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#!/usr/bin/python
# coding: utf-8
# Author: LE YUAN
# Date: 2020-10-01
import os
import math
import model
import torch
import json
import pickle
import numpy as np
from rdkit import Chem
from Bio import SeqIO
from collections import defaultdict
from scipy import stats
fingerprint_dict = model.load_pickle('../../Data/input/fingerprint_dict.pickle')
atom_dict = model.load_pickle('../../Data/input/atom_dict.pickle')
bond_dict = model.load_pickle('../../Data/input/bond_dict.pickle')
edge_dict = model.load_pickle('../../Data/input/edge_dict.pickle')
word_dict = model.load_pickle('../../Data/input/sequence_dict.pickle')
def split_sequence(sequence, ngram):
sequence = '-' + sequence + '='
# print(sequence)
# words = [word_dict[sequence[i:i+ngram]] for i in range(len(sequence)-ngram+1)]
words = list()
for i in range(len(sequence)-ngram+1) :
try :
words.append(word_dict[sequence[i:i+ngram]])
except :
word_dict[sequence[i:i+ngram]] = 0
words.append(word_dict[sequence[i:i+ngram]])
return np.array(words)
# return word_dict
def create_atoms(mol):
"""Create a list of atom (e.g., hydrogen and oxygen) IDs
considering the aromaticity."""
# atom_dict = defaultdict(lambda: len(atom_dict))
atoms = [a.GetSymbol() for a in mol.GetAtoms()]
# print(atoms)
for a in mol.GetAromaticAtoms():
i = a.GetIdx()
atoms[i] = (atoms[i], 'aromatic')
atoms = [atom_dict[a] for a in atoms]
# atoms = list()
# for a in atoms :
# try:
# atoms.append(atom_dict[a])
# except :
# atom_dict[a] = 0
# atoms.append(atom_dict[a])
return np.array(atoms)
def create_ijbonddict(mol):
"""Create a dictionary, which each key is a node ID
and each value is the tuples of its neighboring node
and bond (e.g., single and double) IDs."""
# bond_dict = defaultdict(lambda: len(bond_dict))
i_jbond_dict = defaultdict(lambda: [])
for b in mol.GetBonds():
i, j = b.GetBeginAtomIdx(), b.GetEndAtomIdx()
bond = bond_dict[str(b.GetBondType())]
i_jbond_dict[i].append((j, bond))
i_jbond_dict[j].append((i, bond))
return i_jbond_dict
def extract_fingerprints(atoms, i_jbond_dict, radius):
"""Extract the r-radius subgraphs (i.e., fingerprints)
from a molecular graph using Weisfeiler-Lehman algorithm."""
# fingerprint_dict = defaultdict(lambda: len(fingerprint_dict))
# edge_dict = defaultdict(lambda: len(edge_dict))
if (len(atoms) == 1) or (radius == 0):
fingerprints = [fingerprint_dict[a] for a in atoms]
else:
nodes = atoms
i_jedge_dict = i_jbond_dict
for _ in range(radius):
"""Update each node ID considering its neighboring nodes and edges
(i.e., r-radius subgraphs or fingerprints)."""
fingerprints = []
for i, j_edge in i_jedge_dict.items():
neighbors = [(nodes[j], edge) for j, edge in j_edge]
fingerprint = (nodes[i], tuple(sorted(neighbors)))
# fingerprints.append(fingerprint_dict[fingerprint])
# fingerprints.append(fingerprint_dict.get(fingerprint))
try :
fingerprints.append(fingerprint_dict[fingerprint])
except :
fingerprint_dict[fingerprint] = 0
fingerprints.append(fingerprint_dict[fingerprint])
nodes = fingerprints
"""Also update each edge ID considering two nodes
on its both sides."""
_i_jedge_dict = defaultdict(lambda: [])
for i, j_edge in i_jedge_dict.items():
for j, edge in j_edge:
both_side = tuple(sorted((nodes[i], nodes[j])))
# edge = edge_dict[(both_side, edge)]
# edge = edge_dict.get((both_side, edge))
try :
edge = edge_dict[(both_side, edge)]
except :
edge_dict[(both_side, edge)] = 0
edge = edge_dict[(both_side, edge)]
_i_jedge_dict[i].append((j, edge))
i_jedge_dict = _i_jedge_dict
return np.array(fingerprints)
def create_adjacency(mol):
adjacency = Chem.GetAdjacencyMatrix(mol)
return np.array(adjacency)
def dump_dictionary(dictionary, filename):
with open(filename, 'wb') as file:
pickle.dump(dict(dictionary), file)
def load_tensor(file_name, dtype):
return [dtype(d).to(device) for d in np.load(file_name + '.npy', allow_pickle=True)]
def get_refSeq() :
# get the protein sequence accoding to protein sequence id
# Note that the file 343taxa_proteins.fasta could be downloaded from url: https://figshare.com/articles/dataset/Tempo_and_mode_of_genome_evolution_in_the_budding_yeast_subphylum/5854692?file=13083299
# Then change the following directory to the directory of 343 protein fasta file
with open("/directory/to/343taxa_proteins.fasta", "r") as handleGene :
proteinSeq = dict()
for record in SeqIO.parse(handleGene, "fasta") :
# ['__add__', '__bool__', '__class__', '__contains__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__',
# '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__le___', '__len__', '__lt__',
# '__module__', '__ne__', '__new__', '__nonzero__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__',
# '__subclasshook__', '__weakref__', '_per_letter_annotations', '_seq', '_set_per_letter_annotations', '_set_seq', 'annotations', 'dbxrefs', 'description',
# 'features', 'format', 'id', 'letter_annotations', 'lower', 'name', 'reverse_complement', 'seq', 'translate', 'upper']
# if record.id.startswith("Candida_albicans") :
# if record.id == gene :
proteinSeq[record.id] = str(record.seq)
