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import pandas as pd
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem
# from rdkit.Chem import Draw
from rdkit.Chem import rdChemReactions as Reactions
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing import sequence
from keras.utils import pad_sequences
import keras
from keras import backend as K
from keras.models import load_model
import argparse
import h5py
import pdb
seq_rdic = ['A', 'I', 'L', 'V', 'F', 'W', 'Y', 'N', 'C', 'Q', 'M','S', 'T', 'D', 'E', 'R', 'H', 'K', 'G', 'P', 'O', 'U', 'X', 'B', 'Z']
seq_dic = {w: i+1 for i, w in enumerate(seq_rdic)}
def encodeSeq(seq, seq_dic):
if pd.isnull(seq):
return [0]
else:
return [seq_dic[aa] for aa in seq]
def load_modelfile(model_string):
loaded_model = tf.keras.models.load_model(model_string)
return loaded_model
def prot_feature_gen_from_str_input(prot_input_str, prot_len=2500):
Prot_ID = prot_input_str.split(':')[0]
Prot_seq = prot_input_str.split(':')[1]
prot_dataframe = pd.DataFrame(
{'Protein_ID': Prot_ID, 'Sequence': Prot_seq}, index=[0])
prot_dataframe.set_index('Protein_ID')
prot_dataframe["encoded_sequence"] = prot_dataframe.Sequence.map(
lambda a: encodeSeq(a, seq_dic))
prot_feature = pad_sequences(
prot_dataframe["encoded_sequence"].values, prot_len)
return prot_feature, Prot_ID
def mol_feature_gen_from_str_input(mol_str, kegg_id_flag, kegg_df):
if kegg_id_flag == 1:
KEGG_ID = mol_str
kegg_id_loc = kegg_df.index[kegg_df.Compound_ID == KEGG_ID][0]
KEGG_ID_info = kegg_df.loc[kegg_id_loc]
KEGG_ID_info_df = KEGG_ID_info.to_frame().T.set_index('Compound_ID')
final_return = KEGG_ID_info_df
final_id = KEGG_ID
else:
try:
mol_ID = mol_str.split(':')[0]
mol_smiles = mol_str.split(':')[1]
mol = Chem.MolFromSmiles(mol_smiles)
fp1 = AllChem.GetMorganFingerprintAsBitVect(
mol, useChirality=True, radius=2, nBits=2048)
fp_list = list(np.array(fp1).astype(float))
fp_str = list(map(str, fp_list))
mol_fp = '\t'.join(fp_str)
mol_dict = {}
mol_dict['Compound_ID'] = mol_ID
mol_dict['Smiles'] = mol_smiles
mol_dict['morgan_fp_r2'] = mol_fp
mol_info_df = pd.DataFrame(mol_dict, index=[0])
mol_info_df.set_index('Compound_ID')
final_return = mol_info_df
final_id = mol_ID
except Exception as error:
print('Something wrong with molecule input string...' + repr(error))
return final_return, final_id
def act_df_gen_mol_feature(mol_id, prot_id):
act_df = pd.DataFrame(
{'Protein_ID': prot_id, 'Compound_ID': mol_id}, index=[0])
return act_df
def compound_feature_gen_df_input(act_df, comp_df, comp_len=2048, comp_vec='morgan_fp_r2'):
act_df = pd.merge(act_df, comp_df, left_on='Compound_ID', right_index=True)
comp_feature = np.stack(act_df[comp_vec].map(lambda fp: fp.split("\t")))
comp_feature = comp_feature.astype('float')
return comp_feature
def model_prediction(compound_feature, enz_feature, model):
prediction_vals = model.predict([compound_feature, enz_feature])
return prediction_vals[0][0]
# loaded_model = load_modelfile('./../CNN_results/model_final.model')
# KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')
# kegg_df = KEGG_compound_read.reset_index()
def main():
loaded_model = load_modelfile('./../CNN_results_split_final/Final_model.model')
KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')
kegg_df = KEGG_compound_read.reset_index()
# print(loaded_model.summary())
# def img_to_bytes(img_path):
# img_bytes = Path(img_path).read_bytes()
# encoded = base64.b64encode(img_bytes).decode()
# return encoded
# # st.title('dGPredictor')
# header_html = "<img src='../figures/header.png'>"
# st.markdown(
# header_html, unsafe_allow_html=True,
# )
enz_str ="A0A4P8WFA8:MTKRVLVTGGAGFLGSHLCERLLSEGHEVICLDNFGSGRRKNIKEFEDHPSFKVNDRDVRISESLPSVDRIYHLASRASPADFTQFPVNIALANTQGTRRLLDQARACDARMVFASTSEVYGDPKVHPQPETYTGNVNIRGARGCYDESKRFGETLTVAYQRKYDVDARTVRIFNTYGPRMRPDDGRVVPTFVTQALRGDDLTIYGDGEQTRSFCYVDDLIEGLISLMRVDNPEHNVYNIGKENERTIKELAYEVLGLTDTESDIVYEPLPEDDPGQRRPDITRAKTELDWEPKISLREGLEDTITYFDN"
comp_str = "C00149"
try:
prot_feature, prot_id = prot_feature_gen_from_str_input(enz_str)
kegg_id_flag = 1
comp_feature, comp_id = mol_feature_gen_from_str_input(comp_str, kegg_id_flag, kegg_df)
act_dataframe = act_df_gen_mol_feature(comp_id, prot_id)
# pdb.set_trace()
compound_feature = compound_feature_gen_df_input(act_dataframe, comp_feature)
except Exception as e:
print('Error somewhere...' + repr(e))
# print(type(compound_feature1))
# print(loaded_model.predict([compound_feature1, prot_feature]))
EnzRankScore = model_prediction(compound_feature, prot_feature, loaded_model)
es = EnzRankScore
print('something has happened')
print('EnzRank score')
print(es)
# print(type(es))
# print(type(EnzRankScore))
# graph = tf.compat.v1.get_default_graph()
# with graph.as_default():
# y = loaded_model.predict([compound_feature, prot_feature])
# print('-----------')
# print(y)
# print(type(y[0][0]))
# print(y[0][0])
if __name__ == '__main__':
main()
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