In [1]:
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
# sys.path.insert(0,'..')
# from notebookFunctions import *
# from .. import notebookFunctions
from Bio.PDB.Polypeptide import one_to_three
from Bio.PDB.Polypeptide import three_to_one
from Bio.PDB.PDBParser import PDBParser
from pyCodeLib import *
from small_script.myFunctions import *
from collections import defaultdict
%matplotlib inline
# plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2
In [2]:
plt.rcParams['figure.figsize'] = [16.18033, 10] #golden ratio
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['figure.dpi'] = 100
plt.rcParams.update({'font.size': 22})
In [66]:
def get_two_part_from_eye_seperation(pdb, data):
row = data.query(f"Protein == '{pdb}'")
assert len(row) == 1
row = row.iloc[0]
glob_start, glob_end = row["Range"].split("-")
length = row["Length"]
glob_start = int(glob_start)
glob_end = int(glob_end)
print(pdb, glob_start, glob_end)
GlobularPart = list(range(glob_start, glob_end+1))
MembranePart = []
for i in range(1, length+1):
if i not in GlobularPart:
MembranePart.append(i)
return GlobularPart, MembranePart
def get_contactFromDMP(fileLocation, n, threshold=0.5):
a = np.zeros((n,n))
c_list = []
with open(fileLocation, "r") as f:
# for i in range(9):
# next(f)
for line in f:
# print(line)
try:
i,j,_,_,_,p = line.split(" ")
# print(i,j,p)
a[int(i)-1,int(j)-1] = float(p)
a[int(j)-1,int(i)-1] = float(p)
if float(p) > threshold:
c_list.append([int(i)-1,int(j)-1,float(p)])
except Exception as e:
print(e)
pass
return a, np.array(c_list)
from Bio.PDB.PDBParser import PDBParser
def getContactMapFromPDB(pdbFile):
cutoff = 9.5
MAX_OFFSET = 6
parser = PDBParser()
structure = parser.get_structure('target', pdbFile)
all_residues = list(structure.get_residues())
tmp = []
for res in all_residues:
# print(res.id)
if res.id[0] == ' ':
tmp.append(res)
all_residues = tmp
n = len(all_residues)
contact_table = np.zeros((n,n))
# print(all_residues, n)
for i, res1 in enumerate(all_residues):
for j, res2 in enumerate(all_residues):
contact_table[i][j] = res1["CA"]-res2["CA"]
data = (contact_table < cutoff)
remove_band = np.eye(n)
for i in range(1, MAX_OFFSET):
remove_band += np.eye(n, k=i)
remove_band += np.eye(n, k=-i)
data[remove_band==1] = 0
return data
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pdb_list = ["2xov_complete", "6e67A", "5xpd", "3kp9", "4a2n", "5d91", "4nv6", "4p79", "5dsg", "6g7o", "6a93", "2jo1", "1py6", "1pv6", "1u19"]
infoLocation = "/Users/weilu/Research/database/hybrid_prediction_database/length_info.csv"
info = pd.read_csv(infoLocation, index_col=0)
# get_two_part_from_eye
part_info = pd.read_csv("/Users/weilu/Research/database/hybrid_prediction_database/part_info.csv", names=["Protein", "Range"])
part_info = part_info.merge(info, on="Protein")
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part_info
pdb = pdb_list[0]
GlobularPart, MembranePart = get_two_part_from_eye_seperation(pdb, part_info)
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In [47]:
def getSeqFromFasta(fastaFile):
with open(fastaFile) as f:
a = f.readlines()
seq = ""
for line in a:
if line[0] == ">":
continue
seq += line.strip()
return seq
In [116]:
import textwrap
for pdb in pdb_list:
fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/setup/{pdb}/{pdb}.fasta"
seq = getSeqFromFasta(fastaFile)
GlobularPart, MembranePart = get_two_part_from_eye_seperation(pdb, part_info)
GlobularPart_index = (np.array(GlobularPart)-1).astype(int)
MembranePart_index = (np.array(MembranePart)-1).astype(int)
MembranePart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_MembranePart.fasta"
GlobularPart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_GlobularPart.fasta"
GlobularPart_seq = "".join(np.array(list(seq))[GlobularPart_index])
MembranePart_seq = "".join(np.array(list(seq))[MembranePart_index])
with open(MembranePart_fastaFile, "w") as out:
out.write(f">{pdb}_MembranePart:A\n")
out.write("\n".join(textwrap.wrap(MembranePart_seq, width=80))+"\n")
with open(GlobularPart_fastaFile, "w") as out:
out.write(f">{pdb}_GlobularPart:A\n")
out.write("\n".join(textwrap.wrap(GlobularPart_seq, width=80))+"\n")
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len(seq)
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In [58]:
