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fragsys.py
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fragsys.py
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### IMPORTS ###
import os
import re
import Bio
import math
import scipy
import shutil
import Bio.SeqIO
import importlib
import prointvar
import statistics
import subprocess
import Bio.AlignIO
import numpy as np
import configparser
import pandas as pd
import seaborn as sns
from Bio.Seq import Seq
from Bio import pairwise2
from scipy import cluster
import varalign.alignments
import scipy.stats as stats
import varalign.align_variants
import matplotlib.pyplot as plt
from prointvar.pdbx import PDBXreader
from prointvar.pdbx import PDBXwriter
import matplotlib.patches as mpatches
from prointvar.sifts import SIFTSreader
from prointvar.config import config as cfg
from prointvar.dssp import DSSPrunner, DSSPreader
from scipy.spatial.distance import squareform, pdist
from prointvar.fetchers import download_sifts_from_ebi
from prointvar.fetchers import download_structure_from_pdbe
### DICTIONARIES AND LISTS ###
arpeggio_suffixes = [
"atomtypes", "bs_contacts", "contacts", "specific.sift",
"sift","specific.siftmatch", "siftmatch", "specific.polarmatch",
"polarmatch", "ri", "rings", "ari", "amri", "amam", "residue_sifts"
]
pdb_clean_suffixes = ["break_residues", "breaks"]
simple_ions = [
"ZN", "MN", "CL", "MG", "CD", "NI", "NA", "IOD", "CA", "BR", "XE"
]
acidic_ions = [
"PO4", "ACT", "SO4", "MLI", "CIT", "ACY", "VO4"
]
non_relevant_ligs_manual = [
"DMS", "EDO", "HOH", "TRS", "GOL", "OGA", "FMN", "PG4", "PGR",
"MPD", "TPP", "MES", "PLP", "HYP", "CSO", "UNX", "EPE", "PEG",
"PGE", "DOD", "SUI"
]
non_relevant = non_relevant_ligs_manual + simple_ions + acidic_ions
pdb_resnames = [
"ALA", "CYS", "ASP", "GLU", "PHE", "GLY", "HIS", "ILE", "LYS", "LEU", ### ONE OF THESE MIGHT BE ENOUGH ###
"MET", "ASN", "PRO", "GLN", "ARG", "SER", "THR", "VAL", "TRP", "TYR"
]
aas = [
'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY',
'HIS', 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', ### ONE OF THESE MIGHT BE ENOUGH ###
'THR', 'TRP', 'TYR', 'VAL', 'GLX', 'GLI', 'NLE', 'CYC'
]
bbone = ["N", "CA", "C", "O"]
aa_code = {
"ALA" : 'A', "CYS" : 'C', "ASP" : 'D', "GLU" : 'E',
"PHE" : 'F', "GLY" : 'G', "HIS" : 'H', "ILE" : 'I',
"LYS" : 'K', "LEU" : 'L', "MET" : 'M', "ASN" : 'N',
"PRO" : 'P', "GLN" : 'Q', "ARG" : 'R', "SER" : 'S',
"THR" : 'T', "VAL" : 'V', "TRP" : 'W', "TYR" : 'Y',
"PYL" : 'O', "SEC" : 'U', "HYP" : 'P', "CSO" : 'C', # WEIRD ONES
"SUI" : 'D',
}
### CONFIG FILE READING AND VARIABLE SAVING ###
config = configparser.ConfigParser()
config.read("fragsys_config.txt")
dssp_bin = config["binaries"].get("dssp_bin")
oc_bin = config["binaries"].get("oc_bin")
stamp_bin = config["binaries"].get("stamp_bin")
transform_bin = config["binaries"].get("transform_bin")
clean_pdb_python_bin = config["binaries"].get("clean_pdb_python_bin")
clean_pdb_bin = config["binaries"].get("clean_pdb_bin")
arpeggio_python_bin = config["binaries"].get("arpeggio_python_bin")
arpeggio_bin = config["binaries"].get("arpeggio_bin")
gnomad_vcf = config["dbs"].get("gnomad_vcf")
swissprot = config["dbs"].get("swissprot")
stampdir = config["other"].get("stampdir")
oc_dist = float(config["clustering"].get("oc_dist"))
oc_metric = config["clustering"].get("oc_metric")
oc_method = config["clustering"].get("oc_method")
mes_sig_t = float(config["other"].get("mes_sig_t"))
### PRE-PROCESSING DATA CODE ###
def dump_pickle(data, f_out):
"""
dumps pickle
"""
with open(f_out, "wb") as f:
pickle.dump(data, f)
def load_pickle(f_in):
"""
loads pickle
"""
with open(f_in, "rb") as f:
data = pickle.load(f)
return data
def setup_fragsys_start(main_dir, prot, df):
"""
given a main directory, protein accession and structures dataframe,
downloads all biological assemblies of such structures if they have
not already been downloaded and classified in subdirectories
"""
wd = os.path.join(main_dir, prot)
unsupp_cifs_dir = os.path.join(wd, "unsupp_cifs")
if not os.path.isdir(wd):
os.mkdir(wd)
else:
pass
if not os.path.isdir(unsupp_cifs_dir):
os.mkdir(unsupp_cifs_dir)
else:
pass
prot_df = df.query('entry_uniprot_accession == @prot').copy()
prot_strucs = prot_df.pdb_id.unique().tolist()
struc_files = []
unsupp_cifs_subirs = os.listdir(unsupp_cifs_dir)
if len(unsupp_cifs_subirs) == 0: #it's empty, need to download
pass
else: #not empty
for element in unsupp_cifs_subirs:
element_path = os.path.join(unsupp_cifs_dir, element)
if os.path.isfile(element_path):
pass
elif os.path.isdir(element_path):
element_files = os.listdir(element_path)
for element_file in element_files:
prot_strucs.remove(element_file[:4])
unsupp_file_path = os.path.join(unsupp_cifs_dir, element_file)
if os.path.isfile(unsupp_file_path):
os.remove(unsupp_file_path)
for struc in prot_strucs: #only those that are downloaded or have yet to be (not those classified already)
input_struc = os.path.join(cfg.db_root, cfg.db_pdbx, struc + "_bio.cif")
input_struc_moved = os.path.join(unsupp_cifs_dir, os.path.basename(input_struc))
if os.path.isfile(input_struc) or os.path.isfile(input_struc_moved):
pass
else:
try:
download_structure_from_pdbe(struc, bio = True) # downloads only files missing
except:
print("Error with {}".format(struc))
struc_files.append(input_struc)
for file in struc_files:
if os.path.isfile(file):
shutil.move(file, os.path.join(unsupp_cifs_dir, os.path.basename(file))) #moves downloaded cif files
pdbx_files = os.listdir(os.path.join(cfg.db_root, cfg.db_pdbx))
if len(pdbx_files) != 0:
for pdbx in pdbx_files:
os.remove(os.path.join(cfg.db_root, cfg.db_pdbx, pdbx)) # removes any leftover files?
