| import pandas as pd |
| import numpy as np |
| from tqdm import tqdm |
| import argparse |
| import ipdb |
| import os |
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--brenda_file", help="full path to brenda processed data file", type=str, required=True) |
| parser.add_argument("--sabio_file", help="full path to sabio processed data file", type=str, required=True) |
| parser.add_argument("--output_dir", help="full path to output directory to save merged data files", type=str, required=True) |
| args, unparsed = parser.parse_known_args() |
| parser = argparse.ArgumentParser() |
|
|
| return args |
|
|
| args = parse_args() |
|
|
| sabio_df = pd.read_csv(f'{args.sabio_file}') |
| brenda_df = pd.read_csv(f'{args.brenda_file}') |
|
|
| |
| |
| |
| reacsmi_col = [] |
| for ind, row in sabio_df.iterrows(): |
| smi = row.reaction_smiles |
| if not pd.isna(smi): |
| reacs, prods = smi.split('>>') |
| reacs = sorted(reacs.split('.')) |
| prods = sorted(prods.split('.')) |
| smi = ".".join(reacs) + '>>' + ".".join(prods) |
| reacsmi_col.append(smi) |
| sabio_df['reaction_smiles'] = reacsmi_col |
|
|
| reacsmi_col = [] |
| for ind, row in brenda_df.iterrows(): |
| smi = row.reaction_smiles |
| if not pd.isna(smi): |
| reacs, prods = smi.split('>>') |
| reacs = sorted(reacs.split('.')) |
| prods = sorted(prods.split('.')) |
| smi = ".".join(reacs) + '>>' + ".".join(prods) |
| reacsmi_col.append(smi) |
| brenda_df['reaction_smiles'] = reacsmi_col |
|
|
| cols = ['sequence','sequence_source','uniprot','reaction_smiles','value','temperature','ph','taxonomy_id'] |
|
|
| kcat_brenda = brenda_df[brenda_df.parameter=='turnover_number'] |
| kcat_brenda = kcat_brenda[cols] |
| kcat_brenda.reset_index(inplace=True,drop=True) |
| |
| kcat_sabio = sabio_df[sabio_df.param_name=='kcat'] |
| kcat_sabio['value'] = kcat_sabio.param_value_stdunit |
| kcat_sabio = kcat_sabio[cols] |
|
|
| kcat_merged = pd.concat([kcat_brenda,kcat_sabio]) |
| values = [] |
| for val in kcat_merged.value: |
| if val>1e6: val=1e6 |
| elif val<1e-6: val=1e-6 |
| values.append(val) |
| kcat_merged['value'] = values |
| |
|
|
| kcat_merged['log10_value'] = np.log10(kcat_merged.value) |
| kcat_merged.drop_duplicates(inplace=True) |
| kcat_merged[['reactant_smiles','product_smiles']] = kcat_merged['reaction_smiles'].str.split('>>',expand=True) |
| kcat_merged.dropna(subset=['ec','taxonomy_id','sequence','reactant_smiles','log10_value'],inplace=True) |
| kcat_merged.reset_index(inplace=True,drop=True) |
|
|
| |
| cols = ['sequence','sequence_source','uniprot','substrate_smiles','value','temperature','ph','taxonomy_id'] |
|
|
| km_brenda = brenda_df[brenda_df.parameter=='km_value'] |
| km_brenda = km_brenda[cols] |
|
|
| km_sabio = sabio_df[sabio_df.param_name=='Km'] |
| km_sabio['value'] = km_sabio.param_value_stdunit |
| km_sabio['substrate_smiles'] = km_sabio.param_species_smiles |
| km_sabio = km_sabio[cols] |
|
|
| km_merged = pd.concat([km_brenda,km_sabio]) |
| values = [] |
| for val in km_merged.value: |
| if val>1e4: val=1e4 |
| elif val<1e-8: val=1e-8 |
| values.append(val) |
| km_merged['value'] = values |
|
|
| km_merged['log10_value'] = np.log10(km_merged.value) |
| km_merged.drop_duplicates(inplace=True) |
| km_merged.dropna(subset=['ec','taxonomy_id','sequence','substrate_smiles','log10_value'],inplace=True) |
| km_merged.reset_index(inplace=True,drop=True) |
|
|
| |
| cols = ['sequence','sequence_source','uniprot','substrate_smiles','value','temperature','ph','taxonomy_id'] |
|
|
| ki_brenda = brenda_df[brenda_df.parameter=='ki_value'] |
| ki_brenda = ki_brenda[cols] |
|
|
| ki_sabio = sabio_df[sabio_df.param_name=='Ki'] |
| ki_sabio['value'] = ki_sabio.param_value_stdunit |
| ki_sabio['substrate_smiles'] = ki_sabio.param_species_smiles |
| ki_sabio = ki_sabio[cols] |
|
|
| ki_merged = pd.concat([ki_brenda,ki_sabio]) |
| values = [] |