# print("The protein number of %s is: %d" % (gene,len(proteinSeq)))
return proteinSeq
def get_organisms() :
filenames = os.listdir('../species/MLKCATRESULT/')
filenames = [filename.split('ForKcat')[0] for filename in filenames if filename.endswith('.txt')]
print(len(filenames)) # 343
# print(filenames[:3]) # ['yHMPu5000035645_Yarrowia_divulgata', 'Saccharomyces_uvarum', 'Cyberlindnera_jadinii']
return filenames
class Predictor(object):
def __init__(self, model):
self.model = model
def predict(self, data):
predicted_value = self.model.forward(data)
return predicted_value
def main() :
proteinSeq = get_refSeq()
fingerprint_dict = model.load_pickle('../../Data/input/fingerprint_dict.pickle')
atom_dict = model.load_pickle('../../Data/input/atom_dict.pickle')
bond_dict = model.load_pickle('../../Data/input/bond_dict.pickle')
edge_dict = model.load_pickle('../../Data/input/edge_dict.pickle')
word_dict = model.load_pickle('../../Data/input/sequence_dict.pickle')
n_fingerprint = len(fingerprint_dict)
n_word = len(word_dict)
n_edge = len(edge_dict)
radius=2
ngram=3
dim=10
# dim=5
layer_gnn=3
side=5
window=11
layer_cnn=3
layer_output=3
lr=1e-3
lr_decay=0.5
decay_interval=10
weight_decay=1e-6
iteration=100
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
# torch.manual_seed(1234)
Kcat_model = model.KcatPrediction(device, n_fingerprint, n_word, 2*dim, layer_gnn, window, layer_cnn, layer_output).to(device)
Kcat_model.load_state_dict(torch.load('../../Results/output/all--radius2--ngram3--dim20--layer_gnn3--window11--layer_cnn3--layer_output3--lr1e-3--lr_decay0.5--decay_interval10--weight_decay1e-6--iteration30', map_location=device))
# print(state_dict.keys())
# model.eval()
predictor = Predictor(Kcat_model)
print('It\'s time to start the prediction!')
print('-----------------------------------')
organisms = get_organisms()
i = 0
for organism in organisms :
# if organism == 'Saccharomyces_paradoxus' : # Saccharomyces_cerevisiae
i += 1
print('This is', i, '---------------------------------------')
print(organism)
# with open('../species/MLKCATRESULT/%sForKcatPrediction.txt' % organism, 'r') as infile :
with open('../../Data/input/kcatpredictionfile/%sForKcatPrediction.txt' % organism, 'r') as infile :
lines = infile.readlines()
print(len(lines)) # 6291
print(lines[:2])
print('--'*20+'\n')
# Create the directory '343 species' under the 'Results/output' directory
# The generated prediction results for 343 species are stored in our zenodo: https://doi.org/10.5281/zenodo.5797013
file =open('../../Results/output/343species/%s_PredictionResults.txt' % organism, 'w')
file.write(lines[0].strip() + '\t%s\n' % 'Kcat value (substrate first)')
for line in lines[1:] : # [1:]
data = line.strip('\n').split('\t')
# print(data)
# print(len(data))
# print(data[4]) # Smiles
# print(data[5]) # protein ID
smiles_info = data[4].split(';')
sequence_info = data[5].split(';')
# print(smiles_info)
# print(sequence_info)
if len(smiles_info) :
file.write(line.strip() + '\t')
n = 0
for smiles in smiles_info :
if n :
file.write(';')
Kcat_values = list()
n += 1
if smiles and "." not in smiles :
for sequence_id in sequence_info :
if sequence_id :
print(sequence_id)
# print(proteinSeq[sequence_id])
if organism != 'Saccharomyces_cerevisiae' :
sequence = proteinSeq[sequence_id]
else :
sequence = proteinSeq['Saccharomyces_cerevisiae@'+sequence_id]
# print(smiles)
if "." not in smiles :
# i += 1
# print('This is',i)
mol = Chem.AddHs(Chem.MolFromSmiles(smiles))
try :
atoms = create_atoms(mol)
# print(atoms)
i_jbond_dict = create_ijbonddict(mol)
# print(i_jbond_dict)
fingerprints = extract_fingerprints(atoms, i_jbond_dict, radius)
# print(fingerprints)
# compounds.append(fingerprints)
adjacency = create_adjacency(mol)
# print(adjacency)
# adjacencies.append(adjacency)
words = split_sequence(sequence,ngram)
# print(words)
# proteins.append(words)
fingerprints = torch.LongTensor(fingerprints)
adjacency = torch.FloatTensor(adjacency)
words = torch.LongTensor(words)
inputs = [fingerprints, adjacency, words]
# try :
prediction = predictor.predict(inputs)
Kcat_log_value = prediction.item()
Kcat_value = math.pow(2,Kcat_log_value)
# Kcat_value = math.pow(10,Kcat_log_value)
# print(Kcat_log_value)
# print(Kcat_value)
# print(type(Kcat_value))
print('%.4f' %(Kcat_value))
except :
Kcat_value = '#'
# Kcat_values.append('%.4f' %(Kcat_value))
if type(Kcat_value) == float :
Kcat_values.append('%.4f' %(Kcat_value))
else :
Kcat_values.append(Kcat_value)
# if len(Kcat_values) > 1 :
added_content = ','.join(Kcat_values)
file.write(added_content)
file.write('\n')
file.close()
if __name__ == '__main__' :
main()
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