deepviscosity_predictor.py

# Import libraries
import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 

import numpy as np
import pandas as pd
import random

from numpy.random import seed
import joblib

import tensorflow as tf
from tensorflow.keras.models import model_from_json

import keras
from keras.models import model_from_json
from keras.optimizers import Adam

from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord



# Import dataset
dataset = pd.read_csv('DeepViscosity_input.csv') # replace with your csv file, see format in DeepViscosity_input.csv file

name = dataset['Name'].to_list()
Heavy_seq = dataset['Heavy_Chain'].to_list()
Light_seq = dataset['Light_Chain'].to_list()


# convert to fasta file
file_out='seq_H.fasta'

with open(file_out, "w") as output_handle:
  for i in range(len(name)):
    seq_name = name[i]
    seq = Heavy_seq[i]
    record = SeqRecord(
    Seq(seq),
    id=seq_name,
    name="",
    description="",
    )
    SeqIO.write(record, output_handle, "fasta")

file_out='seq_L.fasta'

with open(file_out, "w") as output_handle:
  for i in range(len(name)):
    seq_name = name[i]
    seq = Light_seq[i]
    record = SeqRecord(
    Seq(seq),
    id=seq_name,
    name="",
    description="",
    )
    SeqIO.write(record, output_handle, "fasta")

# sequence alignment with ANARCI
os.system('ANARCI -i seq_H.fasta -o seq_aligned -s imgt -r heavy --csv')
os.system('ANARCI -i seq_L.fasta -o seq_aligned -s imgt -r light --csv')

H_aligned = pd.read_csv('seq_aligned_H.csv')
L_aligned = pd.read_csv('seq_aligned_KL.csv')

# sequence alignment - source: # https://github.com/Lailabcode/DeepSCM/blob/main/deepscm-master/seq_preprocessing.py
def seq_preprocessing():
  infile_H = pd.read_csv('seq_aligned_H.csv')
  infile_L = pd.read_csv('seq_aligned_KL.csv')
  outfile = open('seq_aligned_HL.txt', "w")

  H_inclusion_list = ['1','2','3','4','5','6','7','8','9','10',                     '11','12','13','14','15','16','17','18','19','20',                     '21','22','23','24','25','26','27','28','29','30',                     '31','32','33','34','35','36','37','38','39','40',                     '41','42','43','44','45','46','47','48','49','50',                     '51','52','53','54','55','56','57','58','59','60',                     '61','62','63','64','65','66','67','68','69','70',                     '71','72','73','74','75','76','77','78','79','80',                     '81','82','83','84','85','86','87','88','89','90',                     '91','92','93','94','95','96','97','98','99','100',                     '101','102','103','104','105','106','107','108','109','110',                     '111','111A','111B','111C','111D','111E','111F','111G','111H',                     '112I','112H','112G','112F','112E','112D','112C','112B','112A','112',                    '113','114','115','116','117','118','119','120',                     '121','122','123','124','125','126','127','128']

  L_inclusion_list = ['1','2','3','4','5','6','7','8','9','10',                     '11','12','13','14','15','16','17','18','19','20',                     '21','22','23','24','25','26','27','28','29','30',                     '31','32','33','34','35','36','37','38','39','40',                     '41','42','43','44','45','46','47','48','49','50',                     '51','52','53','54','55','56','57','58','59','60',                     '61','62','63','64','65','66','67','68','69','70',                     '71','72','73','74','75','76','77','78','79','80',                     '81','82','83','84','85','86','87','88','89','90',                     '91','92','93','94','95','96','97','98','99','100',                     '101','102','103','104','105','106','107','108','109','110',                     '111','112','113','114','115','116','117','118','119','120',                     '121','122','123','124','125','126','127']

  H_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9,           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19,           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29,           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39,           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49,           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59,           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69,           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79,           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89,           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99,           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109,           '111':110,'111A':111,'111B':112,'111C':113,'111D':114,'111E':115,'111F':116,'111G':117,'111H':118,           '112I':119,'112H':120,'112G':121,'112F':122,'112E':123,'112D':124,'112C':125,'112B':126,'112A':127,'112':128,           '113':129,'114':130,'115':131,'116':132,'117':133,'118':134,'119':135,'120':136,           '121':137,'122':138,'123':139,'124':140,'125':141,'126':142,'127':143,'128':144}

  L_dict = {'1': 0, '2':1, '3':2, '4':3, '5':4, '6':5, '7':6, '8':7, '9':8, '10':9,           '11':10, '12':11, '13':12, '14':13, '15':14, '16':15, '17':16, '18':17, '19':18, '20':19,           '21':20, '22':21, '23':22, '24':23, '25':24, '26':25, '27':26, '28':27, '29':28, '30':29,           '31':30, '32':31, '33':32, '34':33, '35':34, '36':35, '37':36, '38':37, '39':38, '40':39,           '41':40, '42':41, '43':42, '44':43, '45':44, '46':45, '47':46, '48':47, '49':48, '50':49,           '51':50, '52':51, '53':52, '54':53, '55':54, '56':55, '57':56, '58':57, '59':58, '60':59,           '61':60, '62':61, '63':62, '64':63, '65':64, '66':65, '67':66, '68':67, '69':68, '70':69,           '71':70, '72':71, '73':72, '74':73, '75':74, '76':75, '77':76, '78':77, '79':78, '80':79,           '81':80, '82':81, '83':82, '84':83, '85':84, '86':85, '87':86, '88':87, '89':88, '90':89,           '91':90, '92':91, '93':92, '94':93, '95':94, '96':95, '97':96, '98':97, '99':98, '100':99,           '101':100,'102':101,'103':102,'104':103,'105':104,'106':105,'107':106,'108':107,'109':108,'110':109,           '111':110,'112':111,'113':112,'114':113,'115':114,'116':115,'117':116,'118':117,'119':118,'120':119,           '121':120,'122':121,'123':122,'124':123,'125':124,'126':125,'127':126,'128':127}