def setup_dirs(dirs):
"""
sets up the directories for the program to tun
"""
for dirr in dirs:
if os.path.isdir(dirr):
continue
else:
os.mkdir(dirr)
def classify_pdbs(prot, main_dir, h = 0.2, show = False):
"""
Classifies a group of cif files into different
folders according to their amino acid sequence
"""
z, un_seqs, seqs_df = cluster_protein_sequences(prot, main_dir, h, show)
seqs_df = assign_seqs_clusters(z, un_seqs, seqs_df)
create_cluster_dirs(main_dir, prot, seqs_df)
def cluster_protein_sequences(prot, main_dir, h = 0.2, show = False):
"""
Extracts sequences from PDB structures of a certain UniProt accession,
calculates distances between them, genereates matrix, dendrogram and
clustermap.
"""
wd = os.path.join(main_dir, prot)
unsupp_cifs_dir = os.path.join(wd, "unsupp_cifs")
cif_paths = []
for file in os.listdir(unsupp_cifs_dir):
if os.path.isfile(os.path.join(unsupp_cifs_dir, file)): #cifs without classifying
cif_paths.append(os.path.join(unsupp_cifs_dir, file))
elif os.path.isdir(os.path.join(unsupp_cifs_dir, file)): #checking for directories, might have classified PDBs already
for file2 in os.listdir(os.path.join(unsupp_cifs_dir, file)):
cif_paths.append(os.path.join(unsupp_cifs_dir, file, file2))
else:
pass
seqs_df_path = os.path.join(wd, "{}_seqs_df.csv".format(prot))
if os.path.isfile(seqs_df_path):
seqs_df = pd.read_csv(seqs_df_path)
else:
seqs = {}
for cif_path in cif_paths:
seq = get_seq_from_pdb(cif_path, pdb_fmt = "mmcif")
cif_id = cif_path.split("/")[-1][:4]
seqs[cif_id] = seq
seqs_df = pd.DataFrame(list(seqs.items()), index = None, columns = ["pdb_id", "seq"])
seqs_df.to_csv(seqs_df_path, index = False)
un_seqs = seqs_df.drop_duplicates("seq").seq.tolist()
msa_df_out = os.path.join(wd, "{}_msa_dists.csv".format(prot))
if os.path.isfile(msa_df_out):
msa_df_norm = pd.read_csv(msa_df_out)
else:
msa_mat_norm = get_msa_matrix(un_seqs)
msa_df_norm = pd.DataFrame(msa_mat_norm)
msa_df_norm.to_csv(msa_df_out, index = False)
if len(un_seqs) == 1:
return np.array([]), un_seqs, seqs_df
else:
pass
condensed_matrix = scipy.spatial.distance.squareform(msa_df_norm)
z = scipy.cluster.hierarchy.linkage(condensed_matrix, method = "complete")
plot_clustermap(msa_df_norm, z, prot, wd, show)
plot_dendrogram(z, msa_df_norm, wd, prot, h, show)
return z, un_seqs, seqs_df
def get_msa_matrix(seqs_list, norm = True):
"""
Given a list of sequences, calculates all pairwise global sequence alignments
and from the scores, generates a distance from 0-1.
"""
scores = {i: {} for i in range(len(seqs_list))}
for i in range(len(seqs_list)):
scores[i][i] = pairwise2.align.globalxx(seqs_list[i], seqs_list[i])[0][2]
for j in range(i+1, len(seqs_list)):
scores[i][j] = pairwise2.align.globalxx(seqs_list[i], seqs_list[j])[0][2]
scores[j][i] = scores[i][j]
if norm == True:
scores_norm = {i: {} for i in range(len(seqs_list))}
for i in range(len(seqs_list)):
scores_norm[i][i] = abs(scores[i][i] - scores[i][i])/scores[i][i]
for j in range(i+1, len(seqs_list)):
scores_norm[i][j] = abs(scores[i][j] - scores[i][i])/scores[i][i]
scores_norm[j][i] = abs(scores[j][i] - scores[i][i])/scores[i][i]
return scores_norm
else:
return scores
def plot_clustermap(msa_df_norm, linkage, prot, wd, show = False):
"""
Plots a clustermap given a matrix of distances between sequences
"""
plt.rcParams['figure.dpi'] = 300
g = sns.clustermap(
msa_df_norm, row_linkage=linkage, col_linkage=linkage,
cmap = "viridis_r", linewidths = 1, linecolor = "k",
vmin = 0, vmax = 1
)
fig_out = os.path.join(wd, "{}_unique_seqs_clustermap.png".format(prot))
if os.path.isfile(fig_out):
pass
else:
plt.savefig(fig_out)
if show == True:
plt.show()
plt.close()
def plot_dendrogram(z, msa_df_norm, wd, prot, h, show = False):
"""
Plots a dendrogram of the complete linkage hierarchical clustering
calculated from the matrix of distances between sequences
"""
plt.figure(figsize = (7.5, 4), dpi = 300)
d = scipy.cluster.hierarchy.dendrogram(
z, labels = msa_df_norm.index, color_threshold = h
)
plt.title("Complete linkage clustering of unique sequences", fontsize = 15)
plt.xlabel("Unique sequence ID", fontsize = 10)
plt.ylabel("Alignment score distance", fontsize = 10)
plt.axhline(y = 0.2, linestyle = "--", color = "k", linewidth = 1)
fig_out = os.path.join(wd, "{}_unique_seqs_dendrogram.png".format(prot))
if os.path.isfile(fig_out):
pass
else:
plt.savefig(fig_out)
if show == True:
plt.show()
plt.close()
def assign_seqs_clusters(z, unique_seqs, seqs_df, h = 0.2):
"""
Given a dendrogram and group of sequences,