# combine contact map.
# and convert into input for simulation.
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# pdb = "4nv6"
# fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/setup/{pdb}/{pdb}.fasta"
# seq = getSeqFromFasta(fastaFile)
# GlobularPart, MembranePart = get_two_part_from_eye_seperation(pdb, part_info)
# GlobularPart_index = (np.array(GlobularPart)-1).astype(int)
# MembranePart_index = (np.array(MembranePart)-1).astype(int)
# pre = "/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/"
# fileLocation = f"{pre}/{pdb}_MembranePart.deepmetapsicov.con"
# n = len(MembranePart)
# MembranePart_data = get_contactFromDMP(fileLocation, n)
# MembranePart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_MembranePart.fasta"
# seq_MembranePart = getSeqFromFasta(MembranePart_fastaFile)
# fileLocation = f"{pre}/{pdb}_GlobularPart.deepmetapsicov.con"
# n = len(GlobularPart)
# GlobularPart_data = get_contactFromDMP(fileLocation, n)
# GlobularPart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_GlobularPart.fasta"
# seq_GlobularPart = getSeqFromFasta(GlobularPart_fastaFile)
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plt.imshow(MembranePart_data[0] > 0.5, origin=0)
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plt.imshow(GlobularPart_data[0] > 0.5, origin=0)
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In [119]:
# pdb = "4nv6"
pdb = "6e67A"
fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/setup/{pdb}/{pdb}.fasta"
seq = getSeqFromFasta(fastaFile)
GlobularPart, MembranePart = get_two_part_from_eye_seperation(pdb, part_info)
GlobularPart_index = (np.array(GlobularPart)-1).astype(int)
MembranePart_index = (np.array(MembranePart)-1).astype(int)
pre = "/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/"
fileLocation = f"{pre}/{pdb}_MembranePart.deepmetapsicov.con"
data = pd.read_csv(fileLocation, sep="\s+", names=["i","j","s", "ss","p"]).dropna().reset_index(drop=True)
data["i"] = data["i"].astype(int)
data["j"] = data["j"].astype(int)
data["i"] = data["i"].apply(lambda x:MembranePart_index[x-1]+1)
data["j"] = data["j"].apply(lambda x:MembranePart_index[x-1]+1)
MembranePart_data = data
MembranePart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_MembranePart.fasta"
seq_MembranePart = getSeqFromFasta(MembranePart_fastaFile)
fileLocation = f"{pre}/{pdb}_GlobularPart.deepmetapsicov.con"
data = pd.read_csv(fileLocation, sep="\s+", names=["i","j","s", "ss","p"]).dropna().reset_index(drop=True)
data["i"] = data["i"].astype(int)
data["j"] = data["j"].astype(int)
data["i"] = data["i"].apply(lambda x:GlobularPart_index[x-1]+1)
data["j"] = data["j"].apply(lambda x:GlobularPart_index[x-1]+1)
GlobularPart_data = data
GlobularPart_fastaFile = f"/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/{pdb}_GlobularPart.fasta"
seq_GlobularPart = getSeqFromFasta(GlobularPart_fastaFile)
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data = pd.concat([GlobularPart_data, MembranePart_data]).reset_index(drop=True)
# data = pd.concat([MembranePart_data]).reset_index(drop=True)
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pre='/Users/weilu/opt/gremlin/'
directory=pre
distancesCACB=pd.read_csv(directory+'CACBmediandist.dat', delim_whitespace=True, header=None)
distancesCACA=pd.read_csv(directory+'CACAmediandist.dat', delim_whitespace=True, header=None)
distancesCBCB=pd.read_csv(directory+'CBCBmediandist.dat', delim_whitespace=True, header=None)
