipynb @@ -97,8 +97,8 @@ ], "source": [ "# additional info in the RFDiffusion module\n", - "! module help bio/rfdiffusion/1.1.0\n", - "\n", + "info = !! module help bio/rfdiffusion/1.1.0\n", + "print(*info, sep=\"\\n\")\n", "# Material used in the documentation can be found in $RFDIFFUSION_HOME:\n", "# Scripts in $RFDIFFUSION_HOME/scripts\n", "# Examples in $RFDIFFUSION_HOME/examples\n", diff --git a/notebooks/bindcraft.ipynb b/notebooks/bindcraft.ipynb index e7394a7..3911117 100644 --- a/notebooks/bindcraft.ipynb +++ b/notebooks/bindcraft.ipynb @@ -373,7 +373,7 @@ "id": "19f3d7bf-aa4f-4915-8dc0-2d34ffbc7433", "metadata": {}, "source": [ - "You find the results in your `WORKING_DIR` on the right. Note that it takes quite a long time to complete the run, see the discussion [here[(runtime https://github.com/martinpacesa/BindCraft/issues/83) to get an idea.\n", + "You find the results in your `WORKING_DIR` on the right. Note that it takes quite a long time to complete the run, see the discussion [here](https://github.com/martinpacesa/BindCraft/issues/83) to get an idea.\n", "\n", "## Known issues\n", "Currently we have to deactivate the trajectory gifs via ffmpeg. 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z{NZq|4MC4}6!^j`fU?+3e051$#!Z%eUHSHMQ}(D#f8(;-{osEN{4%xET;+F7rAOG1}95y{p$7LyET+aRHm;d?(_X*f}SZruO7`}ofh|Nh!}Bz#0D Y8FehW{~qD*$4T9kzjHn7n(4Fu0mjKh-v9sr literal 0 HcmV?d00001 diff --git a/docs/tutorials/tutorial_AF_bwVisu.md b/docs/tutorials/tutorial_AF_bwVisu.md index f4f06d6..3a14a08 100644 --- a/docs/tutorials/tutorial_AF_bwVisu.md +++ b/docs/tutorials/tutorial_AF_bwVisu.md @@ -127,6 +127,7 @@ The GPU is selected by "GPU Type". The memory of each GPU Type is specified in G Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! + ### Step 7: Set Up Your Diffusion Run Within the Notebook - dependencies are missing Open `AFold_Diffusion_GPU.ipynb`. @@ -183,14 +184,10 @@ The best model is presented in the output directory as well, with its structure Open the last notebook `Afold_Confidence_Levels.ipynb` to get a summary of the models confidence levels. This notebook reads the confidence descriptions and renders its central information. -For this last notebook, you need to install a few dependencies into your environment. These dependencies are libraries that are used to analyze and visualize the output. The dependencies are installed in the Jupyter notebook in the first code cell: - - %pip install biopython seaborn +For this last notebook, you need to have access to a shared directory that includes libraries that are used to analyze and visualize the output. Define the `Kernel Path` to the AlphaFold kernel at `/mnt/sds-hd/sd25g005/afold3/share/jupyter/`. [Contact us](/contact.md) for access to this shared directory. -After installing the dependencies, you need to restart the Jupyter kernel so that Jupyter finds the newly installed packages. Click on the circular arrow in the top left of the Jupyter notebook toolbar. - -![Screenshot](../images/tutorial/restart_kernel.png) -{: style="width:268px"} +![Screenshot](../images/tutorial/bwVisu_GPU_Kernel.png) + After this, the analysis should run without any errors. Explanations of the output are provided in the notebook. diff --git a/docs/tutorials/tutorial_Bindcraft_bwVisu.md b/docs/tutorials/tutorial_Bindcraft_bwVisu.md index 9988699..15ea200 100644 --- a/docs/tutorials/tutorial_Bindcraft_bwVisu.md +++ b/docs/tutorials/tutorial_Bindcraft_bwVisu.md @@ -18,11 +18,9 @@ Go to [https://bwvisu.bwservices.uni-heidelberg.de/](https://bwvisu.bwservices.u Choose Jupyter and start a new session. To use Bindcraft, we need to request a GPU core of type A40. -Choose the Kernel Path to Bindcraft: -`/mnt/sds-hd/sd25g005/bindcraft/share/jupyter/` +Choose the Kernel Path to Bindcraft: `/mnt/sds-hd/sd25g005/bindcraft/share/jupyter/` [Contact us](/contact.md) for access to this shared directory. - -![Screenshot](../images/tutorial/bwVisu_GPU.png) +![Screenshot](../images/tutorial/bwVisu_GPU_Kernel.png) Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. diff --git a/docs/tutorials/tutorial_Boltz_bwVisu.md b/docs/tutorials/tutorial_Boltz_bwVisu.md index 9b9d1a6..7fc65cf 100644 --- a/docs/tutorials/tutorial_Boltz_bwVisu.md +++ b/docs/tutorials/tutorial_Boltz_bwVisu.md @@ -34,12 +34,12 @@ For the inference step we need a GPU, so we need to request a GPU node on bwVisu ![Screenshot](../images/tutorial/Helix_GPU.png) -The GPU is selected byw "GPU Type". The memory of each GPU Type is specified in GPU Memory per GPU (GB). For this example we select one of the A40 GPUs. +The GPU is selected byw "GPU Type". The memory of each GPU Type is specified in GPU Memory per GPU (GB). For this example we select one of the A40 GPUs. Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! -![Screenshot](../images/tutorial/bwVisu_GPU.png) +![Screenshot](../images/tutorial/bwVisu_GPU_Kernel.png) -Larger jobs (= longer sequences, more chains) require more memory. To access these, it is suggested to run the job directly on the Helix cluster. We will prepare a tutorial for this shortly - feel free to contact us! +You also need to define the `Kernel Path` to the boltz kernel at `/mnt/sds-hd/sd25g005/boltzgen/share/jupyter/`. [Contact us](/contact.md) for access to this shared directory. ### Step 5: Set Up Your Diffusion Run Within the Notebook @@ -47,13 +47,10 @@ Larger jobs (= longer sequences, more chains) require more memory. To access the #### Set Environment Variables - Link the output of the MSA prediction, and the project name given in the MSA input file BOLTZ_WORKING_DIR = "boltz_test/" - - #### Write Input File First we prepare the `.yaml` input file that will be tell Boltz what to predict. @@ -64,14 +61,18 @@ Important parameters in the input file are the `name`, `sequence` and `id`, as w Remember the name of your input file as it is needed for [the analysis](#step-6-analyze-your-results). -#### Run Structure Prediction +#### Write Run File + +Next we need to write the `run file`, which loads all relevant modules and handles the Boltz `.yaml` file in a program call. You do not need to change these parameters. A full list is available [here](https://github.com/jwohlwend/boltz/blob/main/docs/prediction.md#options). Only change the parameters if you know what you are doing. + +#### Run the Prediction -Run the structure prediction by executing the next cell: +Run the prediction by executing the next cell: - ! CUDA_VISIBLE_DEVICES=0 boltz predict {BOLTZ_WORKING_DIR}/input.yaml --write_full_pae --out_dir {BOLTZ_WORKING_DIR} - + os.system(f'echo "Running file {BOLTZ_RUN_PATH}"') + os.system(f"bash {BOLTZ_RUN_PATH}") -This may take a few minutes, but eventually, you should see... +This may take a few minutes, but eventually, you should see (among other things)... ![Screenshot](../images/tutorial/bwVisu_Boltz_done.png) diff --git a/docs/tutorials/tutorial_Boltzgen_bwVisu.md b/docs/tutorials/tutorial_Boltzgen_bwVisu.md index 0321db3..aedb2bf 100644 --- a/docs/tutorials/tutorial_Boltzgen_bwVisu.md +++ b/docs/tutorials/tutorial_Boltzgen_bwVisu.md @@ -12,9 +12,9 @@ For technical questions regarding the high performance cluster, see [https://bw- ### Step 2: Start the calculation -Request a GPU core of type A40. Choose the Kernel Path to Boltzen ./mnt/sds-hd/sd25g005/boltzgen/share/jupyter/ +Request a GPU core of type A40. Choose the Kernel Path to Boltzen `/mnt/sds-hd/sd25g005/boltzgen/share/jupyter/` [Contact us](/contact.md) for access to this shared directory. -![Screenshot](../images/tutorial/bwVisu_GPU.png) +![Screenshot](../images/tutorial/bwVisu_GPU_Kernel.png) Click on "Launch". This will bring you to a new screen showing your interactive sessions. Wait for your session to be ready, then click on "Connect to Jupyter". This brings you into a JupyterLab environment. 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ze?@3CGIQ!xb{B{`GcX){Xkf9%HdIPACTL4vSst;@nlLFUzhCEJfbZ|aY$MBoR@isI z(VGe-uT{POZ9a*Ff^#7t2|>fwlu_ODY-w1ya0k5DuR-QaGcTSr4Eh|ILp8q8P zi%auo`IlV-(u`~ukh_?yBTFIz4(mrq#5j~<gOLsg!B!cr+#t>;$V}tv}Vzs`;an zZ-gfc?8)E8gATBHY%ywTWa2c4EQ=uTI=hT*l#{yjz$^Z?c}rrA)_z2j{DoTkh^VV| z%r7KaYGh@ zT?8+OHa^1Ob$F`0_b`ebswjJ2)ghU6&xDBCG&>?dXdOB??@B7(L^e*p+KLn}wo6tgwm?9iqcvUYgVu%i zU%)qfeNoUth}CE?Y&_-n;uro6usmajG>HnnI~H2GcqYqjwC((Nuh-&4hM}}ht84G2OEfGsn^FXYu+p5 z9i&(_n%olc-}>AsC-11hDCRG9LC(1B(BBZ`J=j?3Wn808bRfQ10xyILwT*52F266< z0qM6OL$HM51t=Gvu3l^I3U+ Date: Mon, 2 Feb 2026 11:28:01 +0100 Subject: [PATCH 22/28] Update pip install command fighting macOS random failures --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 20c6986..98a65ba 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -32,7 +32,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r requirements.txt + python -m pip install -r requirements.txt - name: Run AlphaFold3 Alignment notebook run: | cd notebooks From dbcb3374e7718661c0403ccb16d09f025a762439 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 2 Feb 2026 16:11:11 +0100 Subject: [PATCH 23/28] attempt to fix boltz paths --- notebooks/boltz_confidence_levels.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/boltz_confidence_levels.ipynb b/notebooks/boltz_confidence_levels.ipynb index 9f4b88f..5b9079c 100644 --- a/notebooks/boltz_confidence_levels.ipynb +++ b/notebooks/boltz_confidence_levels.ipynb @@ -48,13 +48,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "583aad86-e2b3-473b-a1bf-8f9e5b809a91", "metadata": {}, "outputs": [], "source": [ "project_name = \"input_file\"\n", - "BOLTZ_WORKING_DIR = Path.home() / \"boltz_test\"" + "BOLTZ_WORKING_DIR = Path(\"boltz_test\")" ] }, { From c1fea78b77a638eaa3d8d0d55833ad3a27478c5a Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Mon, 2 Feb 2026 16:20:49 +0100 Subject: [PATCH 24/28] boltzgen conftest --- notebooks/conftest.