assigns a cluster ID to those sequences
"""
if z.size == 0:
print("This condition is being met") # only one sequence
seqs_df["seq_id"] = [0 for i in range(len(seqs_df))]
seqs_df["cluster_id"] = [0 for i in range(len(seqs_df))]
else:
cutree = scipy.cluster.hierarchy.cut_tree(z, height = h) # clusters coordinates
cluster_ids = [int(cut) for cut in cutree]
un_seqs_dict = {el: i for i, el in enumerate(unique_seqs)}
cluster_id_dict = {i: el for i, el in enumerate(cluster_ids)}
seqs_df["seq_id"] = seqs_df.seq.map(un_seqs_dict)
seqs_df["cluster_id"] = seqs_df.seq_id.map(cluster_id_dict)
return seqs_df
def create_cluster_dirs(main_dir, prot, seqs_df):
"""
Once sequences have been assigned a cluster ID, the .cif files
are divided into subdirectories
"""
unsupp_cifs_dir = os.path.join(main_dir, prot, "unsupp_cifs")
cluster_ids = sorted([str(cluster_id) for cluster_id in seqs_df.cluster_id.unique().tolist()])
unsupp_files = sorted(os.listdir(unsupp_cifs_dir))
if unsupp_files == cluster_ids:
return
for cluster_id in cluster_ids:
cluster_unsupp_dir = os.path.join(unsupp_cifs_dir, str(cluster_id))
if not os.path.isdir(cluster_unsupp_dir):
os.mkdir(cluster_unsupp_dir)
cluster_pdbs = seqs_df[seqs_df.cluster_id == int(cluster_id)].pdb_id.tolist()
cluster_pdb_paths = ["{}_bio.cif".format(cluster_pdb) for cluster_pdb in cluster_pdbs]
for cluster_pdb_path in cluster_pdb_paths:
if os.path.isfile(os.path.join(unsupp_cifs_dir, cluster_pdb_path)):
shutil.move(
os.path.join(unsupp_cifs_dir, cluster_pdb_path),
os.path.join(cluster_unsupp_dir, cluster_pdb_path)
)
def get_swissprot():
"""
Retrieves sequences and their data from Swiss-Prot
:param db: absolute path to a fasta file containing sequences, Swiss-Prot database by default
:type db: str
:returns: dictionary containing the sequence id, description and sequence for all proteins in Swiss-Prot
:rtpe: dict
"""
swissprot_dict = Bio.SeqIO.parse(swissprot, "fasta")
proteins = {}
for protein in swissprot_dict:
acc = protein.id.split("|")[1]
proteins[acc] = {}
proteins[acc]["id"] = protein.id
proteins[acc]["desc"] = protein.description
proteins[acc]["seq"] = protein.seq
return proteins
### STAMP SUPERIMPOSITION CODE ###
def cif2pdb(cif_in, pdb_out):
"""
converts cif to pdb format, needed to run STAMP
"""
cif_df = PDBXreader(inputfile = cif_in).atoms(format_type = "mmcif", excluded=())
w = PDBXwriter(outputfile = pdb_out)
id_equiv_dict = w.run(cif_df, format_type = "pdb", category = 'auth')
return id_equiv_dict
def get_chain_dict(cif_path):
"""
Creates an equivalence dict between asymemtric unit chain IDs and biological assembly chain IDs
"""
cif_df = PDBXreader(inputfile = cif_path).atoms(format_type = "mmcif", excluded=())
orig_chains = cif_df.query('group_PDB == "ATOM"').copy()[['orig_auth_asym_id', 'auth_asym_id']].drop_duplicates().orig_auth_asym_id.tolist()
new_chains = cif_df.query('group_PDB == "ATOM"').copy()[['orig_auth_asym_id', 'auth_asym_id']].drop_duplicates().auth_asym_id.tolist()
chain_dict = {new_chains[i]: orig_chains[i] for i in range(len(orig_chains))}
return chain_dict
def generate_domains(pdbs_dir, domains_out, roi = "ALL"):
"""
Genereates domains file, needed to run STAMP
"""
with open(domains_out, "w+") as fh:
for pdb in os.listdir(pdbs_dir):
fh.write("{} {} {{{}}}\n".format(os.path.join(pdbs_dir, pdb), pdb[:-4], roi))
def stamp(domains, prefix, out):
"""
runs stamp using the domains file as input
"""
if "STAMPDIR" not in os.environ:
os.environ["STAMPDIR"] = stampdir
args = [
stamp_bin, "-l", domains, "-rough", "-n",
str(2), "-prefix", prefix, ">", out
]
exit_code = os.system(" ".join(args))
if exit_code != 0:
print(" ".join(args))
return exit_code
def transform(matrix):
"""
runs transform to obtain set of transformed coordinates
"""
if "STAMPDIR" not in os.environ:
os.environ["STAMPDIR"] = stampdir
#print(stampdir)
args = [transform_bin, "-f", matrix, "-het"]
#print(" ".join(args))
exit_code = os.system(" ".join(args))
if exit_code != 0:
print(" ".join(args))
return exit_code
def move_supp_files(unsupp_pdbs_dir, supp_pdbs_dir):
"""
moves set of supperimposed coordinate files to appropriate directory
"""
struc_files = os.listdir(unsupp_pdbs_dir)
cwd = os.getcwd()
for file in struc_files:
if os.path.isfile(os.path.join(cwd, file)):
shutil.move(
os.path.join(cwd, file),
os.path.join(supp_pdbs_dir, file)
)
def move_stamp_output(wd, prefix, stamp_out_dir):
"""
moves STAMP output files to appropriate directory
"""
cwd = os.getcwd()
stamp_files = sorted([file for file in os.listdir(cwd) if prefix in file]) + ["stamp_rough.trans"]
