DeepSP_main.py

# ----- Package Import ----- #
import numpy as np
import pandas as pd


import tensorflow as tf
from tensorflow.keras.models import model_from_json
from tensorflow.keras.utils import plot_model
import keras
from keras.models import model_from_json
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam

import os
import subprocess
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
#################
def format_predictions(predictions):
    if len(predictions.shape) > 1:
        formatted = np.round(predictions, 2)
    else:
        formatted = [round(pred, 2) for pred in predictions]
    return formatted
    
def process_file(filepath):

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

    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")

    subprocess.run(['ANARCI', '-i', 'seq_H.fasta', '-o', 'seq_aligned', '-s', 'imgt', '-r', 'heavy', '--csv'])
    subprocess.run(['ANARCI', '-i', 'seq_L.fasta', '-o', 'seq_aligned', '-s', 'imgt', '-r', 'light', '--csv'])


    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()

    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 Sequence ----- #
    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)



# ----- Predict DeepSP Predictor ----- #

    # sappos
    json_file = open('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("Conv1D_regression_SAPpos.h5")
    loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
    sap_pos = loaded_model.predict(X)


    # scmpos
    json_file = open('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("Conv1D_regression_SCMpos.h5")
    loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
    scm_pos = loaded_model.predict(X)
    

    # scmneg
    json_file = open('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("Conv1D_regression_SCMneg.h5")
    loaded_model.compile(optimizer='adam', loss='mae', metrics=['mae'])
    scm_neg = loaded_model.predict(X)

    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 = pd.concat([pd.DataFrame(name_list), pd.DataFrame(format_predictions(sap_pos)), pd.DataFrame(format_predictions(scm_neg)), pd.DataFrame(format_predictions(scm_pos))], ignore_index=True, axis=1,); df.columns = features

    predictions_path = 'uploads/DeepSP_descriptors.csv'
    df.to_csv(predictions_path, index=False)
    
    try:
        os.remove('seq_H.fasta')
        os.remove('seq_L.fasta')
        os.remove('seq_aligned_H.csv')
        os.remove('seq_aligned_KL.csv')
        os.remove('seq_aligned_HL.txt')
    except Exception as e:
        print(f"Error deleting files: {e}")

    FOLDER='uploads'
    input_data_path = os.path.join(FOLDER, 'input_data.csv')
    try:
        os.remove(input_data_path)
        print(f"{input_data_path} has been deleted.")
    except FileNotFoundError:
        print(f"{input_data_path} not found. File might have been deleted already.")
    except Exception as e:
        print(f"Error deleting {input_data_path}: {e}")

    #print(f"Processed file saved to: {csv_path}")
    return predictions_path