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/notebooks/conftest.py b/notebooks/conftest.py index 45f9abb..69aad26 100644 --- a/notebooks/conftest.py +++ b/notebooks/conftest.py @@ -9,6 +9,7 @@ ALPHAFOLD_WORKING_DIR = Path.home() / "afold_test" # must be created by user ALPHAFOLD_RESULTS_DIR_PART1 = ALPHAFOLD_WORKING_DIR / "output" BOLTZ_WORKING_DIR = Path.home() / "boltz_test" # must be created by user +BOLTZGEN_WORKING_DIR = Path.home() / "protein_design_w_Boltzgen" # created by user RFDIFFUSION_WORKING_DIR = Path.home() / "protein_design_RFDiffusion" BINDCRAFT_WORKING_DIR = Path.home() / "protein_design_w_Bindcraft" @@ -23,6 +24,7 @@ def pytest_sessionstart(session): ALPHAFOLD_RESULTS_DIR_PART1.mkdir(exist_ok=True) BOLTZ_WORKING_DIR.mkdir(exist_ok=True) + BOLTZGEN_WORKING_DIR.mkdir(exist_ok=True) RFDIFFUSION_WORKING_DIR.mkdir(exist_ok=True) BINDCRAFT_WORKING_DIR.mkdir(exist_ok=True) @@ -35,6 +37,7 @@ def pytest_sessionfinish(session, exitstatus): # delete all the created dirs shutil.rmtree(ALPHAFOLD_MODEL_DIR) shutil.rmtree(RFDIFFUSION_WORKING_DIR) + shutil.rmtree(BOLTZGEN_WORKING_DIR) shutil.rmtree(BINDCRAFT_WORKING_DIR) # shutil.rmtree(ALPHAFOLD_WORKING_DIR) # shutil.rmtree(BOLTZ_WORKING_DIR) From 7f0f92ceec25890ff370aa32af05de90394d6bbf Mon Sep 17 00:00:00 2001 From: Christine Date: Mon, 2 Feb 2026 16:26:14 +0100 Subject: [PATCH 25/28] Update main.yml Disabling macOS until I figure out what happens there --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 98a65ba..86f7183 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,7 +20,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, macos-latest, windows-latest] + os: [ubuntu-latest, windows-latest] python-version: [3.9, 3.13] steps: - name: Set up Python ${{ matrix.python-version }} From 65dadf9a771c9b26a5e91c33cdb1d01c7e898c65 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Tue, 3 Feb 2026 10:55:21 +0100 Subject: [PATCH 26/28] fix macos by pre-caching matplotlib --- .github/workflows/main.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 98a65ba..b1d75eb 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -33,6 +33,9 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install -r requirements.txt + - name: Pre-cache matplotlib font (cross-platform) + run: | + python -c "import os; os.environ['MPLBACKEND']='Agg'; import matplotlib.pyplot" - name: Run AlphaFold3 Alignment notebook run: | cd notebooks From 93727c254d2c9029df7f01729c735b6c18d3a784 Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Tue, 3 Feb 2026 10:57:51 +0100 Subject: [PATCH 27/28] fix macos by pre-caching matplotlib --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6afc571..b1d75eb 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,7 +20,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, windows-latest] + os: [ubuntu-latest, macos-latest, windows-latest] python-version: [3.9, 3.13] steps: - name: Set up Python ${{ matrix.python-version }} From 05b568e0aea2314efcb89d99498f2f26b3d56b45 Mon Sep 17 00:00:00 2001 From: Christine Date: Tue, 3 Feb 2026 10:58:44 +0100 Subject: [PATCH 28/28] fix indent --- .github/workflows/main.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b1d75eb..9d6b20f 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -34,8 +34,8 @@ jobs: python -m pip install --upgrade pip python -m pip install -r requirements.txt - name: Pre-cache matplotlib font (cross-platform) - run: | - python -c "import os; os.environ['MPLBACKEND']='Agg'; import matplotlib.pyplot" + run: | + python -c "import os; os.environ['MPLBACKEND']='Agg'; import matplotlib.pyplot" - name: Run AlphaFold3 Alignment notebook run: | cd notebooks

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Check if module list and lib dir works by executing the first cells. -If the notebook was open before, restart Kernel. +Load the GNU compiler module for fortran libraries, by clicking on the hexagon on the right and selecting compiler/gnu/11.3. You should see them as loaded modules like so: + +![Screenshot](../images/tutorial/bwVisu_Bindcraft_modules_loaded.png) +{: style="width:378px"} + +In the notebook you can check the modules by checking the output of `! module list` which should look like that: + +![Screenshot](../images/tutorial/bwVisu_Bindcraft_modules_list.png) +{: style="width:520px"} + +If you can see the modules in your module list at the top right, but not listed in the notebook, restart the kernel and execute all cells until this step again: ![Screenshot](../images/tutorial/restart_kernel.png) {: style="width:268px"} diff --git a/notebooks/bindcraft.ipynb b/notebooks/bindcraft.ipynb index d0c7689..2fcb2ba 100644 --- a/notebooks/bindcraft.ipynb +++ b/notebooks/bindcraft.ipynb @@ -2,13 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "9f800252-79ce-4048-b70e-2840182371e9", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", - "import os\n", "import json\n", "import sys\n", "import subprocess" @@ -27,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "id": "6c79622f-88ca-4c30-9655-1c6c2d7b545f", "metadata": { "scrolled": true @@ -37,6 +36,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "/opt/bwhpc/common/compiler/gnu/11.3.0/lib64:/.singularity.d/libs\n", "\n", "Currently Loaded Modules:\n", " 1) system/singularity/3.11.3 2) compiler/gnu/11.3\n", @@ -48,11 +48,12 @@ ], "source": [ "# export paths for pyrosetta\n", + "\n", "! export LD_LIBRARY_PATH=./mnt/sds-hd/sd25g005/bindcraft/lib:$LD_LIBRARY_PATH\n", + "! echo $LD_LIBRARY_PATH\n", "\n", "# test if modules and fortran libraries are loaded\n", - "! module list\n", - "# ! ls $GNU_LIB_DIR" + "! module list" ] }, { @@ -65,12 +66,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "b5fad8c4-0e2a-40b1-823b-24c7e376d0ed", "metadata": {}, "outputs": [], "source": [ - "WORKING_DIR = Path.home() / \"protein_design_w_Bindcraft\" # created by user\n" + "WORKING_DIR = Path.home() / \"protein_design_w_Bindcraft\"" ] }, { @@ -83,26 +84,25 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "c2c6e298-bd3a-45b0-9093-ae4819a8bb58", "metadata": {}, "outputs": [], "source": [ "data = {\n", - " \"design_path\": str(WORKING_DIR), \n", - " \"binder_name\": \"PDL1\", \n", - " \"starting_pdb\": str(WORKING_DIR / \"PDL1.pdb\"), \n", - " \"chains\": \"A\", \n", - " \"target_hotspot_residues\": \"56\", \n", + " \"design_path\": str(WORKING_DIR),\n", + " \"binder_name\": \"PDL1\",\n", + " \"starting_pdb\": str(WORKING_DIR / \"PDL1.pdb\"),\n", + " \"chains\": \"A\",\n", + " \"target_hotspot_residues\": \"56\",\n", " \"lengths\": [65, 150],\n", - " \"number_of_final_designs\": 100 \n", + " \"number_of_final_designs\": 100,\n", "}\n", "\n", - "bindcraft_settings = WORKING_DIR / \"input.json\"\n", + "bindcraft_settings = WORKING_DIR / \"input.json\"\n", "\n", - "with open(bindcraft_settings, \"w\") as json_file: \n", - " json.dump(data, json_file, indent=4) # Use indent for pretty-printing\n", - "\n" + "with open(bindcraft_settings, \"w\") as json_file:\n", + " json.dump(data, json_file, indent=4) # Use indent for pretty-printing" ] }, { @@ -115,39 +115,30 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "id": "fd7c9c83-0ff6-4cb2-9b6d-cdc480e99f4b", "metadata": {}, "outputs": [], "source": [ - "BINDCRAFT_DIR = Path(\"/mnt/sds-hd/sd25g005/bindcraft/bindcraft\")\n", + "BINDCRAFT_DIR = Path(\"/mnt/sds-hd/sd25g005/bindcraft/BindCraft\")\n", "\n", "BINDCRAFT_PY = BINDCRAFT_DIR / \"bindcraft.py\"\n", "BINDCRAFT_FILTERS = BINDCRAFT_DIR / \"settings_filters\" / \"default_filters.json\"\n", - "BINDCRAFT_ADVANCED = BINDCRAFT_DIR / \"settings_advanced\" / \"default_4stage_multimer.json\"\n", + "BINDCRAFT_ADVANCED = (\n", + " BINDCRAFT_DIR / \"settings_advanced\" / \"default_4stage_multimer.json\"\n", + ")\n", "\n", - "subprocess_command = [sys.executable, \"-u\", BINDCRAFT_PY,\n", - " \"--settings\", bindcraft_settings,\n", - " \"--filters\", BINDCRAFT_FILTERS,\n", - " \"--advanced\", BINDCRAFT_ADVANCED]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "779058cc-db9c-4105-8150-36783ab89eb3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/mnt/sds-hd/sd25g005/bindcraft\n" - ] - } - ], - "source": [ - "print(BINDCRAFT_DIR)" + "subprocess_command = [\n", + " sys.executable,\n", + " \"-u\",\n", + " BINDCRAFT_PY,\n", + " \"--settings\",\n", + " bindcraft_settings,\n", + " \"--filters\",\n", + " BINDCRAFT_FILTERS,\n", + " \"--advanced\",\n", + " BINDCRAFT_ADVANCED,\n", + "]" ] }, { @@ -160,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "bbb52775-e65b-4c81-8486-d2702f620f9a", "metadata": { "scrolled": true @@ -180,114 +171,190 @@ "│ NOTE: USE OF PyRosetta FOR COMMERCIAL PURPOSES REQUIRES PURCHASE OF A LICENSE │\n", "│ See LICENSE.PyRosetta.md or email license@uw.edu for details │\n", "└───────────────────────────────────────────────────────────────────────────────┘\n", - "PyRosetta-4 2025 [Rosetta PyRosetta4.conda.ubuntu.cxx11thread.serialization.Ubuntu.python310.Release 2025.45+release.d79cb06334818403e40289334138fb126753c253 2025-11-04T12:19:41] retrieved from: http://www.pyrosetta.org\n", + "PyRosetta-4 2026 [Rosetta PyRosetta4.conda.ubuntu.cxx11thread.serialization.Ubuntu.python310.Release 2026.03+release.5e498f1409c68ade56c8ce5842bf79e1b02e8db4 2026-01-13T13:24:11] retrieved from: http://www.pyrosetta.org\n", "Running binder design for target input\n", "Design settings used: default_4stage_multimer\n", "Filtering designs based on default_filters\n", - "Starting trajectory: PDL1_l144_s794724\n", + "Starting trajectory: PDL1_l89_s699438\n", "Stage 1: Test Logits\n", - "1 models [0] recycles 1 hard 0 soft 0.02 temp 1 loss 13.99 helix 1.63 pae 0.87 i_pae 0.89 con 4.92 i_con 4.38 plddt 0.35 ptm 0.44 i_ptm 0.10 rg 15.45\n", - "2 models [3] recycles 1 hard 0 soft 0.04 temp 1 loss 11.32 helix 0.94 pae 0.76 i_pae 0.80 con 4.26 i_con 4.37 plddt 0.40 ptm 0.45 i_ptm 0.11 rg 8.27\n", - "3 models [0] recycles 1 hard 0 soft 0.05 temp 1 loss 8.80 helix 0.77 pae 0.68 i_pae 0.74 con 3.87 i_con 4.07 plddt 0.42 ptm 0.47 i_ptm 0.14 rg 2.18\n", - "4 models [2] recycles 1 hard 0 soft 0.07 temp 1 loss 9.06 helix 0.66 pae 0.69 i_pae 0.78 con 3.83 i_con 4.29 plddt 0.44 ptm 0.45 i_ptm 0.11 rg 2.30\n", - "5 models [1] recycles 1 hard 0 soft 0.09 temp 1 loss 8.38 helix 0.77 pae 0.60 i_pae 0.77 con 3.37 i_con 4.20 plddt 0.52 ptm 0.46 i_ptm 0.12 rg 2.10\n", - "6 models [4] recycles 1 hard 0 soft 0.11 temp 1 loss 7.68 helix 0.65 pae 0.51 i_pae 0.64 con 2.91 i_con 3.79 plddt 0.63 ptm 0.48 i_ptm 0.18 rg 2.76\n", - "7 models [4] recycles 1 hard 0 soft 0.13 temp 1 loss 7.53 helix 0.69 pae 0.53 i_pae 0.66 con 2.93 i_con 3.70 plddt 0.61 ptm 0.48 i_ptm 0.17 rg 2.53\n", - "8 models [2] recycles 1 hard 0 soft 0.14 temp 1 loss 6.99 helix 0.71 pae 0.44 i_pae 0.57 con 2.53 i_con 3.56 plddt 0.73 ptm 0.52 i_ptm 0.22 rg 2.70\n", - "9 models [4] recycles 1 hard 0 soft 0.16 temp 1 loss 6.71 helix 0.76 pae 0.45 i_pae 0.58 con 2.43 i_con 3.42 plddt 0.72 ptm 0.51 i_ptm 0.20 rg 2.60\n", - "10 models [4] recycles 1 hard 0 soft 0.18 temp 1 loss 8.58 helix 0.78 pae 0.62 i_pae 0.62 con 3.77 i_con 3.76 plddt 0.47 ptm 0.51 i_ptm 0.21 rg 2.96\n", - "11 models [3] recycles 1 hard 0 soft 0.20 temp 1 loss 7.59 helix 0.89 pae 0.53 i_pae 0.59 con 3.08 i_con 3.64 plddt 0.57 ptm 0.52 i_ptm 0.22 rg 2.60\n", - "12 models [1] recycles 1 hard 0 soft 0.22 temp 1 loss 8.09 helix 0.84 pae 0.55 i_pae 0.59 con 3.51 i_con 3.69 plddt 0.54 ptm 0.51 i_ptm 0.22 rg 2.61\n", - "13 models [4] recycles 1 hard 0 soft 0.23 temp 1 loss 7.50 helix 0.78 pae 0.49 i_pae 0.52 con 3.21 i_con 3.46 plddt 0.59 ptm 0.53 i_ptm 0.25 rg 2.47\n", - "14 models [4] recycles 1 hard 0 soft 0.25 temp 1 loss 6.06 helix 0.84 pae 0.37 i_pae 0.40 con 2.35 i_con 2.91 plddt 0.77 ptm 0.61 i_ptm 0.37 rg 2.69\n", - "15 models [2] recycles 1 hard 0 soft 0.27 temp 1 loss 6.36 helix 0.83 pae 0.39 i_pae 0.45 con 2.39 i_con 3.17 plddt 0.77 ptm 0.58 i_ptm 0.32 rg 2.65\n", - "16 models [4] recycles 1 hard 0 soft 0.29 temp 1 loss 5.92 helix 0.92 pae 0.33 i_pae 0.41 con 2.00 i_con 3.12 plddt 0.82 ptm 0.64 i_ptm 0.35 rg 2.84\n", - "17 models [2] recycles 1 hard 0 soft 0.31 temp 1 loss 5.42 helix 1.00 pae 0.27 i_pae 0.31 con 1.95 i_con 2.76 plddt 0.86 ptm 0.72 i_ptm 0.55 rg 2.80\n", - "18 models [2] recycles 1 hard 0 soft 0.32 temp 1 loss 6.46 helix 0.92 pae 0.43 i_pae 0.60 con 2.05 i_con 3.62 plddt 0.82 ptm 0.55 i_ptm 0.17 rg 2.62\n", - "19 models [4] recycles 1 hard 0 soft 0.34 temp 1 loss 7.28 helix 0.81 pae 0.49 i_pae 0.58 con 2.68 i_con 3.71 plddt 0.64 ptm 0.51 i_ptm 0.20 rg 2.66\n", - "20 models [0] recycles 1 hard 0 soft 0.36 temp 1 loss 6.64 helix 0.93 pae 0.40 i_pae 0.48 con 2.45 i_con 3.40 plddt 0.72 ptm 0.56 i_ptm 0.31 rg 2.69\n", - "21 models [4] recycles 1 hard 0 soft 0.38 temp 1 loss 7.45 helix 0.89 pae 0.53 i_pae 0.66 con 2.66 i_con 3.87 plddt 0.62 ptm 0.48 i_ptm 0.17 rg 2.77\n", - "22 models [3] recycles 1 hard 0 soft 0.40 temp 1 loss 6.54 helix 0.91 pae 0.39 i_pae 0.44 con 2.46 i_con 3.26 plddt 0.73 ptm 0.58 i_ptm 0.37 rg 2.77\n", - "23 models [0] recycles 1 hard 0 soft 0.41 temp 1 loss 6.47 helix 0.95 pae 0.37 i_pae 0.47 con 2.34 i_con 3.29 plddt 0.76 ptm 0.57 i_ptm 0.33 rg 2.89\n", - "24 models [1] recycles 1 hard 0 soft 0.43 temp 1 loss 5.32 helix 1.06 pae 0.25 i_pae 0.24 con 2.15 i_con 2.42 plddt 0.81 ptm 0.73 i_ptm 0.64 rg 3.02\n", - "25 models [3] recycles 1 hard 0 soft 0.45 temp 1 loss 5.77 helix 0.97 pae 0.30 i_pae 0.31 con 2.27 i_con 2.69 plddt 0.77 ptm 0.66 i_ptm 0.55 rg 3.00\n", - "26 models [3] recycles 1 hard 0 soft 0.47 temp 1 loss 5.44 helix 0.98 pae 0.26 i_pae 0.23 con 2.27 i_con 2.44 plddt 0.80 ptm 0.75 i_ptm 0.62 rg 2.87\n", - "27 models [4] recycles 1 hard 0 soft 0.49 temp 1 loss 5.05 helix 0.97 pae 0.24 i_pae 0.21 con 2.09 i_con 2.25 plddt 0.82 ptm 0.76 i_ptm 0.65 rg 2.84\n", - "28 models [4] recycles 1 hard 0 soft 0.50 temp 1 loss 4.53 helix 1.06 pae 0.21 i_pae 0.17 con 1.95 i_con 1.98 plddt 0.86 ptm 0.81 i_ptm 0.74 rg 2.67\n", - "29 models [0] recycles 1 hard 0 soft 0.52 temp 1 loss 4.44 helix 1.12 pae 0.19 i_pae 0.16 con 1.86 i_con 1.98 plddt 0.88 ptm 0.82 i_ptm 0.76 rg 2.74\n", - "30 models [3] recycles 1 hard 0 soft 0.54 temp 1 loss 6.64 helix 0.96 pae 0.39 i_pae 0.52 con 2.31 i_con 3.52 plddt 0.76 ptm 0.54 i_ptm 0.24 rg 2.75\n", - "31 models [1] recycles 1 hard 0 soft 0.56 temp 1 loss 5.55 helix 1.07 pae 0.29 i_pae 0.30 con 2.14 i_con 2.71 plddt 0.80 ptm 0.68 i_ptm 0.50 rg 2.75\n", - "32 models [2] recycles 1 hard 0 soft 0.58 temp 1 loss 4.82 helix 0.97 pae 0.24 i_pae 0.20 con 2.07 i_con 2.04 plddt 0.84 ptm 0.79 i_ptm 0.72 rg 2.87\n", - "33 models [4] recycles 1 hard 0 soft 0.59 temp 1 loss 4.44 helix 1.08 pae 0.19 i_pae 0.15 con 1.91 i_con 1.90 plddt 0.87 ptm 0.82 i_ptm 0.77 rg 2.76\n", - "34 models [0] recycles 1 hard 0 soft 0.61 temp 1 loss 4.33 helix 1.24 pae 0.19 i_pae 0.15 con 1.81 i_con 1.93 plddt 0.88 ptm 0.84 i_ptm 0.78 rg 2.83\n", - "35 models [1] recycles 1 hard 0 soft 0.63 temp 1 loss 4.36 helix 1.07 pae 0.21 i_pae 0.17 con 1.90 i_con 1.80 plddt 0.86 ptm 0.83 i_ptm 0.80 rg 2.83\n", - "36 models [0] recycles 1 hard 0 soft 0.65 temp 1 loss 4.35 helix 1.17 pae 0.19 i_pae 0.15 con 1.91 i_con 1.81 plddt 0.87 ptm 0.84 i_ptm 0.79 rg 2.88\n", - "37 models [1] recycles 1 hard 0 soft 0.67 temp 1 loss 4.18 helix 1.26 pae 0.20 i_pae 0.15 con 1.85 i_con 1.76 plddt 0.87 ptm 0.84 i_ptm 0.79 rg 2.74\n", - "38 models [4] recycles 1 hard 0 soft 0.68 temp 1 loss 4.16 helix 1.14 pae 0.19 i_pae 0.15 con 1.83 i_con 1.74 plddt 0.88 ptm 0.84 i_ptm 0.80 rg 2.76\n", - "39 models [3] recycles 1 hard 0 soft 0.70 temp 1 loss 3.94 helix 1.15 pae 0.18 i_pae 0.14 con 1.77 i_con 1.58 plddt 0.89 ptm 0.86 i_ptm 0.82 rg 2.77\n", - "40 models [0] recycles 1 hard 0 soft 0.72 temp 1 loss 4.12 helix 1.18 pae 0.19 i_pae 0.16 con 1.72 i_con 1.83 plddt 0.89 ptm 0.83 i_ptm 0.78 rg 2.70\n", - "41 models [3] recycles 1 hard 0 soft 0.74 temp 1 loss 3.82 helix 1.22 pae 0.17 i_pae 0.13 con 1.65 i_con 1.62 plddt 0.90 ptm 0.86 i_ptm 0.83 rg 2.73\n", - "42 models [2] recycles 1 hard 0 soft 0.76 temp 1 loss 7.47 helix 0.83 pae 0.54 i_pae 0.66 con 2.70 i_con 3.92 plddt 0.62 ptm 0.47 i_ptm 0.16 rg 2.46\n", - "43 models [1] recycles 1 hard 0 soft 0.77 temp 1 loss 4.58 helix 1.11 pae 0.22 i_pae 0.18 con 1.86 i_con 2.10 plddt 0.86 ptm 0.81 i_ptm 0.73 rg 2.76\n", - "44 models [3] recycles 1 hard 0 soft 0.79 temp 1 loss 3.71 helix 1.24 pae 0.17 i_pae 0.13 con 1.62 i_con 1.56 plddt 0.91 ptm 0.87 i_ptm 0.83 rg 2.66\n", - "45 models [3] recycles 1 hard 0 soft 0.81 temp 1 loss 3.81 helix 1.24 pae 0.18 i_pae 0.13 con 1.62 i_con 1.66 plddt 0.90 ptm 0.86 i_ptm 0.82 rg 2.63\n", - "46 models [4] recycles 1 hard 0 soft 0.83 temp 1 loss 3.95 helix 1.24 pae 0.19 i_pae 0.14 con 1.74 i_con 1.66 plddt 0.89 ptm 0.85 i_ptm 0.81 rg 2.73\n", - "47 models [1] recycles 1 hard 0 soft 0.85 temp 1 loss 3.58 helix 1.28 pae 0.18 i_pae 0.13 con 1.70 i_con 1.38 plddt 0.89 ptm 0.87 i_ptm 0.85 rg 2.63\n", - "48 models [3] recycles 1 hard 0 soft 0.86 temp 1 loss 3.75 helix 1.21 pae 0.19 i_pae 0.14 con 1.72 i_con 1.48 plddt 0.89 ptm 0.86 i_ptm 0.82 rg 2.67\n", - "49 models [4] recycles 1 hard 0 soft 0.88 temp 1 loss 5.05 helix 1.22 pae 0.23 i_pae 0.23 con 1.83 i_con 2.64 plddt 0.86 ptm 0.75 i_ptm 0.59 rg 2.68\n", - "50 models [2] recycles 1 hard 0 soft 0.90 temp 1 loss 8.59 helix 0.83 pae 0.66 i_pae 0.72 con 3.70 i_con 4.06 plddt 0.45 ptm 0.47 i_ptm 0.14 rg 2.15\n", - "Initial trajectory pLDDT good, continuing: 0.89\n", + "1 models [3] recycles 1 hard 0 soft 0.02 temp 1 loss 11.70 helix 1.74 pae 0.80 i_pae 0.79 con 4.75 i_con 4.14 plddt 0.29 ptm 0.55 i_ptm 0.10 rg 9.38\n", + "2 models [1] recycles 1 hard 0 soft 0.04 temp 1 loss 10.80 helix 1.15 pae 0.74 i_pae 0.75 con 4.54 i_con 4.12 plddt 0.37 ptm 0.54 i_ptm 0.11 rg 6.71\n", + "3 models [4] recycles 1 hard 0 soft 0.05 temp 1 loss 11.10 helix 1.22 pae 0.75 i_pae 0.73 con 4.59 i_con 3.91 plddt 0.36 ptm 0.55 i_ptm 0.11 rg 8.26\n", + "4 models [4] recycles 1 hard 0 soft 0.07 temp 1 loss 9.62 helix 0.89 pae 0.70 i_pae 0.67 con 4.20 i_con 3.81 plddt 0.41 ptm 0.54 i_ptm 0.12 rg 4.74\n", + "5 models [3] recycles 1 hard 0 soft 0.09 temp 1 loss 9.03 helix 1.22 pae 0.71 i_pae 0.67 con 4.25 i_con 3.64 plddt 0.37 ptm 0.56 i_ptm 0.15 rg 3.49\n", + "6 models [2] recycles 1 hard 0 soft 0.11 temp 1 loss 8.98 helix 1.05 pae 0.69 i_pae 0.69 con 4.17 i_con 3.98 plddt 0.40 ptm 0.55 i_ptm 0.14 rg 2.35\n", + "7 models [4] recycles 1 hard 0 soft 0.13 temp 1 loss 8.55 helix 0.79 pae 0.63 i_pae 0.62 con 3.82 i_con 3.80 plddt 0.45 ptm 0.55 i_ptm 0.15 rg 2.53\n", + "8 models [2] recycles 1 hard 0 soft 0.14 temp 1 loss 8.50 helix 1.01 pae 0.66 i_pae 0.64 con 4.03 i_con 3.70 plddt 0.43 ptm 0.56 i_ptm 0.16 rg 2.16\n", + "9 models [0] recycles 1 hard 0 soft 0.16 temp 1 loss 8.16 helix 0.89 pae 0.59 i_pae 0.62 con 3.68 i_con 3.64 plddt 0.49 ptm 0.57 i_ptm 0.17 rg 2.37\n", + "10 