distancesCACB.columns = ['i', 'j', 'dist']
distancesCACA.columns = ['i', 'j', 'dist']
distancesCBCB.columns = ['i', 'j', 'dist']
# if you want to filter the gremlin data, adjust the parameters below
filter_threshold=0.5
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column=2
n=len(seq)
# print(n)
rnative_matrixCACB=np.ones([n,n])*99
rnative_matrixCACA=np.ones([n,n])*99
rnative_matrixCBCB=np.ones([n,n])*99
for _, line in data.iterrows():
i = int(line["i"])
j = int(line["j"])
irestype=seq[i-1]
jrestype=seq[j-1]
if line["p"] > filter_threshold:
if sum((distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype))>0: #check if pair is in correct order
well_centerCACB = distancesCACB[(distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype)]['dist'].values[0]
well_centerCACA = distancesCACA[(distancesCACA['i']==irestype)&(distancesCACA['j']==jrestype)]['dist'].values[0]
well_centerCBCB = distancesCBCB[(distancesCBCB['i']==irestype)&(distancesCBCB['j']==jrestype)]['dist'].values[0]
else:
well_centerCACB = distancesCACB[(distancesCACB['i']==jrestype)&(distancesCACB['j']==irestype)]['dist'].values[0]
well_centerCACA = distancesCACA[(distancesCACA['i']==jrestype)&(distancesCACA['j']==irestype)]['dist'].values[0]
well_centerCBCB = distancesCBCB[(distancesCBCB['i']==jrestype)&(distancesCBCB['j']==irestype)]['dist'].values[0]
rnative_matrixCACB[i-1, j-1] = well_centerCACB
rnative_matrixCACB[j-1, i-1] = well_centerCACB
rnative_matrixCACA[i-1, j-1] = well_centerCACA
rnative_matrixCACA[j-1, i-1] = well_centerCACA
rnative_matrixCBCB[i-1, j-1] = well_centerCBCB
rnative_matrixCBCB[j-1, i-1] = well_centerCBCB
# import matplotlib.pyplot as plt
pre = "/Users/weilu/Research/server/aug_2019/hybrid_protein_simulation/domain_contact_prediction/"
directory = f"{pre}/protein/{pdb}/DMP/"
os.system(f"mkdir -p {directory}")
# plt.imshow(rnative_matrixCACB, origin=0)
np.savetxt(directory + 'go_rnativeCACB.dat', rnative_matrixCACB, fmt='%10.5f')
np.savetxt(directory + 'go_rnativeCACA.dat', rnative_matrixCACA, fmt='%10.5f')
np.savetxt(directory + 'go_rnativeCBCB.dat', rnative_matrixCBCB, fmt='%10.5f')
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pre
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In [121]:
n = len(seq)
contact_map = np.zeros((n,n))
for _, line in data.iterrows():
i = int(line["i"])
j = int(line["j"])
irestype=seq[i-1]
jrestype=seq[j-1]
n = len(seq)
if line["p"] > 0.5:
contact_map[i-1][j-1] = 1
contact_map[j-1][i-1] = 1
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plt.imshow(contact_map, origin=0)
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len(seq)
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GlobularPart_index
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plt.imshow(contact_map, origin=0)
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plt.imshow(contact_map, origin=0)
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len(seq)
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data.head()
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fileLocation = f"{pre}/{pdb}_combined.deepmetapsicov.con"
with open(fileLocation, "w") as out:
for index, d in MembranePart_data.iterrows():
# print(index)
i = int(d["i"])
j = int(d["j"])
p = round(d["p"], 8)
s = int(d["s"])
ss = int(d["ss"])
out.write(f"{i} {j} {s} {ss} {p}\n")
for index, d in GlobularPart_data.iterrows():
# print(index)
i = int(d["i"]) + len(seq_A)