for file in stamp_files:
filepath = os.path.join(cwd, file)
if os.path.isfile(filepath):
shutil.move(filepath, os.path.join(stamp_out_dir, file))
out_from = os.path.join(wd, prefix + ".out")
out_to = os.path.join(stamp_out_dir, prefix + ".out")
doms_from = os.path.join(wd, prefix + ".domains")
doms_to = os.path.join(stamp_out_dir, prefix + ".domains")
if os.path.isfile(out_from):
shutil.move(out_from, out_to)
if os.path.isfile(doms_from):
shutil.move(doms_from, doms_to)
### DSSP + SIFTS + ARPEGGIO ###
def get_lig_data(supp_pdbs_dir, ligs_df_path):
"""
From a directory containing a set of structurally superimposed pdbs,
writes a csv file indicating the name, chain and residue number of the
ligand(s) of interest in every pdb
"""
ligs_df = pd.DataFrame([])
for struc in os.listdir(supp_pdbs_dir):
struc_path = os.path.join(supp_pdbs_dir, struc)
df = PDBXreader(inputfile = struc_path).atoms(format_type = "pdb", excluded=())
hetatm_df = df.query('group_PDB == "HETATM"').copy()
ligs = hetatm_df.label_comp_id.unique().tolist()
lois = [lig for lig in ligs if lig not in non_relevant]
for loi in lois:
loi_df = hetatm_df.query('label_comp_id == @loi').copy()
lois_df_un = loi_df.copy().drop_duplicates(["label_comp_id", "label_asym_id"])[["label_comp_id", "label_asym_id", "auth_seq_id"]]
lois_df_un["struc_name"] = struc
ligs_df = ligs_df.append(lois_df_un)
ligs_df = ligs_df[["struc_name","label_comp_id", "label_asym_id", "auth_seq_id"]]
ligs_df.to_csv(ligs_df_path, index = False)
return ligs_df
def run_dssp(struc, supp_pdbs_dir, dssp_dir):
"""
runs DSSP, saves and return resulting output dataframe
"""
dssp_csv = os.path.join(dssp_dir, "dssp_" + struc.replace("pdb", "csv")) # output csv filepath
dssp_out = os.path.join(dssp_dir, struc.replace("pdb", "dssp"))
struc_in = os.path.join(supp_pdbs_dir, struc)
DSSPrunner(inputfile = struc_in, outputfile = dssp_out).write() # runs DSSP
dssp_data = DSSPreader(inputfile = dssp_out).read() # reads DSSP output
dssp_data = dssp_data.rename(index = str, columns = {"RES": "PDB_ResNum"})
dssp_data.PDB_ResNum = dssp_data.PDB_ResNum.astype(str)
dssp_cols = ["PDB_ResNum", "SS", "ACC", "KAPPA", "ALPHA", "PHI", "PSI", "RSA"] # selects subset of columns
dssp_data.to_csv(dssp_csv, index = False)
return dssp_data[dssp_cols]
def process_sifts_data(input_sifts, sifts_dir, pdb_id):
"""
processes SIFTS table, saves and returns the processed tables
"""
sifts_data = SIFTSreader(inputfile = input_sifts).read() # read sifts data
sifts_mapping = sifts_data[[
"UniProt_dbResNum", "UniProt_dbResName", "PDB_dbResName",
"PDB_dbResNum", "PDB_dbChainId"
]] # subsetting sifts table
sifts_mapping = sifts_mapping.rename(index = str, columns = {
"UniProt_dbResNum":"UniProt_ResNum", "UniProt_dbResName":"UniProt_ResName",
"PDB_dbResName":"PDB_ResName", "PDB_dbResNum":"PDB_ResNum",
"PDB_dbChainId":"PDB_ChainID"
}) # renaming table columns
try:
sifts_mapping = sifts_mapping[sifts_mapping.UniProt_ResNum != "null"]
except:
pass
try:
sifts_mapping = sifts_mapping[~sifts_mapping.UniProt_ResNum.isnull()]
except:
pass
sifts_mapping = sifts_mapping[sifts_mapping.PDB_ResNum != "null"]
sifts_mapping.UniProt_ResNum = sifts_mapping.UniProt_ResNum.astype(int)
sifts_mapping.PDB_ResNum = sifts_mapping.PDB_ResNum.astype(str)
sifts_csv = os.path.join(sifts_dir, "sifts_" + pdb_id + ".csv") # sifts output csv file
sifts_data.to_csv(sifts_csv, index = False) # write sifts table to csv
sifts_csv2 = os.path.join(sifts_dir, "sifts_mapping_" + pdb_id + ".csv")
sifts_mapping.to_csv(sifts_csv2, index = False)
shutil.move(input_sifts, os.path.join(sifts_dir, os.path.basename(input_sifts)))
return sifts_data, sifts_mapping
### ARPEGGIO ###
def run_clean_pdb(pdb_path):
"""
runs pdb_clean.py to prepare files for arpeggio
"""
args = [
clean_pdb_python_bin, clean_pdb_bin, pdb_path
]
#print(args)
exit_code = os.system(" ".join(args))
if exit_code != 0:
print(" ".join(args))
return exit_code
def run_arpeggio(pdb_path, lig_name, dist = 5):
"""
runs Arpeggio
"""
args = [
arpeggio_python_bin, arpeggio_bin, pdb_path, "-s",
"RESNAME:{}".format(lig_name), "-i", str(dist), "-v"
]
exit_code = os.system(" ".join(args))
if exit_code != 0:
print(" ".join(args))
return exit_code
def move_arpeggio_output(wd, subdir, strucs, supp_pdbs_subdir, clean_pdbs_subdir, struc2ligs):
"""
moves pdb_clean.py and Arpeggio output files to appropriate directory
"""
for struc in strucs:
clean_struc = struc.replace(".pdb", ".clean.pdb")
clean_struc_path_from = os.path.join(supp_pdbs_subdir, clean_struc)