models [4] recycles 1 hard 0 soft 0.18 temp 1 loss 7.71 helix 0.80 pae 0.52 i_pae 0.53 con 3.18 i_con 3.53 plddt 0.58 ptm 0.57 i_ptm 0.20 rg 3.01\n", + "11 models [4] recycles 1 hard 0 soft 0.20 temp 1 loss 8.46 helix 0.95 pae 0.61 i_pae 0.61 con 3.71 i_con 3.68 plddt 0.47 ptm 0.56 i_ptm 0.17 rg 3.16\n", + "12 models [2] recycles 1 hard 0 soft 0.22 temp 1 loss 7.65 helix 0.97 pae 0.56 i_pae 0.57 con 3.19 i_con 3.52 plddt 0.56 ptm 0.57 i_ptm 0.18 rg 2.90\n", + "13 models [3] recycles 1 hard 0 soft 0.23 temp 1 loss 6.69 helix 1.05 pae 0.47 i_pae 0.50 con 2.58 i_con 3.19 plddt 0.66 ptm 0.58 i_ptm 0.25 rg 3.06\n", + "14 models [3] recycles 1 hard 0 soft 0.25 temp 1 loss 7.63 helix 1.16 pae 0.57 i_pae 0.56 con 3.45 i_con 3.40 plddt 0.50 ptm 0.58 i_ptm 0.20 rg 2.48\n", + "15 models [1] recycles 1 hard 0 soft 0.27 temp 1 loss 6.89 helix 1.11 pae 0.43 i_pae 0.47 con 2.48 i_con 3.40 plddt 0.68 ptm 0.60 i_ptm 0.28 rg 3.52\n", + "16 models [2] recycles 1 hard 0 soft 0.29 temp 1 loss 6.21 helix 1.19 pae 0.43 i_pae 0.44 con 2.43 i_con 2.93 plddt 0.70 ptm 0.61 i_ptm 0.32 rg 3.07\n", + "17 models [0] recycles 1 hard 0 soft 0.31 temp 1 loss 7.67 helix 1.13 pae 0.56 i_pae 0.56 con 3.44 i_con 3.51 plddt 0.51 ptm 0.58 i_ptm 0.23 rg 2.34\n", + "18 models [3] recycles 1 hard 0 soft 0.32 temp 1 loss 8.22 helix 1.15 pae 0.61 i_pae 0.60 con 3.57 i_con 3.74 plddt 0.48 ptm 0.57 i_ptm 0.17 rg 2.88\n", + "19 models [2] recycles 1 hard 0 soft 0.34 temp 1 loss 7.98 helix 1.17 pae 0.62 i_pae 0.60 con 3.53 i_con 3.70 plddt 0.48 ptm 0.56 i_ptm 0.17 rg 2.35\n", + "20 models [0] recycles 1 hard 0 soft 0.36 temp 1 loss 7.60 helix 1.08 pae 0.57 i_pae 0.57 con 3.33 i_con 3.56 plddt 0.51 ptm 0.57 i_ptm 0.18 rg 2.23\n", + "21 models [4] recycles 1 hard 0 soft 0.38 temp 1 loss 7.55 helix 1.09 pae 0.59 i_pae 0.55 con 3.35 i_con 3.59 plddt 0.50 ptm 0.56 i_ptm 0.16 rg 1.87\n", + "22 models [4] recycles 1 hard 0 soft 0.40 temp 1 loss 7.47 helix 1.19 pae 0.57 i_pae 0.55 con 3.26 i_con 3.41 plddt 0.53 ptm 0.57 i_ptm 0.20 rg 2.62\n", + "23 models [0] recycles 1 hard 0 soft 0.41 temp 1 loss 7.99 helix 1.38 pae 0.59 i_pae 0.58 con 3.37 i_con 3.46 plddt 0.51 ptm 0.57 i_ptm 0.20 rg 3.97\n", + "24 models [0] recycles 1 hard 0 soft 0.43 temp 1 loss 7.25 helix 1.29 pae 0.53 i_pae 0.56 con 3.13 i_con 3.48 plddt 0.58 ptm 0.57 i_ptm 0.20 rg 2.25\n", + "25 models [1] recycles 1 hard 0 soft 0.45 temp 1 loss 7.47 helix 1.26 pae 0.54 i_pae 0.53 con 3.25 i_con 3.52 plddt 0.57 ptm 0.58 i_ptm 0.20 rg 2.41\n", + "26 models [4] recycles 1 hard 0 soft 0.47 temp 1 loss 6.77 helix 1.26 pae 0.48 i_pae 0.49 con 2.79 i_con 3.30 plddt 0.65 ptm 0.59 i_ptm 0.26 rg 2.46\n", + "27 models [1] recycles 1 hard 0 soft 0.49 temp 1 loss 6.83 helix 1.25 pae 0.49 i_pae 0.51 con 2.70 i_con 3.44 plddt 0.64 ptm 0.58 i_ptm 0.22 rg 2.48\n", + "28 models [3] recycles 1 hard 0 soft 0.50 temp 1 loss 7.08 helix 1.24 pae 0.51 i_pae 0.52 con 2.92 i_con 3.49 plddt 0.60 ptm 0.58 i_ptm 0.23 rg 2.34\n", + "29 models [0] recycles 1 hard 0 soft 0.52 temp 1 loss 6.71 helix 1.35 pae 0.45 i_pae 0.51 con 2.54 i_con 3.47 plddt 0.69 ptm 0.59 i_ptm 0.22 rg 2.67\n", + "30 models [3] recycles 1 hard 0 soft 0.54 temp 1 loss 6.53 helix 1.34 pae 0.47 i_pae 0.50 con 2.54 i_con 3.42 plddt 0.66 ptm 0.58 i_ptm 0.22 rg 2.22\n", + "31 models [4] recycles 1 hard 0 soft 0.56 temp 1 loss 6.53 helix 1.44 pae 0.49 i_pae 0.53 con 2.47 i_con 3.42 plddt 0.67 ptm 0.56 i_ptm 0.19 rg 2.48\n", + "32 models [2] recycles 1 hard 0 soft 0.58 temp 1 loss 6.34 helix 1.39 pae 0.47 i_pae 0.51 con 2.41 i_con 3.34 plddt 0.71 ptm 0.57 i_ptm 0.21 rg 2.35\n", + "33 models [0] recycles 1 hard 0 soft 0.59 temp 1 loss 5.98 helix 1.60 pae 0.39 i_pae 0.41 con 2.30 i_con 3.16 plddt 0.73 ptm 0.62 i_ptm 0.32 rg 2.48\n", + "34 models [0] recycles 1 hard 0 soft 0.61 temp 1 loss 6.16 helix 1.62 pae 0.44 i_pae 0.50 con 2.32 i_con 3.36 plddt 0.72 ptm 0.59 i_ptm 0.22 rg 2.24\n", + "35 models [0] recycles 1 hard 0 soft 0.63 temp 1 loss 5.97 helix 1.73 pae 0.45 i_pae 0.48 con 2.15 i_con 3.35 plddt 0.71 ptm 0.58 i_ptm 0.21 rg 2.33\n", + "36 models [4] recycles 1 hard 0 soft 0.65 temp 1 loss 6.10 helix 1.71 pae 0.47 i_pae 0.50 con 2.27 i_con 3.25 plddt 0.70 ptm 0.57 i_ptm 0.23 rg 2.60\n", + "37 models [4] recycles 1 hard 0 soft 0.67 temp 1 loss 6.54 helix 1.38 pae 0.49 i_pae 0.50 con 2.70 i_con 3.39 plddt 0.63 ptm 0.58 i_ptm 0.20 rg 1.79\n", + "38 models [2] recycles 1 hard 0 soft 0.68 temp 1 loss 7.61 helix 1.28 pae 0.64 i_pae 0.61 con 3.56 i_con 3.73 plddt 0.45 ptm 0.56 i_ptm 0.16 rg 0.99\n", + "39 models [3] recycles 1 hard 0 soft 0.70 temp 1 loss 5.92 helix 1.50 pae 0.46 i_pae 0.47 con 2.30 i_con 3.26 plddt 0.68 ptm 0.59 i_ptm 0.24 rg 1.68\n", + "40 models [1] recycles 1 hard 0 soft 0.72 temp 1 loss 5.76 helix 1.69 pae 0.42 i_pae 0.45 con 2.25 i_con 3.22 plddt 0.70 ptm 0.62 i_ptm 0.29 rg 1.72\n", + "41 models [1] recycles 1 hard 0 soft 0.74 temp 1 loss 6.29 helix 1.47 pae 0.45 i_pae 0.46 con 2.65 i_con 3.39 plddt 0.65 ptm 0.60 i_ptm 0.25 rg 1.32\n", + "42 models [4] recycles 1 hard 0 soft 0.76 temp 1 loss 5.84 helix 1.61 pae 0.46 i_pae 0.47 con 2.31 i_con 3.23 plddt 0.68 ptm 0.58 i_ptm 0.21 rg 1.58\n", + "43 models [1] recycles 1 hard 0 soft 0.77 temp 1 loss 6.98 helix 1.39 pae 0.52 i_pae 0.52 con 3.05 i_con 3.55 plddt 0.58 ptm 0.58 i_ptm 0.20 rg 1.52\n", + "44 models [4] recycles 1 hard 0 soft 0.79 temp 1 loss 5.63 helix 1.60 pae 0.42 i_pae 0.45 con 2.22 i_con 3.10 plddt 0.72 ptm 0.61 i_ptm 0.30 rg 1.68\n", + "45 models [2] recycles 1 hard 0 soft 0.81 temp 1 loss 6.19 helix 1.57 pae 0.50 i_pae 0.54 con 2.36 i_con 3.57 plddt 0.67 ptm 0.55 i_ptm 0.16 rg 1.33\n", + "46 models [0] recycles 1 hard 0 soft 0.83 temp 1 loss 7.24 helix 1.24 pae 0.55 i_pae 0.53 con 3.20 i_con 3.51 plddt 0.50 ptm 0.59 i_ptm 0.21 rg 1.79\n", + "47 models [1] recycles 1 hard 0 soft 0.85 temp 1 loss 6.40 helix 1.55 pae 0.46 i_pae 0.48 con 2.64 i_con 3.48 plddt 0.64 ptm 0.58 i_ptm 0.20 rg 1.44\n", + "48 models [4] recycles 1 hard 0 soft 0.86 temp 1 loss 5.93 helix 1.59 pae 0.44 i_pae 0.48 con 2.32 i_con 3.37 plddt 0.69 ptm 0.58 i_ptm 0.20 rg 1.41\n", + "49 models [4] recycles 1 hard 0 soft 0.88 temp 1 loss 7.69 helix 1.30 pae 0.62 i_pae 0.62 con 3.32 i_con 3.96 plddt 0.48 ptm 0.55 i_ptm 0.14 rg 1.32\n", + "50 models [2] recycles 1 hard 0 soft 0.90 temp 1 loss 6.51 helix 1.40 pae 0.53 i_pae 0.60 con 2.60 i_con 3.68 plddt 0.64 ptm 0.55 i_ptm 0.15 rg 1.00\n", + "Initial trajectory pLDDT good, continuing: 0.72\n", "Stage 1: Additional Logits Optimisation\n", - "51 models [2] recycles 1 hard 0 soft 0.04 temp 1 loss 9.91 helix 1.49 pae 0.83 i_pae 0.81 con 4.50 i_con 4.21 plddt 0.27 ptm 0.44 i_ptm 0.10 rg 3.70\n", - "52 models [4] recycles 1 hard 0 soft 0.08 temp 1 loss 5.24 helix 1.03 pae 0.26 i_pae 0.24 con 1.99 i_con 2.58 plddt 0.82 ptm 0.72 i_ptm 0.59 rg 2.68\n", - "53 models [0] recycles 1 hard 0 soft 0.12 temp 1 loss 6.77 helix 0.98 pae 0.43 i_pae 0.54 con 2.42 i_con 3.63 plddt 0.71 ptm 0.52 i_ptm 0.23 rg 2.40\n", - "54 models [1] recycles 1 hard 0 soft 0.16 temp 1 loss 3.96 helix 1.25 pae 0.19 i_pae 0.15 con 1.70 i_con 1.74 plddt 0.89 ptm 0.85 i_ptm 0.80 rg 2.62\n", - "55 models [0] recycles 1 hard 0 soft 0.20 temp 1 loss 3.90 helix 1.24 pae 0.18 i_pae 0.14 con 1.69 i_con 1.69 plddt 0.90 ptm 0.86 i_ptm 0.83 rg 2.63\n", - "56 models [3] recycles 1 hard 0 soft 0.24 temp 1 loss 3.86 helix 1.31 pae 0.18 i_pae 0.14 con 1.59 i_con 1.78 plddt 0.91 ptm 0.86 i_ptm 0.81 rg 2.59\n", - "57 models [2] recycles 1 hard 0 soft 0.28 temp 1 loss 4.32 helix 1.19 pae 0.20 i_pae 0.17 con 1.85 i_con 1.92 plddt 0.88 ptm 0.83 i_ptm 0.77 rg 2.64\n", - "58 models [2] recycles 1 hard 0 soft 0.32 temp 1 loss 4.45 helix 1.10 pae 0.22 i_pae 0.17 con 2.09 i_con 1.72 plddt 0.84 ptm 0.82 i_ptm 0.81 rg 2.78\n", - "59 models [1] recycles 1 hard 0 soft 0.36 temp 1 loss 3.75 helix 1.24 pae 0.18 i_pae 0.14 con 1.72 i_con 1.54 plddt 0.89 ptm 0.87 i_ptm 0.85 rg 2.52\n", - "60 models [1] recycles 1 hard 0 soft 0.40 temp 1 loss 3.54 helix 1.41 pae 0.17 i_pae 0.13 con 1.58 i_con 1.54 plddt 0.91 ptm 0.88 i_ptm 0.86 rg 2.49\n", - "61 models [2] recycles 1 hard 0 soft 0.44 temp 1 loss 3.40 helix 1.41 pae 0.15 i_pae 0.12 con 1.47 i_con 1.51 plddt 0.93 ptm 0.89 i_ptm 0.85 rg 2.53\n", - "62 models [0] recycles 1 hard 0 soft 0.48 temp 1 loss 3.55 helix 1.35 pae 0.16 i_pae 0.13 con 1.54 i_con 1.55 plddt 0.92 ptm 0.87 i_ptm 0.84 rg 2.56\n", - "63 models [1] recycles 1 hard 0 soft 0.52 temp 1 loss 4.57 helix 1.28 pae 0.21 i_pae 0.16 con 2.02 i_con 2.04 plddt 0.84 ptm 0.81 i_ptm 0.75 rg 2.57\n", - "64 models [4] recycles 1 hard 0 soft 0.56 temp 1 loss 3.73 helix 1.30 pae 0.16 i_pae 0.13 con 1.59 i_con 1.66 plddt 0.92 ptm 0.88 i_ptm 0.83 rg 2.59\n", - "65 models [1] recycles 1 hard 0 soft 0.60 temp 1 loss 3.35 helix 1.43 pae 0.15 i_pae 0.12 con 1.50 i_con 1.46 plddt 0.93 ptm 0.89 i_ptm 0.86 rg 2.43\n", - "66 models [0] recycles 1 hard 0 soft 0.64 temp 1 loss 3.48 helix 1.39 pae 0.15 i_pae 0.12 con 1.54 i_con 1.52 plddt 0.92 ptm 0.89 i_ptm 0.86 rg 2.49\n", - "67 models [1] recycles 1 hard 0 soft 0.68 temp 1 loss 6.97 helix 0.98 pae 0.47 i_pae 0.59 con 2.39 i_con 3.80 plddt 0.68 ptm 0.49 i_ptm 0.17 rg 2.52\n", - "68 models [0] recycles 1 hard 0 soft 0.72 temp 1 loss 3.80 helix 1.36 pae 0.17 i_pae 0.13 con 1.62 i_con 1.72 plddt 0.91 ptm 0.86 i_ptm 0.81 rg 2.55\n", - "69 models [3] recycles 1 hard 0 soft 0.76 temp 1 loss 5.87 helix 1.10 pae 0.36 i_pae 0.48 con 1.91 i_con 3.29 plddt 0.81 ptm 0.58 i_ptm 0.24 rg 2.53\n", - "70 models [0] recycles 1 hard 0 soft 0.80 temp 1 loss 6.03 