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24fff69 | 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 | import requests
from joblib import Parallel, delayed
import os
import argparse
import sys
import json
import pandas as pd
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--data_file", help="data file", type=str, required=True)
parser.add_argument("--alphafold_dir", help="directory where alphafold models are stroed", type=str, required=True)
parser.add_argument("--esm_dir", help="directory where to store esm models", type=str, required=True)
parser.add_argument("--out_file", help="out file", type=str, required=True)
args, unparsed = parser.parse_known_args()
parser = argparse.ArgumentParser()
return args
args = parse_args()
def _is_empty_model(modelpath):
try:
structure = esm.inverse_folding.util.load_structure(modelpath, 'A')
return False
except:
return True
import ipdb
ipdb.set_trace()
alphafold_models = os.listdir(args.alphafold_dir)
esm_models = os.listdir(args.esm_dir)
af_model_path = lambda uni: args.alphafold_dir + f'/AF-{uni}-F1-model_v4.pdb'
esm_model_path = lambda uni: args.esm_dir + f'/ESMFold-{uni}-v1.pdb'
df = pd.read_csv(args.data_file)
modelpaths = []
for uni in tqdm(df.uniprot):
modelpath = af_model_path(uni)
modelpath2 = esm_model_path(uni)
if not _is_empty_model(modelpath): modelpaths.append(modelpath)
elif not _is_empty_model(modelpath2): modelpaths.append(modelpath2)
else: modelpaths.append(None)
df['pdbpath'] = modelpaths
df.info()
df.dropna(subset=['pdbpath'],inplace=True)
df.reset_index(inplace=True,drop=True)
df.to_csv(args.out_file) |