clean_struc_path_to = os.path.join(clean_pdbs_subdir, clean_struc)
if os.path.isfile(clean_struc_path_from):
shutil.move(clean_struc_path_from, clean_struc_path_to)
for arpeggio_suff in arpeggio_suffixes:
for the_lig in struc2ligs[struc]:
arpeggio_file_from_supp = os.path.join(supp_pdbs_subdir, struc[:-3] + "clean_{}".format(the_lig) + "." + arpeggio_suff)
arpeggio_file_to = os.path.join(wd, "results/arpeggio/", subdir, struc[:-3] + "clean_{}".format(the_lig) + "." + arpeggio_suff)
arpeggio_file_from_clean = os.path.join(clean_pdbs_subdir, struc[:-3] + "clean_{}".format(the_lig) + "." + arpeggio_suff)
if os.path.isfile(arpeggio_file_from_supp):
shutil.move(arpeggio_file_from_supp, arpeggio_file_to)
elif os.path.isfile(arpeggio_file_from_clean):
shutil.move(arpeggio_file_from_clean, arpeggio_file_to)
for pdb_clean_suff in pdb_clean_suffixes:
pdb_clean_file_from = os.path.join(supp_pdbs_subdir, struc + "." + pdb_clean_suff)
pdb_clean_file_to = os.path.join(wd, "results/pdb_clean/", subdir, struc + "." + pdb_clean_suff)
if os.path.isfile(pdb_clean_file_from):
shutil.move(pdb_clean_file_from, pdb_clean_file_to)
elif os.path.isfile(os.path.join(wd, "results/pdb_clean", struc + "." + pdb_clean_suff)):
shutil.move(os.path.join(wd, "results/pdb_clean", struc + "." + pdb_clean_suff), pdb_clean_file_to)
def process_arpeggio(struc, all_ligs, clean_pdbs_dir, arpeggio_dir, sifts_dir, bio2asym_chain_dict, cif2pdb_chain_dict):
"""
processes arpeggio output to generate the two tables that will
be used later in the analysis
"""
lig_cons_splits = []
arpeggio_lig_conss = []
for lig in all_ligs:
arpeggio_out_path_lig = os.path.join(arpeggio_dir, struc.replace("pdb", "clean_{}.bs_contacts".format(lig)))
all_cons_lig = pd.read_table(arpeggio_out_path_lig, header = None)
lig_cons_split_lig = reformat_arpeggio(all_cons_lig)
lig_cons_split_lig = add_resnames_to_arpeggio_table(struc, clean_pdbs_dir, lig_cons_split_lig)
lig_cons_split_lig = ligand_to_atom2(lig_cons_split_lig, lig)
lig_cons_split_lig["contact_type"] = lig_cons_split_lig.apply(lambda row: contact_type(row), axis = 1)
arpeggio_lig_cons_lig = lig_cons_split_lig.sort_values(by = ["Chain (Atom1)", "ResNum (Atom1)"])
arpeggio_lig_cons_lig = arpeggio_lig_cons_lig[["ResNum (Atom1)","Chain (Atom1)", 'ResName (Atom1)']]
arpeggio_lig_cons_lig = arpeggio_lig_cons_lig.drop_duplicates(subset = ["ResNum (Atom1)", "Chain (Atom1)"])
arpeggio_lig_cons_lig = arpeggio_lig_cons_lig.rename(index = str, columns = {"ResNum (Atom1)": "PDB_ResNum", "Chain (Atom1)": "PDB_ChainID"})
arpeggio_lig_cons_lig = arpeggio_lig_cons_lig.astype({"PDB_ResNum": int})
lig_cons_splits.append(lig_cons_split_lig)
arpeggio_lig_conss.append(arpeggio_lig_cons_lig)
lig_cons_split = pd.concat(lig_cons_splits)
arpeggio_lig_cons = pd.concat(arpeggio_lig_conss)
########################### ADDED TO AVOID INCORRECT BS DEFINITION DUE TO PDB RESNUMS ###########################
pdb_id = struc[:4]
sifts_mapping = pd.read_csv(os.path.join(sifts_dir, "sifts_mapping_{}.csv".format(pdb_id)))
sifts_dict = {}
for chain, chain_df in sifts_mapping.groupby("PDB_ChainID"):
for i, row in chain_df.iterrows():
sifts_dict[(chain, str(row["PDB_ResNum"]))] = row["UniProt_ResNum"]
sifts_dict = extend_sifts_mapping_dict(sifts_dict, bio2asym_chain_dict, cif2pdb_chain_dict)
lig_cons_split['ResNum (Atom1)'] = lig_cons_split['ResNum (Atom1)'].astype(str)
arpeggio_lig_cons['PDB_ResNum'] = arpeggio_lig_cons['PDB_ResNum'].astype(str)
lig_cons_split["UniProt_Resnum"] = lig_cons_split.set_index(['Chain (Atom1)', 'ResNum (Atom1)']).index.map(sifts_dict.get)
arpeggio_lig_cons["UniProt_Resnum"] = arpeggio_lig_cons.set_index(['PDB_ChainID', 'PDB_ResNum']).index.map(sifts_dict.get)
########################### ADDED TO AVOID INCORRECT BS DEFINITION DUE TO PDB RESNUMS ###########################
old_len1 = len(arpeggio_lig_cons)
old_len2 = len(lig_cons_split)
lig_cons_split = lig_cons_split[~lig_cons_split.UniProt_Resnum.isnull()].copy()
arpeggio_lig_cons = arpeggio_lig_cons[~arpeggio_lig_cons.UniProt_Resnum.isnull()].copy()
new_len1 = len(arpeggio_lig_cons)
new_len2 = len(lig_cons_split)
if new_len1 != old_len1:
print("WARNING!!! {} residues lacked mapping from PDB to UniProt at arpeggio_lig_cons for {}".format(new_len1-old_len1, pdb_id))
if new_len2 != old_len2:
print("WARNING!!! {} residues lacked mapping from PDB to UniProt at lig_cons_split for {}".format(new_len2-old_len2, pdb_id))
########################### ADDED TO AVOID INCORRECT BS DEFINITION DUE TO LACKING MAPPING TO PDB RESNUMS ###########################