j = int(d["j"]) + len(seq_A)
p = round(d["p"], 8)
s = int(d["s"])
ss = int(d["ss"])
out.write(f"{i} {j} {s} {ss} {p}\n")
out.write("END\n")
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getSeqFromFasta
In [ ]:
def get_contactFromDMP(fileLocation, n, threshold=0.2):
a = np.zeros((n,n))
c_list = []
with open(fileLocation, "r") as f:
# for i in range(9):
# next(f)
for line in f:
# print(line)
try:
i,j,_,_,_,p = line.split(" ")
# print(i,j,p)
a[int(i)-1,int(j)-1] = float(p)
a[int(j)-1,int(i)-1] = float(p)
if float(p) > threshold:
c_list.append([int(i),int(j),float(p)])
except Exception as e:
print(e)
pass
return a, np.array(c_list)
def convertDMPToInput(pdbID, dmp_file, fasta_file, pre='/Users/weilu/opt/gremlin/'):
# pdbID = "2xov_complete_2"
# read in median distances for pairwise interactions (obtained from analysis of the pdb)
directory=pre
distancesCACB=pd.read_csv(directory+'CACBmediandist.dat', delim_whitespace=True, header=None)
distancesCACA=pd.read_csv(directory+'CACAmediandist.dat', delim_whitespace=True, header=None)
distancesCBCB=pd.read_csv(directory+'CBCBmediandist.dat', delim_whitespace=True, header=None)
distancesCACB.columns = ['i', 'j', 'dist']
distancesCACA.columns = ['i', 'j', 'dist']
distancesCBCB.columns = ['i', 'j', 'dist']
# if you want to filter the gremlin data, adjust the parameters below
filter_threshold=0.5
column=2
seq = ""
with open(fasta_file) as f:
for line in f:
if line[0] == ">":
continue
seq += line.strip()
# seq
n=len(seq)
_, dmp_pairs = get_contactFromDMP(dmp_file, n=n)
# print(n)
rnative_matrixCACB=np.ones([n,n])*99
rnative_matrixCACA=np.ones([n,n])*99
rnative_matrixCBCB=np.ones([n,n])*99
for pair in dmp_pairs:
i=int(pair[0])
j=int(pair[1])
irestype=seq[i-1]
jrestype=seq[j-1]
if float(pair[column]) > filter_threshold:
if sum((distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype))>0: #check if pair is in correct order
well_centerCACB = distancesCACB[(distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype)]['dist'].values[0]
well_centerCACA = distancesCACA[(distancesCACA['i']==irestype)&(distancesCACA['j']==jrestype)]['dist'].values[0]
well_centerCBCB = distancesCBCB[(distancesCBCB['i']==irestype)&(distancesCBCB['j']==jrestype)]['dist'].values[0]
else:
well_centerCACB = distancesCACB[(distancesCACB['i']==jrestype)&(distancesCACB['j']==irestype)]['dist'].values[0]
well_centerCACA = distancesCACA[(distancesCACA['i']==jrestype)&(distancesCACA['j']==irestype)]['dist'].values[0]
well_centerCBCB = distancesCBCB[(distancesCBCB['i']==jrestype)&(distancesCBCB['j']==irestype)]['dist'].values[0]
rnative_matrixCACB[i-1, j-1] = well_centerCACB
rnative_matrixCACB[j-1, i-1] = well_centerCACB
rnative_matrixCACA[i-1, j-1] = well_centerCACA
rnative_matrixCACA[j-1, i-1] = well_centerCACA
rnative_matrixCBCB[i-1, j-1] = well_centerCBCB
rnative_matrixCBCB[j-1, i-1] = well_centerCBCB
import matplotlib.pyplot as plt
plt.imshow(rnative_matrixCACB, origin=0)
# plt.show()
fig = plt.gcf()
directory = f"{pre}/protein/" + pdbID + "/DMP/"
os.system("mkdir -p " + directory)
figureDirectory = f"{directory}/contact.png"
fig.savefig(figureDirectory)
os.system(f"cp {dmp_file} {directory}")
os.system(f"cp {fasta_file} {directory}")
np.savetxt(directory + 'go_rnativeCACB.dat', rnative_matrixCACB, fmt='%10.5f')
np.savetxt(directory + 'go_rnativeCACA.dat', rnative_matrixCACA, fmt='%10.5f')
np.savetxt(directory + 'go_rnativeCBCB.dat', rnative_matrixCBCB, fmt='%10.5f')