  N_mAbs = len(infile_H["Id"])

  for i in range(N_mAbs):
    H_tmp = 145*['-']
    L_tmp = 127*['-']
    for col in infile_H.columns:
        if(col in H_inclusion_list):
            H_tmp[H_dict[col]]=infile_H.iloc[i][col]
    for col in infile_L.columns:
        if(col in L_inclusion_list):
            L_tmp[L_dict[col]]=infile_L.iloc[i][col]

    aa_string = ''
    for aa in H_tmp+L_tmp:
         aa_string += aa
    outfile.write(infile_H.iloc[i,0]+" "+aa_string)
    outfile.write("\n")

  outfile.close()
  return

seq_preprocessing()

# read aligned sequences
def load_input_data(filename):
    name_list=[]
    seq_list=[]
    with open(filename) as datafile:
        for line in datafile:
            line = line.strip().split()
            name_list.append(line[0])
            seq_list.append(line[1])
    return name_list, seq_list

name_list, seq_list = load_input_data('seq_aligned_HL.txt')
X = seq_list

# One hot encoding of aligned sequences
def one_hot_encoder(s):
    d = {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4, 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9, 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14, 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20}

    x = np.zeros((len(d), len(s)))
    x[[d[c] for c in s], range(len(s))] = 1

    return x
X = [one_hot_encoder(s=x) for x in X]
X = np.transpose(np.asarray(X), (0, 2, 1))
X = np.asarray(X)


# DeepSP Predictions: source - https://github.com/Lailabcode/DeepSP

# sappos
json_file = open('DeepSP_CNN_model/Conv1D_regressionSAPpos.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into model
loaded_model.load_weights("DeepSP_CNN_model/Conv1D_regression_SAPpos.h5")
loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
sap_pos = loaded_model.predict(X)

# scmpos
json_file = open('DeepSP_CNN_model/Conv1D_regressionSCMpos.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into model
loaded_model.load_weights("DeepSP_CNN_model/Conv1D_regression_SCMpos.h5")
loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
scm_pos = loaded_model.predict(X)

# scmneg
json_file = open('DeepSP_CNN_model/Conv1D_regressionSCMneg.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into model
loaded_model.load_weights("DeepSP_CNN_model/Conv1D_regression_SCMneg.h5")
loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
scm_neg = loaded_model.predict(X)

#print predicted DeepSP features
features = ['Name', 'SAP_pos_CDRH1','SAP_pos_CDRH2','SAP_pos_CDRH3','SAP_pos_CDRL1','SAP_pos_CDRL2','SAP_pos_CDRL3','SAP_pos_CDR','SAP_pos_Hv','SAP_pos_Lv','SAP_pos_Fv',
          'SCM_neg_CDRH1','SCM_neg_CDRH2','SCM_neg_CDRH3','SCM_neg_CDRL1','SCM_neg_CDRL2','SCM_neg_CDRL3','SCM_neg_CDR','SCM_neg_Hv','SCM_neg_Lv','SCM_neg_Fv',
          'SCM_pos_CDRH1','SCM_pos_CDRH2','SCM_pos_CDRH3','SCM_pos_CDRL1','SCM_pos_CDRL2','SCM_pos_CDRL3','SCM_pos_CDR','SCM_pos_Hv','SCM_pos_Lv','SCM_pos_Fv']
df_deepsp = pd.concat([pd.DataFrame(name_list), pd.DataFrame(sap_pos), pd.DataFrame(scm_neg), pd.DataFrame(scm_pos)], ignore_index=True, axis=1,); df_deepsp.columns = features
df_deepsp.to_csv('DeepSP_descriptors.csv', index=False)


# DeepViscosity Predictions [ Low viscoity(<=20cps) : 0, High viscosity(>20cps) : 1 ]
X = df_deepsp.iloc[:, 1:]

scaler = joblib.load("DeepViscosity_scaler/DeepViscosity_scaler.save")
X_scaled = scaler.transform(X.values)

model_preds = []
for i in range(102):
    file = 'ANN_logo_' + str(i)
    with open('DeepViscosity_ANN_ensemble_models/'+file+'.json', 'r') as json_file:
        loaded_model_json = json_file.read()

    model = model_from_json(loaded_model_json)
    model.load_weights('DeepViscosity_ANN_ensemble_models/'+file+'.h5')
    model.compile(optimizer=Adam(0.0001), metrics=['accuracy'])

    pred = model.predict(X_scaled, verbose=0)
    model_preds.append(pred)

# Convert to numpy array
model_preds = np.array(model_preds)  # shape: (102, num_samples, 1)

# Calculate mean and std of probabilities
prob_mean = model_preds.mean(axis=0).flatten()  # shape: (num_samples,)
prob_std = model_preds.std(axis=0).flatten()

# Final class prediction by majority vote (threshold=0.5 on mean probability)
final_pred = (prob_mean >= 0.5).astype(int)

# Combine into DataFrame
df_deepvis = pd.DataFrame({
    'Name': name_list,
    'Prob_Mean': prob_mean,
    'Prob_Std': prob_std,
    'DeepViscosity_classes': final_pred
    
})

df_deepvis.to_csv('DeepViscosity_classes.csv', index=False)