lig_cons_split.to_csv(os.path.join(arpeggio_dir, "arpeggio_all_cons_split_" + struc[:4] + ".csv"), index = False)
arpeggio_lig_cons.to_csv(os.path.join(arpeggio_dir, "arpeggio_lig_cons_" + struc[:4] + ".csv"), index = False)
return lig_cons_split, arpeggio_lig_cons
def reformat_arpeggio(arpeggio_df):
"""
starts formatting arpeggio table
"""
arpeggio_df.columns = [
'Atom_1', 'Atom_2', 'Clash', 'Covalent', 'VdW Clash', 'Vdw', 'Proximal', 'Hydrogen Bond',
'Weak Hydrogen Bond', 'Halogen bond', 'Ionic', 'Metal Complex', 'Aromatic', 'Hydrophobic',
'Carbonyl', 'Polar', 'Weak Polar', 'Atom proximity', 'Vdw proximity', 'Interacting entities'
]
lig_cons = arpeggio_df.loc[arpeggio_df["Interacting entities"] == "INTER"].copy() # Selecting only the interactions between our specified atoms (i.e ligands) and other selections
lig_cons = lig_cons.sort_values(by = ["Atom_1"])
split_atom1 = lig_cons.Atom_1.str.split("/", expand = True) # splits atom1 column into three new columns: chain, resnum and atom
split_atom1.columns = ["Chain (Atom1)", "ResNum (Atom1)", "Atom (Atom1)"]
split_atom2 = lig_cons.Atom_2.str.split("/", expand = True) # splits atom2 column into three new columns: chain, resnum and atom
split_atom2.columns = ["Chain (Atom2)", "ResNum (Atom2)", "Atom (Atom2)"]
lig_cons_split = pd.merge(split_atom2, lig_cons, left_index = True, right_index = True) # Making a table of the contacts, but with the atom identifier split into chain, resnum, and atom
lig_cons_split = pd.merge(split_atom1, lig_cons_split, left_index = True, right_index = True) # Making a table of the contacts, but with the atom identifier split into chain, resnum, and atom
lig_cons_split = lig_cons_split.drop(axis = 1, labels = ["Atom_1", "Atom_2"])
lig_cons_split["ResNum (Atom1)"] = lig_cons_split["ResNum (Atom1)"].astype(int)
lig_cons_split["ResNum (Atom2)"] = lig_cons_split["ResNum (Atom2)"].astype(int)
return lig_cons_split
def add_resnames_to_arpeggio_table(structure, clean_pdbs_dir, arpeggio_cons_split):
"""
adds residue names to arpeggio table, needed for later table mergings
"""
structure_path = os.path.join(clean_pdbs_dir, structure.replace("pdb", "clean.pdb"))
pdb_structure = PDBXreader(inputfile = structure_path).atoms(format_type = "pdb", excluded=())
resnames_dict = {(row.label_asym_id, int(row.label_seq_id)): row.label_comp_id for index, row in pdb_structure.drop_duplicates(['label_asym_id', 'label_seq_id']).iterrows()}
arpeggio_cons_split["ResName (Atom1)"] = arpeggio_cons_split.set_index(["Chain (Atom1)", "ResNum (Atom1)"]).index.map(resnames_dict.get)
arpeggio_cons_split["ResName (Atom2)"] = arpeggio_cons_split.set_index(["Chain (Atom2)", "ResNum (Atom2)"]).index.map(resnames_dict.get)
return arpeggio_cons_split
def ligand_to_atom2(lig_cons_split, lig):
"""
formats arpeggio table so that the ligand atoms are always Atom2
"""
ordered_cols = [
'Chain (Atom1)', 'ResNum (Atom1)', 'ResName (Atom1)', 'Atom (Atom1)',
'Chain (Atom2)', 'ResNum (Atom2)', 'ResName (Atom2)', 'Atom (Atom2)',
'Clash', 'Covalent', 'VdW Clash', 'Vdw', 'Proximal', 'Hydrogen Bond',
'Weak Hydrogen Bond', 'Halogen bond', 'Ionic', 'Metal Complex', 'Aromatic',
'Hydrophobic', 'Carbonyl', 'Polar', 'Weak Polar','Atom proximity',
'Vdw proximity', 'Interacting entities'
]
lig_is_atom1 = lig_cons_split[lig_cons_split["ResName (Atom1)"] == lig].copy().sort_values("ResNum (Atom2)")
lig_is_atom2 = lig_cons_split[lig_cons_split["ResName (Atom2)"] == lig].copy().sort_values("ResNum (Atom1)")
lig_is_atom1.rename(columns = {
'Chain (Atom1)': 'Chain (Atom2)', 'Chain (Atom2)': 'Chain (Atom1)',
'ResNum (Atom1)': 'ResNum (Atom2)', 'ResNum (Atom2)': 'ResNum (Atom1)',
'Atom (Atom1)': 'Atom (Atom2)', 'Atom (Atom2)': 'Atom (Atom1)',
'ResName (Atom1)': 'ResName (Atom2)', 'ResName (Atom2)': 'ResName (Atom1)'
}, inplace = True)
lig_cons_split_rf = pd.concat([lig_is_atom1[ordered_cols], lig_is_atom2[ordered_cols]]) # new dataframe so ligand is always atom2
lig_cons_split_rf = lig_cons_split_rf[lig_cons_split_rf["ResName (Atom1)"].isin(aas)]
return lig_cons_split_rf
def extend_sifts_mapping_dict(sifts_dict, bio2asym_chain_dict, cif2pdb_chain_dict):
"""
extends PDB-UniProt residue number mapping so ALL chains in preferred biological assembly
are present and a UniProt residue number can be obtained
"""
unique_sifts_chains = list(set([k[0] for k in list(sifts_dict.keys())]))
for bio_chain, asym_chain in bio2asym_chain_dict.items():
if bio_chain not in unique_sifts_chains:
asym_chain_ks = [k for k in list(sifts_dict.keys()) if k[0] == asym_chain]
for asym_chain_k in asym_chain_ks:
sifts_dict[(cif2pdb_chain_dict[bio_chain], asym_chain_k[1])] = sifts_dict[asym_chain_k]
return sifts_dict
def contact_type(row):
"""
determines whether a row is backbone or sidechain contact
"""
if row["Atom (Atom1)"] in bbone:
return "backbone"
else:
return "sidechain"
### BINDING SITE DEFINITION ###
def def_bs_oc(results_dir, pdb_files, prot, subdir, lig_names, bs_def_out, attr_out, chimera_script_out, arpeggio_dir, metric = oc_metric, dist = oc_dist, method = oc_method, alt_fmt = False):
"""
given a set of pdb structures, and other arguments, clusters ligands in space,
defines binding sites and writes chimera attribute files and chimera script to
format the superimposed structures to facilitate visualisation
alt_fmt is a boolean I added so it works with slightly different input. Still PDB
files, but some which coordinates were transformed using PDBe-KB transformation
matrices. They have different nomenclature and therefore indices to get pdb_files_dict
must be different
"""
lig_data_df, labs = generate_ligs_res_df(arpeggio_dir, alt_fmt = alt_fmt)
if len(lig_data_df) == 1: #should only happen in the case of only one LOI
lig_data_df["binding_site"] = 0
else:
dis_out = os.path.join(results_dir, "{}_{}_{}.dis".format(prot, subdir, metric))
get_dis_file(lig_data_df, labs, dis_out, metric = metric)
ocout, ec = oc(dis_out, method = method, cut_t = dist)
oc_dict = oc2dict(ocout)
cluster_id_dict = {}
for k, v in oc_dict.items():
for member in v["members"]:
cluster_id_dict[member] = v["new_id"]
sample_colors = list(itertools.islice(rgbs(), 200)) # new_colours
lig_data_df["lab"] = labs
lig_data_df["binding_site"] = lig_data_df.lab.map(cluster_id_dict)
if alt_fmt == True:
pdb_files_dict = {f[-16:-10]: f.split("/")[-1] for f in pdb_files}
lig_data_df["pdb_id2"] = lig_data_df.pdb_id + "_" + lig_data_df.lig_chain
lig_data_df["pdb_path"] = lig_data_df.pdb_id2.map(pdb_files_dict)
else:
pdb_files_dict = {f[-18:-14]: f.split("/")[-1] for f in pdb_files}
lig_data_df["pdb_path"] = lig_data_df.pdb_id.map(pdb_files_dict)
write_bs_files(lig_data_df, bs_def_out, attr_out, chimera_script_out)
return lig_data_df, labs
def generate_ligs_res_df(arpeggio_dir, alt_fmt = False):
"""
Given a directory containing processed Arpeggio output files,
returns a dataset containing information about the ligands of
interest binding the protein and their labels.
"""
lig_files = sorted([file for file in os.listdir(arpeggio_dir) if file.startswith("arpeggio_all_cons_split")])
pdbs, ligs, resnums, chains, ligs_ress = [[], [], [], [], []]
for file in lig_files:
if alt_fmt == True:
pdb_id = file[-10:-6]
else:
pdb_id = file[-8:-4]
df = pd.read_csv(os.path.join(arpeggio_dir, file))
for lig, lig_df in df.groupby(["ResName (Atom2)", "ResNum (Atom2)", "Chain (Atom2)"]):
lig_ress = lig_df["UniProt_Resnum"].unique().tolist()
pdbs.append(pdb_id)
ligs.append(lig[0])
resnums.append(lig[1])
chains.append(lig[2])
ligs_ress.append(lig_ress)
lig_data_df = pd.DataFrame(list(zip(pdbs, ligs, resnums, chains, ligs_ress)), columns = ["pdb_id", "lig_name", "lig_resnum", "lig_chain", "binding_res"])
labs = [pdbs[i] + "_" + str(ligs[i]) + "_" + str(resnums[i]) + "_" + str(chains[i]) for i in range(len(ligs))]
return lig_data_df, labs
def get_dis_file(lig_data_df, labs, out, metric = oc_metric):
"""
creates dis file to be fed to OC
"""
lig_res = lig_data_df.binding_res.tolist() #this is a list of lists, each list contains residue numbers interacting with ligand
if metric == "i_rel":
intersect_dict = get_intersect_rel_matrix(lig_res)
elif metric == "i_abs":
intersect_dict = get_intersect_matrix(lig_res)
n_ligs = len(lig_res)
with open(out, "w+") as fh:
fh.write(str(n_ligs) + "\n")
for lab in labs: #labs contain unique identifier for a ligand
fh.write(lab + "\n")
for i in range(n_ligs):
for j in range(i+1, n_ligs):
fh.write(str(intersect_dict[i][j]) + "\n")
def get_intersect_rel_matrix(binding_ress):
"""
Given a set of ligand binding residues, calcualtes a
similarity matrix between all the different sets of ligand
binding residues.
"""
inters = {i: {} for i in range(len(binding_ress))}
for i in range(len(binding_ress)):
inters[i][i] = intersection_rel(binding_ress[i], binding_ress[i])
for j in range(i+1, len(binding_ress)):
inters[i][j] = intersection_rel(binding_ress[i], binding_ress[j])
inters[j][i] = inters[i][j]
return inters
def intersection_rel(l1, l2):
"""
Calculates relative intersection.