helix 1.27 pae 0.38 i_pae 0.58 con 1.74 i_con 3.67 plddt 0.84 ptm 0.57 i_ptm 0.20 rg 2.47\n", - "71 models [4] recycles 1 hard 0 soft 0.84 temp 1 loss 7.07 helix 0.99 pae 0.50 i_pae 0.62 con 2.47 i_con 3.78 plddt 0.65 ptm 0.48 i_ptm 0.16 rg 2.62\n", - "72 models [4] recycles 1 hard 0 soft 0.88 temp 1 loss 6.61 helix 0.92 pae 0.44 i_pae 0.53 con 2.34 i_con 3.48 plddt 0.71 ptm 0.51 i_ptm 0.23 rg 2.58\n", - "73 models [4] recycles 1 hard 0 soft 0.92 temp 1 loss 6.26 helix 0.96 pae 0.41 i_pae 0.45 con 2.37 i_con 3.13 plddt 0.70 ptm 0.56 i_ptm 0.32 rg 2.59\n", - "74 models [3] recycles 1 hard 0 soft 0.96 temp 1 loss 6.44 helix 0.96 pae 0.41 i_pae 0.48 con 2.32 i_con 3.31 plddt 0.71 ptm 0.54 i_ptm 0.29 rg 2.74\n", - "75 models [0] recycles 1 hard 0 soft 1 temp 1 loss 4.89 helix 1.08 pae 0.23 i_pae 0.20 con 1.95 i_con 2.28 plddt 0.84 ptm 0.78 i_ptm 0.69 rg 2.82\n", - "Optimised logit trajectory pLDDT: 0.93\n", + "51 models [2] recycles 1 hard 0 soft 0.04 temp 1 loss 5.73 helix 1.60 pae 0.42 i_pae 0.44 con 2.22 i_con 3.29 plddt 0.73 ptm 0.59 i_ptm 0.24 rg 1.43\n", + "52 models [0] recycles 1 hard 0 soft 0.08 temp 1 loss 6.96 helix 1.37 pae 0.55 i_pae 0.54 con 3.29 i_con 3.46 plddt 0.52 ptm 0.58 i_ptm 0.22 rg 0.87\n", + "53 models [3] recycles 1 hard 0 soft 0.12 temp 1 loss 5.67 helix 1.53 pae 0.41 i_pae 0.46 con 2.19 i_con 3.21 plddt 0.71 ptm 0.60 i_ptm 0.25 rg 1.52\n", + "54 models [1] recycles 1 hard 0 soft 0.16 temp 1 loss 6.75 helix 1.37 pae 0.50 i_pae 0.49 con 3.06 i_con 3.34 plddt 0.58 ptm 0.61 i_ptm 0.27 rg 1.42\n", + "55 models [0] recycles 1 hard 0 soft 0.20 temp 1 loss 5.91 helix 1.42 pae 0.43 i_pae 0.45 con 2.34 i_con 3.27 plddt 0.67 ptm 0.60 i_ptm 0.25 rg 1.48\n", + "56 models [0] recycles 1 hard 0 soft 0.24 temp 1 loss 5.40 helix 1.54 pae 0.35 i_pae 0.37 con 2.21 i_con 2.97 plddt 0.73 ptm 0.67 i_ptm 0.42 rg 1.47\n", + "57 models [0] recycles 1 hard 0 soft 0.28 temp 1 loss 5.45 helix 1.83 pae 0.42 i_pae 0.49 con 2.04 i_con 3.32 plddt 0.73 ptm 0.57 i_ptm 0.20 rg 1.19\n", + "58 models [2] recycles 1 hard 0 soft 0.32 temp 1 loss 5.16 helix 1.67 pae 0.34 i_pae 0.32 con 2.22 i_con 2.71 plddt 0.75 ptm 0.68 i_ptm 0.53 rg 1.70\n", + "59 models [0] recycles 1 hard 0 soft 0.36 temp 1 loss 4.46 helix 1.75 pae 0.25 i_pae 0.22 con 2.08 i_con 2.27 plddt 0.79 ptm 0.78 i_ptm 0.71 rg 1.57\n", + "60 models [3] recycles 1 hard 0 soft 0.40 temp 1 loss 3.99 helix 1.89 pae 0.23 i_pae 0.18 con 2.01 i_con 1.94 plddt 0.82 ptm 0.80 i_ptm 0.75 rg 1.56\n", + "61 models [3] recycles 1 hard 0 soft 0.44 temp 1 loss 3.80 helix 1.89 pae 0.23 i_pae 0.16 con 2.03 i_con 1.70 plddt 0.82 ptm 0.82 i_ptm 0.79 rg 1.68\n", + "62 models [0] recycles 1 hard 0 soft 0.48 temp 1 loss 3.74 helix 1.94 pae 0.22 i_pae 0.16 con 2.01 i_con 1.74 plddt 0.83 ptm 0.82 i_ptm 0.79 rg 1.48\n", + "63 models [1] recycles 1 hard 0 soft 0.52 temp 1 loss 4.38 helix 2.06 pae 0.25 i_pae 0.22 con 1.99 i_con 2.41 plddt 0.81 ptm 0.76 i_ptm 0.61 rg 1.47\n", + "64 models [1] recycles 1 hard 0 soft 0.56 temp 1 loss 3.46 helix 2.04 pae 0.20 i_pae 0.15 con 1.91 i_con 1.58 plddt 0.84 ptm 0.83 i_ptm 0.81 rg 1.53\n", + "65 models [4] recycles 1 hard 0 soft 0.60 temp 1 loss 5.35 helix 1.88 pae 0.39 i_pae 0.41 con 2.04 i_con 3.22 plddt 0.75 ptm 0.61 i_ptm 0.28 rg 1.34\n", + "66 models [3] recycles 1 hard 0 soft 0.64 temp 1 loss 5.48 helix 1.94 pae 0.41 i_pae 0.47 con 2.04 i_con 3.34 plddt 0.75 ptm 0.58 i_ptm 0.21 rg 1.35\n", + "67 models [3] recycles 1 hard 0 soft 0.68 temp 1 loss 3.50 helix 1.94 pae 0.21 i_pae 0.15 con 1.97 i_con 1.51 plddt 0.84 ptm 0.83 i_ptm 0.81 rg 1.61\n", + "68 models [0] recycles 1 hard 0 soft 0.72 temp 1 loss 3.43 helix 2.13 pae 0.20 i_pae 0.14 con 1.96 i_con 1.51 plddt 0.85 ptm 0.84 i_ptm 0.81 rg 1.61\n", + "69 models [2] recycles 1 hard 0 soft 0.76 temp 1 loss 4.22 helix 1.78 pae 0.23 i_pae 0.19 con 2.06 i_con 2.11 plddt 0.82 ptm 0.79 i_ptm 0.71 rg 1.50\n", + "70 models [1] recycles 1 hard 0 soft 0.80 temp 1 loss 5.39 helix 2.00 pae 0.39 i_pae 0.44 con 2.00 i_con 3.31 plddt 0.77 ptm 0.59 i_ptm 0.23 rg 1.40\n", + "71 models [2] recycles 1 hard 0 soft 0.84 temp 1 loss 5.31 helix 1.90 pae 0.42 i_pae 0.46 con 1.97 i_con 3.29 plddt 0.75 ptm 0.58 i_ptm 0.20 rg 1.15\n", + "72 models [2] recycles 1 hard 0 soft 0.88 temp 1 loss 7.29 helix 1.32 pae 0.57 i_pae 0.54 con 3.17 i_con 3.62 plddt 0.52 ptm 0.57 i_ptm 0.18 rg 1.72\n", + "73 models [2] recycles 1 hard 0 soft 0.92 temp 1 loss 7.40 helix 1.17 pae 0.56 i_pae 0.55 con 3.12 i_con 3.63 plddt 0.55 ptm 0.57 i_ptm 0.20 rg 2.09\n", + "74 models [0] recycles 1 hard 0 soft 0.96 temp 1 loss 7.05 helix 1.18 pae 0.52 i_pae 0.52 con 3.06 i_con 3.63 plddt 0.56 ptm 0.59 i_ptm 0.23 rg 1.23\n", + "75 models [4] recycles 1 hard 0 soft 1 temp 1 loss 6.81 helix 1.09 pae 0.48 i_pae 0.42 con 3.15 i_con 3.19 plddt 0.56 ptm 0.64 i_ptm 0.36 rg 1.62\n", + "Optimised logit trajectory pLDDT: 0.85\n", "Stage 2: Softmax Optimisation\n", - "76 models [0] recycles 1 hard 0 soft 1 temp 0.96 loss 4.56 helix 1.05 pae 0.22 i_pae 0.20 con 1.91 i_con 2.01 plddt 0.84 ptm 0.81 i_ptm 0.74 rg 2.73\n", - "77 models [1] recycles 1 hard 0 soft 1 temp 0.91 loss 6.56 helix 0.99 pae 0.44 i_pae 0.51 con 2.34 i_con 3.40 plddt 0.70 ptm 0.53 i_ptm 0.24 rg 2.72\n", - "78 models [3] recycles 1 hard 0 soft 1 temp 0.87 loss 6.46 helix 0.99 pae 0.41 i_pae 0.49 con 2.34 i_con 3.35 plddt 0.71 ptm 0.55 i_ptm 0.28 rg 2.65\n", - "79 models [2] recycles 1 hard 0 soft 1 temp 0.83 loss 7.08 helix 0.95 pae 0.49 i_pae 0.65 con 2.32 i_con 3.95 plddt 0.69 ptm 0.47 i_ptm 0.15 rg 2.52\n", - "80 models [4] recycles 1 hard 0 soft 1 temp 0.79 loss 6.88 helix 0.94 pae 0.48 i_pae 0.58 con 2.38 i_con 3.68 plddt 0.69 ptm 0.49 i_ptm 0.19 rg 2.59\n", - "81 models [1] recycles 1 hard 0 soft 1 temp 0.75 loss 6.61 helix 0.95 pae 0.42 i_pae 0.52 con 2.26 i_con 3.54 plddt 0.72 ptm 0.53 i_ptm 0.24 rg 2.70\n", - "82 models [1] recycles 1 hard 0 soft 1 temp 0.72 loss 6.67 helix 0.93 pae 0.42 i_pae 0.54 con 2.26 i_con 3.55 plddt 0.73 ptm 0.53 i_ptm 0.20 rg 2.81\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubprocess_command\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:503\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Popen(\u001b[38;5;241m*\u001b[39mpopenargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mas\u001b[39;00m process:\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 503\u001b[0m stdout, stderr \u001b[38;5;241m=\u001b[39m \u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommunicate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TimeoutExpired \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 505\u001b[0m process\u001b[38;5;241m.\u001b[39mkill()\n", - "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1141\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m 1139\u001b[0m stderr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1141\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1204\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1202\u001b[0m endtime \u001b[38;5;241m=\u001b[39m _time() \u001b[38;5;241m+\u001b[39m timeout\n\u001b[1;32m 1203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1205\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m 1209\u001b[0m \u001b[38;5;66;03m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1938\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1936\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1937\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m \u001b[38;5;66;03m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1938\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1939\u001b[0m \u001b[38;5;66;03m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m 1940\u001b[0m \u001b[38;5;66;03m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;66;03m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m 1942\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pid \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid:\n", - "File \u001b[0;32m~/mnt/sds-hd/sd25g005/bindcraft/lib/python3.10/subprocess.py:1896\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m 1895\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1896\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitpid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait_flags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1897\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mChildProcessError\u001b[39;00m:\n\u001b[1;32m 1898\u001b[0m \u001b[38;5;66;03m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m 1899\u001b[0m \u001b[38;5;66;03m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m 1900\u001b[0m \u001b[38;5;66;03m# process. This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m 1901\u001b[0m pid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "76 models [2] recycles 1 hard 0 soft 1 temp 0.96 loss 7.27 helix 1.27 pae 0.61 i_pae 0.59 con 3.35 i_con 3.63 plddt 0.49 ptm 0.57 i_ptm 0.19 rg 0.94\n", + "77 models [1] recycles 1 hard 0 soft 1 temp 0.91 loss 7.15 helix 1.30 pae 0.56 i_pae 0.49 con 3.60 i_con 3.22 plddt 0.52 ptm 0.62 i_ptm 0.37 rg 1.20\n", + "78 models [0] recycles 1 hard 0 soft 1 temp 0.87 loss 6.83 helix 1.34 pae 0.47 i_pae 0.43 con 3.16 i_con 3.26 plddt 0.59 ptm 0.65 i_ptm 0.36 rg 1.70\n", + "79 models [3] recycles 1 hard 0 soft 1 temp 0.83 loss 6.64 helix 1.20 pae 0.45 i_pae 0.43 con 2.94 i_con 3.25 plddt 0.59 ptm 0.64 i_ptm 0.33 rg 1.70\n", + "80 models [3] recycles 1 hard 0 soft 1 temp 0.79 loss 4.71 helix 1.67 pae 0.28 i_pae 0.22 con 2.23 i_con 2.30 plddt 0.78 ptm 0.78 i_ptm 0.68 rg 