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| import numpy as np | |
| import pandas as pd | |
| from pathlib import Path | |
| import requests | |
| from add_annotations import * | |
| from utils import * | |
| from add_annotations import * | |
| from add_sasa import * | |
| import streamlit as st | |
| import json | |
| UNIPROT_ANNOTATION_COLS = ['disulfide', 'intMet', 'intramembrane', 'naturalVariant', 'dnaBinding', | |
| 'activeSite', | |
| 'nucleotideBinding', 'lipidation', 'site', 'transmembrane', | |
| 'crosslink', 'mutagenesis', 'strand', | |
| 'helix', 'turn', 'metalBinding', 'repeat', 'topologicalDomain', | |
| 'caBinding', 'bindingSite', 'region', | |
| 'signalPeptide', 'modifiedResidue', 'zincFinger', 'motif', | |
| 'coiledCoil', 'peptide', | |
| 'transitPeptide', 'glycosylation', 'propeptide', 'disulfideBinary', | |
| 'intMetBinary', 'intramembraneBinary', | |
| 'naturalVariantBinary', 'dnaBindingBinary', 'activeSiteBinary', | |
| 'nucleotideBindingBinary', 'lipidationBinary', 'siteBinary', | |
| 'transmembraneBinary', 'crosslinkBinary', 'mutagenesisBinary', | |
| 'strandBinary', 'helixBinary', 'turnBinary', 'metalBindingBinary', | |
| 'repeatBinary', 'topologicalDomainBinary', 'caBindingBinary', | |
| 'bindingSiteBinary', 'regionBinary', 'signalPeptideBinary', | |
| 'modifiedResidueBinary', 'zincFingerBinary', 'motifBinary', | |
| 'coiledCoilBinary', 'peptideBinary', 'transitPeptideBinary', | |
| 'glycosylationBinary', 'propeptideBinary'] | |
| SIMPLE_COLS = ['uniprotID', 'wt', 'pos', 'mut', 'datapoint', 'composition', 'polarity', | |
| 'volume', 'granthamScore', 'domain', 'domStart', 'domEnd', 'distance', | |
| 'intMet', 'naturalVariant', 'activeSite', 'crosslink', 'mutagenesis', | |
| 'strand', 'helix', 'turn', 'region', 'modifiedResidue', 'motif', | |
| 'metalBinding', 'lipidation', 'glycosylation', 'topologicalDomain', | |
| 'nucleotideBinding', 'bindingSite', 'transmembrane', 'transitPeptide', | |
| 'repeat', 'site', 'peptide', 'signalPeptide', 'disulfide', 'coiledCoil', | |
| 'intramembrane', 'zincFinger', 'caBinding', 'propeptide', 'dnaBinding', | |
| 'disulfideBinary', 'intMetBinary', 'intramembraneBinary', | |
| 'naturalVariantBinary', 'dnaBindingBinary', 'activeSiteBinary', | |
| 'nucleotideBindingBinary', 'lipidationBinary', 'siteBinary', | |
| 'transmembraneBinary', 'crosslinkBinary', 'mutagenesisBinary', | |
| 'strandBinary', 'helixBinary', 'turnBinary', 'metalBindingBinary', | |
| 'repeatBinary', 'topologicalDomainBinary', 'caBindingBinary', | |
| 'bindingSiteBinary', 'regionBinary', 'signalPeptideBinary', | |
| 'modifiedResidueBinary', 'zincFingerBinary', 'motifBinary', | |
| 'coiledCoilBinary', 'peptideBinary', 'transitPeptideBinary', | |
| 'glycosylationBinary', 'propeptideBinary'] | |
| def addSwissModels(to_swiss, path_to_input_files, path_to_output_files): | |
| ''' | |
| :param to_swiss: | |
| :param path_to_input_files: | |
| :param path_to_output_files: | |
| :return: swissmodel dataframe with mapped SWISSMODEL information, dataframe that will be sent to modbase. | |
| ''' | |
| print('\n>>> Proceeding to SwissModel search...') | |
| print('------------------------------------\n') | |
| if len(to_swiss) > 0: | |
| print('\n>>> Generating SwissModel file...\n') | |
| to_swiss.reset_index(drop=True, inplace=True) | |
| to_swiss.fillna(np.NaN) | |
| swiss_model = pd.read_csv(Path(path_to_input_files / 'swissmodel_structures.txt'), | |
| sep='\t', dtype=str, header=None, skiprows=1, | |
| names=['UniProtKB_ac', 'iso_id', 'uniprot_seq_length', 'uniprot_seq_md5', | |
| 'coordinate_id', 'provider', 'from', 'to', 'template', 'qmean_norm', 'seqid', | |
| 'url']) | |
| swiss_model = swiss_model[swiss_model.UniProtKB_ac.isin(to_swiss.uniprotID.to_list())] | |
