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tools.py
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tools.py
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#################################################################
# #
# Useful python scripts for interfacing #
# with datasets and programs #
# #
#################################################################
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#from pypdb import describe_pdb
import os, sys
import tqdm
import Bio
def ProTherm_data():
data_path = "data/ProTherm+HotMusic.csv"
dataset = pd.read_csv(data_path)
return dataset
def Master_results():
data_path = "results/master.csv"
dataset = pd.read_csv(data_path)
return dataset
class HotMusic_data(object):
def __init__(self, data_path="HotMusic_dataset.csv"):
self.data_path = data_path
self.load_dataset()
def load_dataset(self):
dataset = pd.read_csv(self.data_path)
variations = list(dataset["Variation"])
for i, variation in enumerate(variations):
dataset.loc[i,"wt"] = variation[0]
dataset.loc[i,"mut"] = variation[-1]
dataset.loc[i,"location"] = variation[1:-1]
self.dataset = dataset
class Missense3D_data(object):
def __init__(self, data_path="", tsv_path=""):
self.data_path = data_path
self.tsv_path = tsv_path
try:
self.load_dataset()
except:
self.read_tsv()
def load_dataset(self):
self.dataset = pd.read_csv(self.data_path, header=0, index_col=False)
def save_dataset(self):
self.dataset.to_csv(self.data_path, index=False)
print("Dataset saved as: " + self.data_path)
def read_tsv(self):
print("No precomputed database detected, searching for TSV files")
dataset = pd.read_csv(self.tsv_path, sep='\t', header=0, index_col=False)
#Remove errors from dataset
count = 0
for i in range(dataset.shape[0]):
if dataset.loc[i, "#UniProt ID"][0] == "#":
dataset.drop(i, inplace=True)
count = count + 1
dataset.reset_index(inplace=True)
print("--- Removed {} errors from Missense3d results ---".format(count))
#Extract discription headers
descriptions = dataset.loc[1, "#Description"]
descriptions = descriptions.split("|")
headers = []
for i in range(16):
headers.append(descriptions[i].split(":")[1])
#Save prediction and description information
dataset.astype("object")
for i in range(dataset.shape[0]):
descriptions = dataset.loc[i, "#Description"]
descriptions = descriptions.split("|")
dataset.loc[i, "one_hot_features"] = ""
if dataset.loc[i,"#Prediction"].split()[1] == "Damaging":
dataset.loc[i, "BoolPrediction"] = 1
else:
dataset.loc[i, "BoolPrediction"] = 0
for j in range(16):
description = descriptions[j].split(":")[2]
if description[0] == "Y":
dataset.loc[i, "one_hot_features"] = dataset.loc[i, "one_hot_features"]+ "1"
else:
dataset.loc[i, "one_hot_features"]= dataset.loc[i, "one_hot_features"]+ "0"
# CONSTRUCT VARIANT INFO FOR DATA MERGING
dataset.loc[i, "#Orig"] = dataset.loc[i, "#Orig"][0] + dataset.loc[i, "#Orig"][1:].lower()
dataset.loc[i, "#Mutant"] = dataset.loc[i, "#Mutant"][0] + dataset.loc[i, "#Mutant"][1:].lower()
dataset.loc[i, "variant_info"] = dataset.loc[i, "#PDB ID"] + "_" + Bio.Data.IUPACData.protein_letters_3to1[dataset.loc[i, "#Orig"]] + str(dataset.loc[i, "#PosInPDB"]) + Bio.Data.IUPACData.protein_letters_3to1[dataset.loc[i, "#Mutant"]]
print(dataset.loc[i])
self.dataset = dataset
print("Constructed database")
def Missense3D_training_data():
data1 = pd.read_excel("/project/home/student1/FYP/data/raw/missense3d/all_dataset.xlsx", header=0, indexes=True)
data2 = pd.read_excel("/project/home/student1/FYP/data/raw/missense3d/control_dataset.xlsx", header=0, indexes=True)
data = pd.concat([data1, data2])
data.reset_index(drop=True, inplace=True)
return data
class FoldX(object):
def __init__(self, pdb_id, mode=None, save_pdb=False, mutation=None, temp=25, pH=7):
self.output_file = pdb_id + "_Repair_0_ST.fxout"