"""
len1 = len(l1)
len2 = len(l2)
I_max = min([len1, len2])
I = len(list(set(l1).intersection(l2)))
return I/I_max
def get_intersect_matrix(binding_ress):
"""
Given a set of ligand binding residues, calcualtes a
similarity matrix between all the different sets of ligand
binding residues.
"""
inters = {i: {} for i in range(len(binding_ress))}
for i in range(len(binding_ress)):
inters[i][i] = intersection(binding_ress[i], binding_ress[i])
for j in range(i+1, len(binding_ress)):
inters[i][j] = intersection(binding_ress[i], binding_ress[j])
inters[j][i] = inters[i][j]
return inters
def intersection(l1, l2):
"""
Calculates intersection.
"""
I = len(list(set(l1).intersection(l2)))
return I
def oc(oc_in, type_mat = "sim", method = oc_method, cut_t = oc_dist):
"""
runs OC and returns exit code, should be 0 if all is ok
"""
oc_out = oc_in.replace(".dis", "_{}_{}_cut_{}.ocout".format(type_mat, method, cut_t))
args = [
oc_bin, type_mat, method, "id", "cut", str(cut_t),
"ps", oc_out[:-6], "<", oc_in, ">", oc_out
]
exit_code = os.system(" ".join(args))
return oc_out, exit_code
def oc2dict(ocout):
"""
parses OC output to generate dict
"""
oc_dict = {}
re_cluster = re.compile("""##\s+(\d+)\s(\d+\.*\d*)\s(\d+)\s*""")
with open (ocout, "r") as fh:
s = 0
n_singleton = 0
n_cluster = 0
for line in fh:
if s == 0:
if line.startswith("##"):
m = re_cluster.match(line)
cluster_id, score, cluster_size = m.groups()
oc_dict[cluster_id] = {"score": float(score), "size": int(cluster_size), "new_id": n_cluster}
n_cluster += 1
elif line.startswith(" "):
oc_dict[cluster_id]["members"] = line.strip().split(" ")
else:
if line.strip() == "":
pass
elif line.strip() == "UNCLUSTERED ENTITIES":
s = 1
else:
oc_dict["singleton_{}".format(n_singleton)] = {"score": "", "size": 1, "members": [line.strip(),], "new_id": n_cluster}
n_singleton += 1
n_cluster += 1
return oc_dict
def write_bs_files(frag_mean_coords, bs_def_out, attr_out, chimera_script_out):
"""
doc
"""
frag_mean_coords = frag_mean_coords.dropna()
frag_mean_coords.binding_site = frag_mean_coords.binding_site.astype(int)
frag_mean_coords.lig_resnum = frag_mean_coords.lig_resnum.astype(int)
chimera_atom_spec = (':'+ frag_mean_coords.lig_resnum.astype(str) +
'.'+ frag_mean_coords.lig_chain +
'&#/name==' + frag_mean_coords.pdb_path)
frag_mean_coords = frag_mean_coords.assign(chimera_atom_spec = chimera_atom_spec)
frag_mean_coords.to_csv(bs_def_out, index = False) # saves table to csv
write_bs_attribute_file(frag_mean_coords, attr_out)
bs_labs = frag_mean_coords.binding_site.unique().tolist()
write_chimera_script(chimera_script_out, bs_labs)
def write_bs_attribute_file(clustered_fragments, attr_out):
"""
writes Chimera attribute file to later colour ligands
according to the binding site they bind to
"""
with open(attr_out, "w") as out:
out.write("attribute: binding_site\n")
out.write("match mode: 1-to-1\n")
out.write("recipient: residues\n")
out.write("\n".join("\t" + clustered_fragments.chimera_atom_spec.values + "\t" + clustered_fragments.binding_site.astype(str)))
def write_chimera_script(chimera_script_out, bs_labels):
"""
writes Chimera script that will format the superimposed structures
as well as colour ligands according to their binding site
"""
sample_colors = list(itertools.islice(rgbs(), 200)) # new_colours
chimera_rgb_string = [','.join(map(str, rgb)) for rgb in sample_colors]
cmds = [
"~rib", "rib #0", "ksdssp", "set silhouette", "set silhouettewidth 3",
"background solid white", "~dis", "sel ~@/color=white", "dis sel", "namesel lois",
"~sel"
]
with open(chimera_script_out, 'w') as out:
out.write('# neutral colour for everything not assigned a cluster\n')
out.write('colour white\n')
out.write('# colour each binding site\n')
for i in range(0, len(bs_labels)):
out.write('colour {} :/binding_site=={}\n'.format(','.join(list(map(str, list(sample_colors[i])))), i))
out.write("### SOME FORMATTING ###\n")
out.write("\n".join(cmds))
print("Chimera script successfully created!")
### CONSERVATION AND VARIATION ###
def create_alignment_from_struc(example_struc, fasta_path, pdb_fmt = "pdb", n_it = 3, seqdb = swissprot):
"""
given an example structure, creates and reformats an MSA
"""
create_fasta_from_seq(get_seq_from_pdb(example_struc, pdb_fmt = pdb_fmt), fasta_path) # CREATES FASTA FILE FROM PDB FILE
hits_out = fasta_path.replace("fa", "out")
hits_aln = fasta_path.replace("fa", "sto")
hits_aln_rf = fasta_path.replace(".fa", "_rf.sto")
jackhmmer(fasta_path, hits_out, hits_aln , n_it = n_it, seqdb = seqdb,) # RUNS JACKHAMMER USING AS INPUT THE SEQUENCE FROM THE PDB AND GENERATES ALIGNMENT
add_acc2msa(hits_aln, hits_aln_rf)
def get_seq_from_pdb(pdb_path, pdb_fmt = "mmcif"): # MIGHT NEED TO BE MORE SELECTIVE WITH CHAIN, ETC
"""
generates aa sequence string from a pdb coordinates file
"""
struc = PDBXreader(pdb_path).atoms(format_type = pdb_fmt, excluded=())
return "".join([aa_code[aa] for aa in struc[struc.group_PDB == "ATOM"].drop_duplicates(["auth_seq_id"]).auth_comp_id.tolist()])