Protein Domain Search Dashboard

A web application for searching and visualizing protein domains using AlphaFold predictions.

App development setup

• Docker image, built using .gitlab-ci.yml, pushed to GitLab Container Registry at registry.gitlab.ics.muni.cz:443/445526/widgets-app-starterpack:latest
• Kubernetes deployment using an appropriate kubeconfig file.

  kubectl --kubeconfig=/path/to/your/kubeconfig.yml apply -f kubernetes/deployment.yaml -n fi-lmi-ns
  

• data storage in a persistent volume claim, mounted to /app/data in the container.
  • to copy the data to the PVC:

  # 1) download kubectl, chmod +x kubectl
  # 2) configure your kubeconfig.yml, 
  # 3) run the command:
  kubectl --kubeconfig=/path/to/your/kubeconfig.yml cp -n fi-lmi-ns <path_to_data> <pod_with_pvc_mounted>:/app/data
  # kubectl --kubeconfig=/path/to/your/kubeconfig.yml cp -n fi-lmi-ns proteins/proteins-disordered-regions/analyses/search_results_P10911.json fi-lmi-ns/protein-search-dashboard-7c46dd6c75-m25nc:/app/data/structures/.
  

Useful commands:

1. Get storage info:

• curl https://protein-search-dashboard.dyn.cloud.e-infra.cz/storage-info

2. Access a specific protein:

• https://protein-search-dashboard.dyn.cloud.e-infra.cz/protein/P10911

Database Setup

To populate the protein database:

1. Prepare your data directories:

mkdir -p protein_db metadata
# Copy your PDB files to protein_db/
# Copy your JSON files to metadata/

2. Copy to the PVC:

# Get pod name
POD=$(kubectl get pods -n fi-lmi-ns -l app=protein-search-dashboard -o jsonpath='{.items[0].metadata.name}')

# Copy protein database
kubectl --kubeconfig=kubeconfig.yml cp -n fi-lmi-ns ./protein_db/. $POD:/app/data/protein_db/

# Copy metadata
kubectl --kubeconfig=kubeconfig.yml cp -n fi-lmi-ns ./metadata/. $POD:/app/data/metadata/

3. Verify the data:

curl https://protein-search-dashboard.dyn.cloud.e-infra.cz/storage-info

Environment Setup

1. Configure Environment Variables

Copy the example environment file and configure your settings:

cp env.example .env
# Edit .env with your actual values

Required environment variables:

• ALPHAFOLD_API_KEY: Your AlphaFold API key (optional, but recommended for full functionality)

2. Kubernetes Configuration (for deployment)

If you need to deploy to Kubernetes, ensure you have:

• A valid kubeconfig.yml file (not included in this repository for security)
• Proper permissions to access the target cluster

Local Development and Testing

Setting up the Development Environment

1. Ensure Conda is installed:

conda --version

2. Create and activate a Conda environment:

# Create environment with Python 3.9
conda create -n protein-viz python=3.9

# Activate environment
conda activate protein-viz

Alternatively, you can create the environment from the provided yml file:

conda env create -f conda-environment.yml
conda activate protein-viz

3. Install development dependencies:

# Install core dependencies
conda install -c conda-forge fastapi uvicorn py3dmol pandas numpy requests

# Install additional dependencies
pip install python-multipart typing-extensions

# Install development tools (optional)
conda install -c conda-forge pytest black flake8

4. Set up test data:

mkdir -p test
# Copy your test files
cp path/to/AF-P10911-F1-model_v4.pdb test/
cp path/to/search_results_P10911.json test/

5. Run the test server:

python test_local.py

6. Visit http://localhost:8000/protein/P10911 in your browser

The test environment uses a local directory structure that mirrors the production setup.

Troubleshooting

1. If you get SSL errors with Conda:

conda config --set ssl_verify False

2. If you need to remove and recreate the environment:

conda deactivate
conda env remove -n protein-viz
conda env create -f conda-environment.yml

3. If py3dmol is not available in conda-forge:

conda activate protein-viz
pip install py3dmol

4. To update the environment after changes to conda-environment.yml:

conda env update -f conda-environment.yml --prune

Project Structure

app/
├── __init__.py
├── main.py
├── config.py
├── models/
│   ├── __init__.py
│   └── protein.py
├── services/
│   ├── __init__.py
│   ├── protein_service.py
│   └── storage_service.py
├── templates/
│   ├── __init__.py
│   └── protein_view.py
└── utils/
    ├── __init__.py
    └── logging.py