1.71\n", + "81 models [1] recycles 1 hard 0 soft 1 temp 0.75 loss 8.05 helix 1.43 pae 0.62 i_pae 0.58 con 3.76 i_con 3.61 plddt 0.47 ptm 0.58 i_ptm 0.20 rg 2.36\n", + "82 models [2] recycles 1 hard 0 soft 1 temp 0.72 loss 5.04 helix 1.76 pae 0.36 i_pae 0.34 con 2.19 i_con 2.71 plddt 0.76 ptm 0.67 i_ptm 0.45 rg 1.47\n", + "83 models [3] recycles 1 hard 0 soft 1 temp 0.68 loss 5.99 helix 1.34 pae 0.40 i_pae 0.36 con 2.87 i_con 2.90 plddt 0.64 ptm 0.68 i_ptm 0.44 rg 1.17\n", + "84 models [0] recycles 1 hard 0 soft 1 temp 0.64 loss 4.38 helix 1.69 pae 0.26 i_pae 0.20 con 2.15 i_con 2.18 plddt 0.78 ptm 0.79 i_ptm 0.71 rg 1.34\n", + "85 models [3] recycles 1 hard 0 soft 1 temp 0.61 loss 4.80 helix 1.68 pae 0.28 i_pae 0.23 con 2.28 i_con 2.27 plddt 0.77 ptm 0.77 i_ptm 0.69 rg 1.91\n", + "86 models [1] recycles 1 hard 0 soft 1 temp 0.58 loss 3.92 helix 1.87 pae 0.25 i_pae 0.18 con 2.10 i_con 1.85 plddt 0.79 ptm 0.81 i_ptm 0.76 rg 1.29\n", + "87 models [0] recycles 1 hard 0 soft 1 temp 0.54 loss 4.26 helix 1.83 pae 0.24 i_pae 0.21 con 2.05 i_con 2.27 plddt 0.81 ptm 0.78 i_ptm 0.66 rg 1.12\n", + "88 models [3] recycles 1 hard 0 soft 1 temp 0.51 loss 4.50 helix 1.97 pae 0.26 i_pae 0.20 con 2.14 i_con 2.44 plddt 0.79 ptm 0.77 i_ptm 0.65 rg 1.17\n", + "89 models [3] recycles 1 hard 0 soft 1 temp 0.48 loss 4.32 helix 1.86 pae 0.23 i_pae 0.17 con 2.19 i_con 2.15 plddt 0.80 ptm 0.81 i_ptm 0.73 rg 1.31\n", + "90 models [4] recycles 1 hard 0 soft 1 temp 0.45 loss 4.22 helix 1.93 pae 0.25 i_pae 0.18 con 2.07 i_con 2.21 plddt 0.81 ptm 0.79 i_ptm 0.72 rg 1.19\n", + "91 models [1] recycles 1 hard 0 soft 1 temp 0.42 loss 4.25 helix 1.86 pae 0.21 i_pae 0.16 con 2.06 i_con 2.18 plddt 0.83 ptm 0.81 i_ptm 0.72 rg 1.42\n", + "92 models [4] recycles 1 hard 0 soft 1 temp 0.39 loss 4.60 helix 2.04 pae 0.22 i_pae 0.18 con 2.18 i_con 2.41 plddt 0.82 ptm 0.79 i_ptm 0.68 rg 1.59\n", + "93 models [3] recycles 1 hard 0 soft 1 temp 0.37 loss 5.63 helix 1.79 pae 0.40 i_pae 0.45 con 2.19 i_con 3.32 plddt 0.73 ptm 0.59 i_ptm 0.23 rg 1.31\n", + "94 models [4] recycles 1 hard 0 soft 1 temp 0.34 loss 4.15 helix 2.05 pae 0.21 i_pae 0.18 con 1.89 i_con 2.30 plddt 0.84 ptm 0.77 i_ptm 0.67 rg 1.47\n", + "95 models [2] recycles 1 hard 0 soft 1 temp 0.32 loss 4.51 helix 1.80 pae 0.28 i_pae 0.23 con 2.14 i_con 2.39 plddt 0.80 ptm 0.75 i_ptm 0.65 rg 1.18\n", + "96 models [3] recycles 1 hard 0 soft 1 temp 0.29 loss 4.40 helix 1.91 pae 0.23 i_pae 0.19 con 1.96 i_con 2.44 plddt 0.83 ptm 0.78 i_ptm 0.66 rg 1.45\n", + "97 models [0] recycles 1 hard 0 soft 1 temp 0.27 loss 3.61 helix 2.21 pae 0.17 i_pae 0.14 con 1.72 i_con 2.03 plddt 0.88 ptm 0.81 i_ptm 0.75 rg 1.38\n", + "98 models [1] recycles 1 hard 0 soft 1 temp 0.25 loss 4.13 helix 2.06 pae 0.20 i_pae 0.17 con 1.84 i_con 2.37 plddt 0.85 ptm 0.78 i_ptm 0.67 rg 1.37\n", + "99 models [4] recycles 1 hard 0 soft 1 temp 0.23 loss 4.33 helix 2.17 pae 0.21 i_pae 0.19 con 1.89 i_con 2.52 plddt 0.86 ptm 0.77 i_ptm 0.65 rg 1.46\n", + "100 models [1] recycles 1 hard 0 soft 1 temp 0.21 loss 3.87 helix 2.14 pae 0.18 i_pae 0.14 con 1.79 i_con 2.21 plddt 0.87 ptm 0.81 i_ptm 0.73 rg 1.33\n", + "101 models [3] recycles 1 hard 0 soft 1 temp 0.19 loss 3.98 helix 1.98 pae 0.22 i_pae 0.18 con 2.00 i_con 1.99 plddt 0.83 ptm 0.79 i_ptm 0.74 rg 1.48\n", + "102 models [0] recycles 1 hard 0 soft 1 temp 0.17 loss 4.79 helix 1.87 pae 0.25 i_pae 0.23 con 2.16 i_con 2.59 plddt 0.79 ptm 0.75 i_ptm 0.60 rg 1.44\n", + "103 models [1] recycles 1 hard 0 soft 1 temp 0.15 loss 5.27 helix 2.19 pae 0.35 i_pae 0.41 con 1.96 i_con 3.32 plddt 0.78 ptm 0.60 i_ptm 0.26 rg 1.35\n", + "104 models [3] recycles 1 hard 0 soft 1 temp 0.14 loss 5.40 helix 1.89 pae 0.39 i_pae 0.45 con 1.94 i_con 3.40 plddt 0.78 ptm 0.58 i_ptm 0.20 rg 1.21\n", + "105 models [1] recycles 1 hard 0 soft 1 temp 0.12 loss 3.79 helix 2.04 pae 0.20 i_pae 0.16 con 1.99 i_con 1.87 plddt 0.84 ptm 0.81 i_ptm 0.78 rg 1.40\n", + "106 models [3] recycles 1 hard 0 soft 1 temp 0.11 loss 4.32 helix 2.00 pae 0.23 i_pae 0.21 con 1.94 i_con 2.42 plddt 0.84 ptm 0.75 i_ptm 0.63 rg 1.35\n", + "107 models [4] recycles 1 hard 0 soft 1 temp 0.09 loss 4.33 helix 1.98 pae 0.22 i_pae 0.18 con 2.08 i_con 2.25 plddt 0.83 ptm 0.79 i_ptm 0.74 rg 1.52\n", + "108 models [1] recycles 1 hard 0 soft 1 temp 0.08 loss 4.28 helix 1.81 pae 0.22 i_pae 0.17 con 2.09 i_con 2.15 plddt 0.81 ptm 0.80 i_ptm 0.74 rg 1.46\n", + "109 models [1] recycles 1 hard 0 soft 1 temp 0.07 loss 4.38 helix 1.76 pae 0.23 i_pae 0.18 con 2.28 i_con 2.04 plddt 0.79 ptm 0.80 i_ptm 0.78 rg 1.49\n", + "110 models [4] recycles 1 hard 0 soft 1 temp 0.06 loss 6.83 helix 1.52 pae 0.52 i_pae 0.54 con 2.82 i_con 3.72 plddt 0.60 ptm 0.57 i_ptm 0.18 rg 1.34\n", + "111 models [0] recycles 1 hard 0 soft 1 temp 0.05 loss 3.93 helix 2.11 pae 0.18 i_pae 0.13 con 1.92 i_con 2.13 plddt 0.86 ptm 0.84 i_ptm 0.77 rg 1.37\n", + "112 models [4] recycles 1 hard 0 soft 1 temp 0.04 loss 6.19 helix 1.57 pae 0.44 i_pae 0.43 con 2.73 i_con 3.28 plddt 0.65 ptm 0.62 i_ptm 0.29 rg 1.21\n", + "113 models [4] recycles 1 hard 0 soft 1 temp 0.03 loss 6.47 helix 1.56 pae 0.46 i_pae 0.46 con 2.84 i_con 3.43 plddt 0.63 ptm 0.59 i_ptm 0.24 rg 1.19\n", + "114 models [3] recycles 1 hard 0 soft 1 temp 0.03 loss 4.09 helix 2.01 pae 0.21 i_pae 0.18 con 2.02 i_con 2.12 plddt 0.84 ptm 0.79 i_ptm 0.74 rg 1.41\n", + "115 models [4] recycles 1 hard 0 soft 1 temp 0.02 loss 4.96 helix 1.73 pae 0.25 i_pae 0.20 con 2.40 i_con 2.49 plddt 0.78 ptm 0.77 i_ptm 0.67 rg 1.46\n", + "116 models [4] recycles 1 hard 0 soft 1 temp 0.02 loss 5.75 helix 1.55 pae 0.34 i_pae 0.30 con 2.73 i_con 2.87 plddt 0.69 ptm 0.71 i_ptm 0.53 rg 1.33\n", + "117 models [3] recycles 1 hard 0 soft 1 temp 0.01 loss 5.89 helix 1.67 pae 0.41 i_pae 0.44 con 2.42 i_con 3.34 plddt 0.71 ptm 0.61 i_ptm 0.27 rg 1.19\n", + "118 models [1] recycles 1 hard 0 soft 1 temp 0.01 loss 4.11 helix 1.94 pae 0.22 i_pae 0.18 con 2.06 i_con 2.08 plddt 0.82 ptm 0.79 i_ptm 0.71 rg 1.37\n", + "119 models [3] recycles 1 hard 0 soft 1 temp 0.01 loss 4.23 helix 1.80 pae 0.23 i_pae 0.17 con 2.18 i_con 2.01 plddt 0.82 ptm 0.80 i_ptm 0.75 rg 1.49\n", + "120 models [2] recycles 1 hard 0 soft 1 temp 0.01 loss 5.96 helix 1.63 pae 0.38 i_pae 0.39 con 2.52 i_con 3.26 plddt 0.72 ptm 0.64 i_ptm 0.34 rg 1.41\n", + "Softmax trajectory pLDDT good, continuing: 0.88\n", + "Stage 3: One-hot Optimisation\n", + "121 models [4] recycles 1 hard 1 soft 1 temp 0.01 loss 5.53 helix 1.89 pae 0.39 i_pae 0.43 con 2.16 i_con 3.23 plddt 0.69 ptm 0.59 i_ptm 0.24 rg 1.45\n", + "122 models [0] recycles 1 hard 1 soft 1 temp 0.01 loss 3.89 helix 1.82 pae 0.20 i_pae 0.16 con 2.04 i_con 1.80 plddt 0.81 ptm 0.82 i_ptm 0.80 rg 1.58\n", + "123 models [2] recycles 1 hard 1 soft 1 temp 0.01 loss 5.77 helix 1.82 pae 0.42 i_pae 0.47 con 2.17 i_con 3.43 plddt 0.71 ptm 0.58 i_ptm 0.20 rg 1.44\n", + "124 models [0] recycles 1 hard 1 soft 1 temp 0.01 loss 3.95 helix 1.78 pae 0.21 i_pae 0.16 con 2.09 i_con 1.78 plddt 0.80 ptm 0.82 i_ptm 0.81 rg 1.59\n", + "125 models [2] recycles 1 hard 1 soft 1 temp 0.01 loss 5.75 helix 1.74 pae 0.42 i_pae 0.48 con 2.12 i_con 3.44 plddt 0.71 ptm 0.58 i_ptm 0.19 rg 1.45\n", + "One-hot trajectory pLDDT good, continuing: 0.81\n", + "Stage 4: PSSM Semigreedy Optimisation\n", + "Running semigreedy optimization...\n", + "126 models [1] recycles 1 hard 1 soft 0 temp 1 loss 6.48 helix 2.23 pae 0.36 i_pae 0.28 con 2.87 i_con 1.67 plddt 0.72 ptm 0.78 i_ptm 0.81 rg 8.01\n", + "127 models [0] recycles 1 hard 1 soft 0 temp 1 loss 6.48 helix 2.15 pae 0.35 i_pae 0.28 con 2.90 i_con 1.70 plddt 0.73 ptm 0.78 i_ptm 0.82 rg 7.73\n", + "128 models [4] recycles 1 hard 1 soft 0 temp 1 loss 6.59 helix 2.25 pae 0.34 i_pae 0.26 con 2.78 i_con 1.90 plddt 0.73 ptm 0.78 i_ptm 0.78 rg 7.95\n", + "129 models [0] recycles 1 hard 1 soft 0 temp 1 loss 7.75 helix 2.14 pae 0.38 i_pae 0.31 con 2.95 i_con 2.23 plddt 0.71 ptm 0.75 i_ptm 0.73 rg 9.92\n", + "130 models [3] recycles 1 hard 1 soft 0 temp 1 loss 8.56 helix 2.05 pae 0.49 i_pae 0.49 con 2.86 i_con 3.29 plddt 0.63 ptm 0.62 i_ptm 0.31 rg 9.02\n", + "131 models [4] recycles 1 hard 1 soft 0 temp 1 loss 8.68 helix 1.91 pae 0.58 i_pae 0.54 con 3.52 i_con 3.55 plddt 0.49 ptm 0.58 i_ptm 0.20 rg 6.03\n", + "132 models [4] recycles 1 hard 1 soft 0 temp 1 loss 9.55 helix 1.96 pae 0.68 i_pae 0.64 con 3.92 i_con 3.55 plddt 0.40 ptm 0.56 i_ptm 0.14 rg 7.43\n", + "133 models [4] recycles 1 hard 1 soft 0 temp 1 loss 9.71 helix 1.98 pae 0.68 i_pae 0.64 con 3.92 i_con 3.52 plddt 0.39 ptm 0.57 i_ptm 0.16 rg 8.07\n", + "134 models [4] recycles 1 hard 1 soft 0 temp 1 loss 9.65 helix 2.01 pae 0.68 i_pae 0.64 con 3.94 i_con 3.50 plddt 0.39 ptm 0.57 i_ptm 0.17 rg 7.91\n", + "135 models [2] recycles 1 hard 1 soft 0 temp 1 loss 9.16 helix 2.07 pae 0.70 i_pae 0.69 con 3.88 i_con 3.94 plddt 0.38 ptm 0.56 i_ptm 0.13 rg 4.98\n", + "136 models [2] recycles 1 hard 1 soft 0 temp 1 loss 9.36 helix 2.10 pae 0.72 i_pae 0.71 con 3.91 i_con 4.09 plddt 0.38 ptm 0.55 i_ptm 0.12 rg 5.06\n", + "137 models [1] recycles 1 hard 1 soft 0 temp 1 loss 10.19 helix 2.23 pae 0.71 i_pae 0.69 con 3.96 i_con 3.79 plddt 0.40 ptm 0.56 i_ptm 0.15 rg 8.86\n", + "138 models [4] recycles 1 hard 1 soft 0 temp 1 loss 9.11 helix 2.15 pae 0.71 i_pae 0.68 con 3.96 i_con 3.90 plddt 0.35 ptm 0.56 i_ptm 0.14 rg 4.76\n", + "139 models [2] recycles 1 hard 1 soft 0 temp 1 loss 9.39 helix 2.10 pae 0.73 i_pae 0.71 con 4.07 i_con 4.03 plddt 0.33 ptm 0.55 i_ptm 0.12 rg 4.82\n", + "140 models [0] recycles 1 hard 1 soft 0 temp 1 loss 10.18 helix 2.08 pae 0.69 