self.repaired_id = pdb_id + "_Repair.pdb"
self.position_scan_file = "PS_" + self.repaired_id[:-4] + "_scanning_output.txt"
self.pdb_id = pdb_id + ".pdb"
self.pdb_dir = "/project/home/student1/FYP/files/pdb/"
self.repaired_dir = "/project/home/student1/FYP/files/repaired_pdb"
self.output_dir = "/project/home/student1/FYP/files/foldx_stability"
self.position_scan_dir = "/project/home/student1/FYP/files/foldx_position_scan"
self.mutation = mutation
self.temp = temp + 273
self.pH = pH
if save_pdb == False:
self.save_pdb = "false"
else:
self.save_pdb = "true"
if mode == "stability":
self.stability()
elif mode == "position_scan":
self.position_scan()
else:
print("Error: No mode selected")
exit()
def stability(self):
try:
self.load_stability_output()
except:
os.system("module load foldx")
try:
self.run_stability()
self.load_stability_output()
except:
self.repair_pdb()
self.run_stability()
self.load_output()
def position_scan(self):
try:
self.run_position_scan()
self.read_position_scan()
except:
self.repair_pdb()
self.run_position_scan()
self.read_position_scan()
def run_position_scan(self):
os.system("module load foldx")
os.system("foldx --command=PositionScan --out-pdb={} --pdb-dir={} --pdb={} --output-dir={} --positions={} --temperature={} --pH={}".format(
self.save_pdb,
self.repaired_dir,
self.repaired_id,
self.position_scan_dir,
self.mutation,
self.temp,
self.pH))
#print("Run FoldX point mutation for " + self.pdb_id + " with mutation " + self.mutation)
def read_position_scan(self):
try:
#Read and extract ddG calculation
output = open(self.position_scan_dir + "/" + self.position_scan_file, "r")
self.ddG = float(output.readlines()[1][8:])
except:
pass
def load_stability_output(self):
#Read and extract stability calculation
output = open(self.output_dir + "/" + self.output_file, "r")
output = output.read()
output = output.split("\t")
self.stability = float(output[1])
def repair_pdb(self):
os.system("foldx --command=RepairPDB --pdb-dir={} --pdb={} --output-dir={}".format(
self.pdb_dir,
self.pdb_id,
self.repaired_dir))
print("Repaired " + self.pdb_id)
def run_stability(self):
shell_cmd = ("foldx --command=Stability --pdb-dir={} --pdb={} --output-dir={}".format(
self.repaired_dir,
self.repaired_id,
self.output_dir))
os.system(shell_cmd)
#print("Run FoldX stability calculations for " + self.pdb_id)
def read_dssp(dsspfile):
#Read DSSP file
dsspfile = open(dsspfile, "r")
lines = dsspfile.readlines()
for i, line in enumerate(lines):
if line[2] == "#":
start = i
lines = lines[start+1:]
#Extract ASA information
asa_dict = {}
for line in lines:
try:
res_id = int(line[7:10].strip())
"""
print(res_id)
for i in range(len(res_id)):
print(res_id[i])
if res_id[i] != " ":
res_id = int(res_id[i:])
"""
asa = int(line[36:38])
asa_dict["res_" + str(res_id)] = asa
except:
pass
return asa_dict
def get_ASA(pdb_id, position):
pdbfile = "/project/home/student1/FYP/files/pdb/" + pdb_id + ".pdb"
dsspfile = "/project/home/student1/FYP/files/dssp/" + pdb_id + ".dssp"
logfile = "/project/home/student1/FYP/files/dssp/" + pdb_id + ".log"
shell_cmd = ("dssp {} {} > {} ".format(pdbfile, dsspfile, logfile))
os.system(shell_cmd)
shell_check = os.system("echo $?")
if shell_check != 0:
print("DSSP Error detected for " + pdb_id)
asa_dict = read_dssp(dsspfile)
return asa_dict["res_" + str(position)]
from Bio.Data.IUPACData import protein_letters_3to1
def generate_missense3d_input_file(path):
data = pd.read_csv(path)
input_file = open("missense_input.csv", "w")
for i in range(data.shape[0]):
line = "P\tPDB\t-\tfiles/pdb/" + data.loc[i, "pdb_id"] + ".pdb\tA\t" + str(data.loc[i, "pos"]) + "\t" + Bio.Data.IUPACData.protein_letters_1to3[data.loc[i, "wt"]] + "\t" + Bio.Data.IUPACData.protein_letters_1to3[data.loc[i, "mut"]] + "\n"
input_file.write(line)