| for val in ki_merged.value: |
| if val>1e4: val=1e4 |
| elif val<1e-10: val=1e-10 |
| values.append(val) |
| ki_merged['value'] = values |
|
|
| ki_merged['log10_value'] = np.log10(ki_merged.value) |
| ki_merged.drop_duplicates(inplace=True) |
| ki_merged.dropna(subset=['ec','taxonomy_id','sequence','substrate_smiles','log10_value'],inplace=True) |
| ki_merged.reset_index(inplace=True,drop=True) |
|
|
| def handle_duplicates(df, param): |
| if param=='kcat': grouper = ['reactant_smiles','sequence'] |
| else: grouper = ['substrate_smiles','sequence'] |
| newdf = pd.DataFrame() |
| groups = df.groupby(grouper) |
| for each in tqdm(groups): |
| groupname, group = each |
| if param=='kcat': |
| value = group.log10_value.max() |
| group['log10kcat_max'] = value |
| else: |
| value = group.log10_value.mean() |
| group[f'log10{param}_mean'] = value |
| |
| group['group'] = '__'.join(groupname) |
| newdf = pd.concat([newdf,group.iloc[:1]]) |
| return newdf.reset_index(drop=True) |
|
|
| ipdb.set_trace() |
|
|
| kcat_merged = handle_duplicates(kcat_merged,'kcat') |
| km_merged = handle_duplicates(km_merged,'km') |
| ki_merged = handle_duplicates(ki_merged,'ki') |
|
|
| if not os.path.exists(args.output_dir): |
| os.mkdir(args.output_dir) |
|
|
| kcat_merged.to_csv(f'{args.output_dir}/kcat_merged_max.csv') |
| km_merged.to_csv(f'{args.output_dir}/km_merged_mean.csv') |
| ki_merged.to_csv(f'{args.output_dir}/ki_merged_mean.csv') |
|
|
| print(len(kcat_merged)) |
| print(len(km_merged)) |
| print(len(ki_merged)) |
|
|
| import sys |
| sys.exit(0) |
|
|
| from sklearn.model_selection import train_test_split |
|
|
| def split_train_test(df): |
| train_df, test_df = train_test_split(df, test_size=0.1, random_state=0) |
| train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=0) |
| train_df['split'] = 'train' |
| val_df['split'] = 'val' |
| test_df['split'] = 'test' |
| |
| return pd.concat([train_df, val_df]).reset_index(drop=True), test_df |
|
|
|
|
| kcat_train, kcat_test = split_train_test(kcat_merged) |
| km_train, km_test = split_train_test(km_merged) |
| ki_train, ki_test = split_train_test(ki_merged) |
|
|
| kcat_train.to_csv('./final_data/kcat_train.csv') |
| kcat_test.to_csv('./final_data/kcat_test.csv') |
| km_train.to_csv('./final_data/km_train.csv') |
| km_test.to_csv('./final_data/km_test.csv') |
| ki_train.to_csv('./final_data/ki_train.csv') |
| ki_test.to_csv('./final_data/ki_test.csv') |
|
|
| kcat_train_seq = kcat_train.dropna(subset=['sequence']).reset_index(drop=True) |
| kcat_test_seq = kcat_test.dropna(subset=['sequence']).reset_index(drop=True) |
| km_train_seq = km_train.dropna(subset=['sequence']).reset_index(drop=True) |
| km_test_seq = km_test.dropna(subset=['sequence']).reset_index(drop=True) |
| ki_train_seq = ki_train.dropna(subset=['sequence']).reset_index(drop=True) |
| ki_test_seq = ki_test.dropna(subset=['sequence']).reset_index(drop=True) |
|
|
| |
|
|
| |
| ecs = set(kcat_merged.ec).union(set(km_merged.ec)).union(set(ki_merged.ec)) |
| ecs_seq = set(kcat_train_seq.ec).union(set(kcat_test_seq.ec)).union(set(km_train_seq.ec)).union(set(km_test_seq.ec)).union(set(ki_train_seq.ec)).union(set(ki_test_seq.ec)) |
|
|
| ec_to_seq = {ec:0 for ec in ecs} |
| for ec in ecs_seq: ec_to_seq[ec] = 1 |
|
|
| smis = set(km_merged.ec).union(set(ki_merged.ec)) |
| for reacsmi in kcat_merged.reactant_smiles: |
| reacs = smis.split('.') |
| for r in reacs:smis.add(r) |
|
|
| smis_seq = set(km_train_seq.ec).union(set(km_test_seq.ec)).union(set(ki_train_seq.ec)).union(set(ki_test_seq.ec)) |
| for reacsmi in kcat_train_seq.reactant_smiles: |
| reacs = smis_seq.split('.') |
| for r in reacs:smis_seq.add(r) |
| for reacsmi in kcat_test_seq.reactant_smiles: |
| reacs = smis.split('.') |
| for r in reacs:smis_seq.add(r) |
| |
| smi_to_seq = {smi:0 for smi in smis} |
| for smi in smis_seq: smi_to_seq[smi] = 1 |
|
|
|
|
|
|