i_pae 0.68 con 4.01 i_con 3.98 plddt 0.37 ptm 0.56 i_ptm 0.13 rg 7.89\n", + "Trajectory successful, final pLDDT: 0.81\n", + "Starting trajectory took: 0 hours, 8 minutes, 11 seconds\n", + "\n", + "Fixing interface residues: B24,B27,B28,B29,B31,B32,B35,B39,B46,B60,B61,B62,B63,B65,B66,B67,B69,B82,B83,B88\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn1, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn2, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn3, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn4, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn5, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn6, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn7, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn8, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn9, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn10, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn11, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn12, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn13, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn14, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn15, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn16, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn17, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn18, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn19, skipping interface scoring\n", + "Base AF2 filters not passed for PDL1_l89_s699438_mpnn20, skipping interface scoring\n", + "No accepted MPNN designs found for this trajectory.\n", + "\n", + "Design and validation of trajectory PDL1_l89_s699438 took: 0 hours, 1 minutes, 17 seconds\n", + "Starting trajectory: PDL1_l147_s368230\n", + "Stage 1: Test Logits\n" ] } ], @@ -295,17 +362,24 @@ "subprocess.run(subprocess_command)" ] }, + { + "cell_type": "markdown", + "id": "19f3d7bf-aa4f-4915-8dc0-2d34ffbc7433", + "metadata": {}, + "source": [ + "You find the results in your `WORKING_DIR` on the right. Note that it takes quite a long time to complete the run, see the discussion [here[(runtime https://github.com/martinpacesa/BindCraft/issues/83) to get an idea.\n", + "\n", + "## Known issues\n", + "Currently we have to deactivate the trajectory gifs via ffmpeg. See https://github.com/martinpacesa/BindCraft/issues/20 " + ] + }, { "cell_type": "code", "execution_count": null, - "id": "1b1c8a12-b72b-4fab-b677-652948fad541", + "id": "40eb1eb2-6b70-4454-a2cd-d9767b8c9042", "metadata": {}, "outputs": [], - "source": [ - "# Known issues\n", - "#ffmpeg https://github.com/martinpacesa/BindCraft/issues/20 \n", - "#runtime https://github.com/martinpacesa/BindCraft/issues/83" - ] + "source": [] } ], "metadata": { From 8201ea48540c01b498e0aeb3ee8746500edae1ed Mon Sep 17 00:00:00 2001 From: ChristineSchulz Date: Thu, 22 Jan 2026 16:40:49 +0100 Subject: [PATCH 11/28] fixed AFold CI --- notebooks/AFold_Alignment_CPU.ipynb | 152 ++++++++---- notebooks/AFold_Confidence_Levels.ipynb | 303 +++++++++++++++--------- notebooks/AFold_Diffusion_GPU.ipynb | 97 ++++++-- notebooks/afold_test/input.json | 3 +- notebooks/afold_test/run.sh | 7 +- notebooks/afold_test/run_gpu.sh | 6 +- 6 files changed, 387 insertions(+), 181 deletions(-) diff --git a/notebooks/AFold_Alignment_CPU.ipynb b/notebooks/AFold_Alignment_CPU.ipynb index 3a957b6..b019053 100644 --- a/notebooks/AFold_Alignment_CPU.ipynb +++ b/notebooks/AFold_Alignment_CPU.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "0", + "id": "b4680197-02df-4622-ae23-68fac60c5e08", "metadata": {}, "source": [ "## Preparation\n", @@ -10,17 +10,16 @@ "Before we can start an AlphaFold3 calculations, the terms of use for AlphaFold3 requier that each user must obtain their own copy of the trained model parameters:\n", "\n", "1. Fill out the form [https://forms.gle/svvpY4u2jsHEwWYS6](https://forms.gle/svvpY4u2jsHEwWYS6)\n", - "2. Once access has been granted, download the model parameters file: `af3.bin.zst`\n", - "3. Extract the file as `af3.bin`.\n", - "4. Store the model parameters file in a directory on the cluster, for example in $HOME/af3-models\n", + "2. Once access has been granted, download the model parameters file: af3.bin.zst\n", + "3. Store the model parameters file in a directory on the cluster, for example in $HOME/af3-models\n", "\n", "AlphaFold 3 will not run without the model parameters file." ] }, { "cell_type": "code", - "execution_count": null, - "id": "1", + "execution_count": 1, + "id": "6ff5de81-3589-4081-9993-29241f4fc451", "metadata": {}, "outputs": [], "source": [ @@ -32,7 +31,7 @@ }, { "cell_type": "markdown", - "id": "2", + "id": "937738e7-c6b6-495b-9c17-a5c115a7cbbd", "metadata": {}, "source": [ "Next we need to define where AlphaFold finds our input data and where the output files are written to. You can see these files in the file browser on the left. If you change these names, remember to change them in the second notebook as well." @@ -40,8 +39,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "3", + "execution_count": 2, + "id": "937944d5-9bfd-4b1a-b9a2-6fe8f320d22e", "metadata": {}, "outputs": [], "source": [ @@ -51,18 +50,10 @@ ") # must be created by user" ] }, - { - "cell_type": "markdown", - "id": "4", - "metadata": {}, - "source": [ - "You do not need to edit the cells below, these set the environment for this and the subsequent runs." - ] - }, { "cell_type": "code", - "execution_count": null, - "id": "5", + "execution_count": 3, + "id": "ee9d14bd-14b9-4e21-9ff1-7be70398ec4e", "metadata": {}, "outputs": [], "source": [ @@ -92,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "6", + "id": "04fdd503-3ee5-4631-b8d5-56e07c218f81", "metadata": {}, "source": [ "## Input File\n", @@ -103,8 +94,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "7", + "execution_count": 7, + "id": "277536a4-49fd-4760-bece-6e35bf116283", "metadata": {}, "outputs": [], "source": [ @@ -113,8 +104,7 @@ "\"name\": \"test\",\n", "\"sequences\": [\n", " { \n", - " \"protein\": \n", - " {\n", + " \"protein\": {\n", " \"id\": [\"A\", \"B\"],\n", " \"sequence\": \"GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\"\n", " }\n", @@ -128,14 +118,12 @@ "\"\"\"\n", "\n", "with open(ALPHAFOLD_JSON_PATH, \"w\") as file:\n", - " print(ALPHAFOLD_JSON_PATH)\n", - " file.write(input_json)\n", - " print(f\"File written to {ALPHAFOLD_JSON_PATH}.\")" + " file.write(input_json)" ] }, { "cell_type": "markdown", - "id": "8", + "id": "4eaea505-7fec-480b-8a75-96935cc26d61", "metadata": {}, "source": [ "Now we combine the information on input and output directories to generate the run file to start the calculation:" @@ -143,8 +131,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "9", + "execution_count": 8, + "id": "88e12134-4f09-4c04-be49-32d3bb70a459", "metadata": {}, "outputs": [], "source": [ @@ -155,43 +143,111 @@ "# Load software module \n", "module load bio/alphafold/3.0.1\n", "\n", + "\n", "# Run with option --norun_inference to generate Multiple Sequence Alignments (MSAs) and templates\n", "python $ALPHAFOLD_BIN_DIR/run_alphafold.py \\\\\n", - " --json_path={str(ALPHAFOLD_JSON_PATH)} \\\\\n", + " --json_path={ALPHAFOLD_JSON_PATH} \\\\\n", " --db_dir=$ALPHAFOLD_DATABASES \\\\\n", - " --model_dir={str(ALPHAFOLD_MODEL_DIR)} \\\\\n", - " --output_dir={str(ALPHAFOLD_RESULTS_DIR_PART1)} \\\\\n", + " --model_dir={ALPHAFOLD_MODEL_DIR} \\\\\n", + " --output_dir={ALPHAFOLD_RESULTS_DIR_PART1} \\\\\n", " --norun_inference\n", "\"\"\"\n", "\n", "with open(ALPHAFOLD_RUN_PATH, \"w\") as file:\n", - " file.write(run_file)\n", - " print(f\"File written to {ALPHAFOLD_RUN_PATH}.\")" + " file.write(run_file)" ] }, { "cell_type": "markdown", - "id": "10", + "id": "e2355a2b-4be5-43ba-b464-82a2a9b9ec80", "metadata": {}, "source": [ "## Run the Multi Sequence Alignment\n", - "Execute the cell below to start the alignment job. This will take about 5-10 minutes. Good luck!\n" + "Execute the cell below to start the alignment job. Good luck!\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "11", + "execution_count": 6, + "id": "e8b12862-7d99-4c49-ab59-cdd5ad1d4abf", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ - "nbval-ignore-output" + "nbval-skip" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running file /home/hd/hd_hd/hd_aq354/afold_test/run.sh\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0122 15:25:50.146059 23027279923008 pipeline.py:82] Getting protein MSAs for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:25:50.148108 23026631632640 jackhmmer.py:78] Query sequence: GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:25:50.148344 23026629531392 jackhmmer.py:78] Query sequence: GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:25:50.149204 23026627413760 jackhmmer.py:78] Query sequence: GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:25:50.149543 23026625296128 jackhmmer.py:78] Query sequence: GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:25:50.149919 23026629531392 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/jackhmmer -o /dev/null -A /tmp/tmp6_n9_mth/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/tmp6_n9_mth/query.fasta /opt/bwhpc/common/bio/alphafold/3.0.1/databases/mgy_clusters_2022_05.fa\"\n", + "I0122 15:25:50.150073 23026631632640 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/jackhmmer -o /dev/null -A /tmp/tmpgngbip86/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/tmpgngbip86/query.fasta /opt/bwhpc/common/bio/alphafold/3.0.1/databases/uniref90_2022_05.fa\"\n", + "I0122 15:25:50.153998 23026625296128 