| try: | |
| swiss_model.iso_id = swiss_model.iso_id.astype('str') | |
| except: | |
| AttributeError | |
| swiss_model['iso_id'] = np.NaN | |
| for ind in swiss_model.index: | |
| swiss_model.at[ind, 'UniProtKB_ac'] = swiss_model.at[ind, 'UniProtKB_ac'].split('-')[0] | |
| swiss_model = swiss_model[swiss_model.provider == 'SWISSMODEL'] | |
| print('\n>>> Index File Processed...\n') | |
| swiss_model = swiss_model[['UniProtKB_ac', 'from', 'to', 'template', 'qmean_norm', 'seqid', 'url']] | |
| # Sort models on qmean score and identity. Some proteins have more than one models, we will pick one. | |
| swiss_model = swiss_model.sort_values(by=['UniProtKB_ac', 'qmean_norm', 'seqid'], ascending=False) | |
| swiss_model.reset_index(inplace=True, drop=True) | |
| with_swiss_models = to_swiss[to_swiss.uniprotID.isin(swiss_model.UniProtKB_ac.to_list())] | |
| no_swiss_models = to_swiss[~to_swiss.uniprotID.isin(swiss_model.UniProtKB_ac.to_list())] | |
| if len(no_swiss_models) == 0: | |
| no_swiss_models = pd.DataFrame(columns=to_swiss.columns) | |
| else: | |
| no_swiss_models.reset_index(drop=True, inplace= True) | |
| swiss_models_with_data = pd.merge(with_swiss_models, swiss_model, left_on=['uniprotID'], | |
| right_on=['UniProtKB_ac'], how='left') | |
| swiss_models_with_data = swiss_models_with_data.sort_values(by=['uniprotID', 'wt','pos', 'qmean_norm'], | |
| ascending=False) | |
| swiss_models_with_data['pos'] = swiss_models_with_data['pos'] .apply(lambda x: int(x)) | |
| swiss_models_with_data['from'] = swiss_models_with_data['from'].apply(lambda x: int(x)) | |
| swiss_models_with_data['to'] = swiss_models_with_data['to'] .apply(lambda x: int(x)) | |
| notEncompassed = swiss_models_with_data[((swiss_models_with_data['pos'] > swiss_models_with_data['to']) | (swiss_models_with_data['pos'] < swiss_models_with_data['from']))] | |
| swiss_models_with_data = swiss_models_with_data[(swiss_models_with_data['pos'] < swiss_models_with_data['to']) & (swiss_models_with_data['pos'] > swiss_models_with_data['from'])] | |
| notEncompassed = notEncompassed[~notEncompassed.uniprotID.isin(swiss_models_with_data.uniprotID.to_list())] | |
| swiss_models_with_data = swiss_models_with_data.drop(['UniProtKB_ac', 'seqid'], axis=1) | |
| swiss_models_with_data = swiss_models_with_data[swiss_models_with_data.url != np.NaN] | |
| url_nan = swiss_models_with_data[swiss_models_with_data.url == np.NaN] | |
| url_nan = url_nan.drop(['from', 'qmean_norm', 'template', 'to', 'url'], axis=1) | |
| no_swiss_models_updated = pd.concat([no_swiss_models, url_nan, notEncompassed]) | |
| if len(swiss_models_with_data)>0: | |
| for i in swiss_models_with_data.index: | |
| try: | |
| swiss_models_with_data.at[i, 'chain'] = swiss_models_with_data.at[i, 'template'].split('.')[2] | |
| swiss_models_with_data.at[i, 'template'] = swiss_models_with_data.at[i, 'template'].split('.')[0] | |
| except IndexError: | |
| swiss_models_with_data.at[i, 'chain'] = np.NaN | |
| swiss_models_with_data.at[i, 'template'] = np.NaN | |
| swiss_models_with_data.chain = swiss_models_with_data.chain.astype('str') | |
| swiss_models_with_data['qmean_norm'] = swiss_models_with_data.qmean_norm.apply(lambda x: round(float(x), 2)) | |
| no_swiss_models_updated.reset_index(drop = True, inplace=True) | |
| swiss_models_with_data.reset_index(drop=True, inplace=True) | |
| existing_free_sasa = list(Path(path_to_output_files / 'freesasa_files').glob("*")) | |
| existing_free_sasa = [str(i) for i in existing_free_sasa] | |
| existing_free_sasa = [i.split('/')[-1].split('.')[0] for i in existing_free_sasa] | |
| print('Beginning SwissModel files download...') | |