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/jackhmmer -o /dev/null -A /tmp/tmpdd_6r46v/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/tmpdd_6r46v/query.fasta /opt/bwhpc/common/bio/alphafold/3.0.1/databases/uniprot_all_2021_04.fa\"\n", + "I0122 15:25:50.154871 23026627413760 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/jackhmmer -o /dev/null -A /tmp/tmp_g1j2iol/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/tmp_g1j2iol/query.fasta /opt/bwhpc/common/bio/alphafold/3.0.1/databases/bfd-first_non_consensus_sequences.fasta\"\n", + "I0122 15:27:38.826486 23026627413760 subprocess_utils.py:97] Finished Jackhmmer in 108.671 seconds\n", + "I0122 15:31:08.789398 23026631632640 subprocess_utils.py:97] Finished Jackhmmer in 318.639 seconds\n", + "I0122 15:36:14.747779 23026625296128 subprocess_utils.py:97] Finished Jackhmmer in 624.594 seconds\n", + "I0122 15:40:57.124381 23026629531392 subprocess_utils.py:97] Finished Jackhmmer in 906.974 seconds\n", + "I0122 15:40:57.215243 23027279923008 pipeline.py:115] Getting protein MSAs took 907.07 seconds for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:40:57.215333 23027279923008 pipeline.py:121] Deduplicating MSAs for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:40:57.230077 23027279923008 pipeline.py:134] Deduplicating MSAs took 0.01 seconds for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG, found 8506 unpaired sequences, 7080 paired sequences\n", + "I0122 15:40:57.233228 23027279923008 pipeline.py:40] Getting protein templates for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n", + "I0122 15:40:57.338438 23027279923008 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/hmmbuild --informat stockholm --hand --amino /tmp/tmpuwu0tzh5/output.hmm /tmp/tmpuwu0tzh5/query.msa\"\n", + "I0122 15:40:57.814821 23027279923008 subprocess_utils.py:97] Finished Hmmbuild in 0.476 seconds\n", + "I0122 15:40:57.817795 23027279923008 subprocess_utils.py:68] Launching subprocess \"/opt/bwhpc/common/bio/alphafold/3.0.1/extlib/hmmer/3.4/bin/hmmsearch --noali --cpu 8 --F1 0.1 --F2 0.1 --F3 0.1 -E 100 --incE 100 --domE 100 --incdomE 100 -A /tmp/tmpd3pcw5zq/output.sto /tmp/tmpd3pcw5zq/query.hmm /opt/bwhpc/common/bio/alphafold/3.0.1/databases/pdb_seqres_2022_09_28.fasta\"\n", + "I0122 15:41:05.071332 23027279923008 subprocess_utils.py:97] Finished Hmmsearch in 7.253 seconds\n", + "I0122 15:41:05.389657 23027279923008 pipeline.py:52] Getting 4 protein templates took 8.16 seconds for sequence GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running AlphaFold 3. Please note that standard AlphaFold 3 model parameters are\n", + "only available under terms of use provided at\n", + "https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md.\n", + "If you do not agree to these terms and are using AlphaFold 3 derived model\n", + "parameters, cancel execution of AlphaFold 3 inference with CTRL-C, and do not\n", + "use the model parameters.\n", + "Skipping running model inference.\n", + "Processing fold inputs.\n", + "Processing fold input #1\n", + "Processing fold input test\n", + "Running data pipeline...\n", + "Processing chain A\n", + "Processing chain A took 915.30 seconds\n", + "Processing chain B\n", + "Processing chain B took 0.06 seconds\n", + "Output directory: /home/hd/hd_hd/hd_aq354/afold_test/output/test\n", + "Writing model input JSON to /home/hd/hd_hd/hd_aq354/afold_test/output/test\n", + "Skipping inference...\n", + "Done processing fold input test.\n", + "Done processing 1 fold inputs.\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "os.system(f'echo \"Running file {ALPHAFOLD_RUN_PATH}\"')\n", "os.system(f\"bash {ALPHAFOLD_RUN_PATH}\")" @@ -199,7 +255,7 @@ }, { "cell_type": "markdown", - "id": "12", + "id": "d29f2b4d-886e-4941-b526-77a3f24974ea", "metadata": {}, "source": [ "## Next steps\n", @@ -211,7 +267,7 @@ ], "metadata": { "kernelspec": { - "display_name": "bsh", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -225,7 +281,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/AFold_Confidence_Levels.ipynb b/notebooks/AFold_Confidence_Levels.ipynb index a0a9298..be798d6 100644 --- a/notebooks/AFold_Confidence_Levels.ipynb +++ b/notebooks/AFold_Confidence_Levels.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "0", + "id": "5fca6cd7-1e14-4ff5-9fa5-724ae0c06454", "metadata": {}, "source": [ "## Preparation\n", @@ -13,18 +13,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "1", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install biopython seaborn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2", + "execution_count": 1, + "id": "f8b82069-92f6-4164-820d-3faa067047ca", "metadata": {}, "outputs": [], "source": [ @@ -39,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "3", + "id": "716613cb-1c90-463b-9045-5359d581cf1d", "metadata": {}, "source": [ "Now we need to find the output files we need:" @@ -47,8 +37,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "4", + "execution_count": 2, + "id": "a8477e5b-d829-4b27-91e4-8201a991aded", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +46,7 @@ "project_name = \"test\"\n", "\n", "# working directory\n", - "ALPHAFOLD_WORKING_DIR = Path.home() / \"afold_test\"\n", + "ALPHAFOLD_WORKING_DIR = Path(\"afold_test\")\n", "\n", "# inference output directory\n", "ALPHAFOLD_RESULTS_DIR_PART2 = ALPHAFOLD_WORKING_DIR / \"output_gpu\"" @@ -64,7 +54,7 @@ }, { "cell_type": "markdown", - "id": "5", + "id": "7f227a3f-4cfa-4314-8da6-d24cf6823879", "metadata": {}, "source": [ "Now we use this information to find the relevant files from the AlphaFold3 output directly. This includes:\n", @@ -74,13 +64,9 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, + "execution_count": 3, + "id": "9ec8372f-5fe3-48cb-9cec-ba9ec14fd230", + "metadata": {}, "outputs": [], "source": [ "# get residue info from cif file\n", @@ -108,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "7", + "id": "e18ea10c-410a-49cc-a325-a0f63b58ac82", "metadata": {}, "source": [ "## Overal Confidence Levels\n" @@ -116,76 +102,97 @@ }, { "cell_type": "markdown", - "id": "8", + "id": "ec51c7fe-bcc5-4b36-bce4-c316dd1a00de", "metadata": {}, "source": [ - "The clash value indicates if the structure has a significant number of clashing atoms (more than 50% or a chain or a chain with more than 100 atoms)." + "The clash value indicates if the structure has a significant number of clasing atoms (more than 50% or a chain or a chain with more than 100 atoms)." ] }, { "cell_type": "code", - "execution_count": null, - "id": "9", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 4, + "id": "1457cffb-f7a1-4c46-a40c-76c5be38d399", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confidence[\"has_clash\"]" ] }, { "cell_type": "markdown", - "id": "10", + "id": "88a92613-8b1b-4b8f-abc5-7a3354c7ff53", "metadata": {}, "source": [ "The **predicted template modeling (pTM)** score measures the accuracy of the entire structure. It ranges from 0-1. A pTM score above 0.5 means the overal predicted ford for the complex might be similar to the true structure.\n", "For more information see https://doi.org/10.1093/BIOINFORMATICS/BTQ066.\n", "\n", - "Note that TM score is strict for small structures or short chains (fewer than 20 tokens). For these cases PAE and pLDDT may be more indicative of prediction quality." + "Note that TM score is strict for small structures or short chains (fewer than 20 tolkens). For these cases PAE and pLDDT may be more indicative of prediction quality." ] }, { "cell_type": "code", - "execution_count": null, - "id": "11", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 5, + "id": "df085637-e6f7-47a7-a135-8cc62785a5d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.62" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confidence[\"ptm\"]" ] }, { "cell_type": "markdown", - "id": "12", + "id": "3ee8090e-f2b2-4960-a14d-1d73f35b823d", "metadata": {}, "source": [ - "The **interface predicted template modeling (ipTM)** score measures accuracy of the predicted relative positions of the subunits within the complex. It ranges from 0 to 1. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggests likely a failed prediction." + "The **interface predicted template modeling (ipTM)** score measures accuracy of the predicted relative positions of the subunits within the complex. It ranges from 0 to 1. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggest likely a failed prediction." ] }, { "cell_type": "code", - "execution_count": null, - "id": "13", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 6, + "id": "6eb16382-14e8-4aa7-9c05-46dea8672e85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.63" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confidence[\"iptm\"]" ] }, { "cell_type": "markdown", - "id": "14", + "id": "8fdd7d04-da9b-499b-b66a-870ad8e3e0c3", "metadata": {}, "source": [ "The **predicted local distance difference test (pLDDT)** is a local confidence measure, calculated for each atom and residue. It uses a 0-100 scale, where higher values indicate higher confidence. Values above 90 indicate high confidence, a value below 50 indicates low confidence in this part of the predicted structure." @@ -193,14 +200,21 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "15", - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], + "execution_count": 7, + "id": "c78f19ff-c680-4c0c-a228-b848f487f246", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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