| existing_swiss = list(Path(path_to_output_files / 'swissmodel_structures').glob("*")) | |
| existing_swiss = [str(i) for i in existing_swiss] | |
| existing_swiss = ['.'.join(i.split('/')[-1].split('.')[:-1]) for i in existing_swiss] | |
| for i in swiss_models_with_data.index: | |
| protein = swiss_models_with_data.at[i, 'uniprotID'] | |
| varPos = swiss_models_with_data.at[i, 'pos'] | |
| wt = swiss_models_with_data.at[i, 'wt'] | |
| template = swiss_models_with_data.at[i, 'template'].split('.')[0] | |
| qmean_norm = str(round(float(swiss_models_with_data.at[i, 'qmean_norm']), 2)) | |
| swiss_models_with_data.at[i, 'coordVAR'] = np.NaN | |
| swiss_models_with_data.at[i, 'coordinates'] = np.NaN | |
| swiss_models_with_data.at[i, 'AAonPDB'] = np.NaN | |
| varPos = swiss_models_with_data.at[i, 'pos'] | |
| AAonPDB = np.NaN | |
| coordDict = {} | |
| if protein + '_' + template + '_' + qmean_norm not in existing_swiss: | |
| url = swiss_models_with_data.at[i, 'url'].strip('\"').strip('}').replace('\\', '').strip('\"') | |
| req = requests.get(url) | |
| name = Path(path_to_output_files / 'swissmodel_structures' / f'{protein}_{template}_{qmean_norm}.txt') | |
| print('Downloading for Protein:', protein + ' Model: ' + template) | |
| with open(name, 'wb') as f: | |
| f.write(req.content) | |
| else: | |
| print(f'Model exists for {protein}.') | |
| name = Path(path_to_output_files / 'swissmodel_structures' / f'{protein}_{template}_{qmean_norm}.txt') | |
| swiss_dp = protein + '_' + template + '_' + qmean_norm | |
| if swiss_dp not in existing_free_sasa: | |
| (run_freesasa(Path(path_to_output_files / 'swissmodel_structures' / f'{swiss_dp}.txt'), | |
| Path(path_to_output_files / 'freesasa_files' / f'{swiss_dp}.txt'), include_hetatms=True, | |
| outdir=None, force_rerun=False, file_type='pdb')) | |
| filename = Path(path_to_output_files / 'freesasa_files' / f'{swiss_dp}.txt') | |
| swiss_models_with_data.at[i, 'sasa'] = sasa(protein, varPos, wt, 1, filename, path_to_output_files, | |
| file_type='pdb') | |
| with open(name, encoding="utf8") as f: | |
| lines = f.readlines() | |
| for row in lines: | |
| if row[0:4] == 'ATOM' and row[13:15] == 'CA': | |
| position = int(row[22:26].strip()) | |
| chain = row[20:22].strip() | |
| aminoacid = threeToOne(row[17:20]) | |
| coords = [row[31:38].strip(), row[39:46].strip(), row[47:54].strip()] | |
| coordDict[position] = coords | |
| if int(position) == int(varPos): | |
| AAonPDB = aminoacid | |
| coordVAR = coords | |
| if (row[0:3] == 'TER') or (row[0:3] == 'END'): | |
| swiss_models_with_data.loc[i, 'coordinates'] = str(coordDict) | |
| swiss_models_with_data.loc[i, 'AAonPDB'] = str(AAonPDB) | |
| swiss_models_with_data.loc[i, 'coordVAR'] = str(coordVAR) | |
| break | |
| if swiss_models_with_data.at[i, 'AAonPDB'] == swiss_models_with_data.at[i, 'wt']: | |
| swiss_models_with_data.at[i, 'PDB_ALIGN_STATUS'] = 'aligned' | |
| else: | |
| swiss_models_with_data.at[i, 'PDB_ALIGN_STATUS'] = 'notAligned' | |
| swiss_models_with_data.sort_values(['uniprotID', 'wt', 'pos', 'mut', 'PDB_ALIGN_STATUS', 'qmean_norm'], | |
| ascending=[True, True, True, True, True, False], inplace=True) | |
| swiss_models_with_data.drop_duplicates(['uniprotID', 'wt', 'pos', 'mut'], keep='first', inplace=True) | |
| obsolete = swiss_models_with_data[pd.isna(swiss_models_with_data.coordVAR)] | |
| no_swiss_models_updated = pd.concat([no_swiss_models_updated, obsolete]) | |
| swiss_models_with_data = swiss_models_with_data.fillna(np.NaN) | |
| else: | |
| swiss_models_with_data = pd.DataFrame() | |
| no_swiss_models_updated = pd.DataFrame() | |
| no_swiss_models_updated = no_swiss_models_updated[SIMPLE_COLS] | |
| return swiss_models_with_data, no_swiss_models_updated | |