Create modbaseModelAdd.py

code/modbaseModelAdd.py

@@ -0,0 +1,131 @@
+import requests
+import numpy as np
+import pandas as pd
+from utils import *
+from pathlib import Path
+from bs4 import BeautifulSoup
+from add_sasa import *
+def addModbaseModels(dataframe, path_to_input_files, path_to_output_files):
+    if len(dataframe) != 0:
+        # GET MODBASE MODELS
+        # Get IDs from data to retrieve only their models from MODBASE
+        dataframe.reset_index(inplace=True, drop=True)
+
+        existing_modbase_models = list(Path(path_to_output_files / 'modbase_structures').glob("*"))
+        existing_modbase_models = [str(i) for i in existing_modbase_models]
+        existing_modbase_models = [i.split('/')[-1].split('.')[0] for i in existing_modbase_models]
+
+        existing_modbase_models_ind = list(Path(path_to_output_files / 'modbase_structures_individual').glob("*"))
+        existing_modbase_models_ind = [str(i) for i in existing_modbase_models_ind]
+        existing_modbase_models_ind = [i.split('/')[-1].split('.')[0] for i in existing_modbase_models_ind]
+
+        modbase_reduced = pd.DataFrame(columns = ['uniprotID', 'target_begin', 'target_end', 'quality_score',
+                                               'model_id', 'coordinates','AAonPDB', 'coordVAR'])
+        print('Retrieving ModBase models...\n')
+        modbase = pd.DataFrame(
+            columns=['uniprotID', 'target_begin', 'target_end', 'quality_score', 'model_id',
+                     'coordinates', 'AAonPDB', 'coordVAR'])
+        no_modbase = pd.DataFrame(
+            columns=['uniprotID', 'target_begin', 'target_end', 'quality_score', 'model_id',
+                     'coordinates', 'AAonPDB', 'coordVAR'])
+        # Get model files associated with each UniProtID
+        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]
+        for i in dataframe.index:
+            coordDict = {}
+            protein = dataframe.at[i, 'uniprotID']
+            varPos = int(dataframe.at[i, 'pos'])
+            wt =  dataframe.at[i, 'wt']
+            if protein not in existing_modbase_models:
+                print('Downloading Modbase models for ', protein)
+                url = 'https://salilab.org/modbase/retrieve/modbase/?databaseID=' + protein
+                req = requests.get(url)
+                name = path_to_output_files / 'modbase_structures' /  f'{protein}.txt'
+                with open(name, 'wb') as f:
+                    f.write(req.content)
+            else:
+                print('Model exists for', protein)
+                name = Path(path_to_output_files / 'modbase_structures' / f'{protein}.txt')
+
+            with open(name, encoding="utf8") as f:
+                a = open(name, 'r').read()
+                soup = BeautifulSoup(a, 'lxml')
+                if soup.findAll('pdbfile') != []:
+                    for pdb in soup.findAll('pdbfile'):
+                        model_id = str(pdb.contents[1])[10:-11]
+                        if model_id not in existing_modbase_models_ind:
+                            with open(path_to_output_files / 'modbase_structures_individual' / f'{model_id}.txt', 'w', encoding="utf8") as individual:
+                                individual.write(str('UniProt ID: ' + protein))
+                                individual.write('\n')
+                                individual.write(str(pdb.contents[3])[10:-11].strip())
+                            run_freesasa(
+                                Path(path_to_output_files / 'modbase_structures_individual' / f'{model_id.lower()}.txt'),
+                                Path(path_to_output_files / 'freesasa_files' / f'{model_id.lower()}.txt'),
+                                include_hetatms=True,
+                                outdir=None, force_rerun=False, file_type='pdb')
+                        filename = Path(path_to_output_files / 'freesasa_files' / f'{model_id.lower()}.txt')
+                        dataframe.loc[i, 'sasa'] = sasa(protein, varPos, wt, 1, filename, path_to_output_files, file_type='pdb')
+                        with open(path_to_output_files / 'modbase_structures_individual'/ f'{model_id}.txt', encoding="utf8") as m:
+
+                            lines = m.readlines()
+                            quality_score = -999
+                            for ind_line in lines:
+                                if ind_line[0:10] == 'UniProt ID':
+                                    uniprot_id = ind_line.split(':')[1].strip()
+                                if ind_line[0:23] == 'REMARK 220 TARGET BEGIN':
+                                    target_begin = ind_line[40:43].strip()
+                                if ind_line[0:21] == 'REMARK 220 TARGET END':
+                                    target_end = ind_line[40:43].strip()
+                            coordDict, AAonPDB, coordVAR = {},np.NaN,np.NaN
+                            if (int(varPos) > int(target_begin)) & (int(varPos) < int(target_end)):
+                                coordDict = {}
+                                for ind_line in lines:
+                                    if ind_line[0:27] == 'REMARK 220 MODPIPE MODEL ID':
+                                        model_id = ind_line[40:].strip()
+                                    if ind_line[0:15].strip() == 'REMARK 220 MPQS':
+                                        quality_score = ind_line[40:].strip()
+                                    if ind_line[0:4] == 'ATOM' and ind_line[13:15] == 'CA':
+                                        position = int(ind_line[22:26].strip())
+                                        chain = ind_line[20:22].strip()
+                                        aminoacid = threeToOne(ind_line[17:20])
+                                        coords = [ind_line[31:38].strip(), ind_line[39:46].strip(), ind_line[47:54].strip()]
+                                        coordDict[position] = coords
+                                        if position == int(varPos):
+                                            AAonPDB = aminoacid
+                                            coordVAR = str(coords)
+                                        if ind_line[0:3] == 'TER':
+                                            break
+                                try:
+                                    k = pd.Series(
+                                        [uniprot_id, target_begin, target_end,quality_score, model_id, coordDict, AAonPDB, coordVAR])
+                                    new_row = {'uniprotID': uniprot_id, 'target_begin': target_begin,
+                                               'target_end': target_end, 'quality_score': quality_score,
+                                               'model_id': model_id, 'coordinates': coordDict,
+                                               'AAonPDB': AAonPDB, 'coordVAR': coordVAR}
+                                    modbase_reduced = modbase_reduced.append(new_row, ignore_index=True)
+                                    modbase_reduced = modbase_reduced[['uniprotID', 'quality_score', 'model_id', 'coordinates', 'AAonPDB', 'coordVAR']]
+                                    modbase = dataframe.merge(modbase_reduced, on='uniprotID', how='left')
+                                    modbase.quality_score = modbase.quality_score.astype(float)
+                                    modbase = modbase.sort_values(by=['datapoint', 'quality_score'], ascending=False)
+                                    modbase.reset_index(inplace=True, drop=True)
+                                    modbase.fillna(np.NaN, inplace=True)
+                                    modbase.replace({'\'?\', ': '',
+                                                     ', \'?\'': '',
+                                                     '(': '', ')': '',
+                                                     '[\'?\']': np.NaN,
+                                                     '[]': np.NaN,
+                                                     'nan-nan': np.NaN,
+                                                     '': np.NaN}, inplace=True)
+                                except NameError:
+                                    print('This file doesnt have Quality Score. Replacer: -999', model_id)
+                else:
+                    k = pd.Series(
+                        dataframe.loc[i])
+                    no_modbase = no_modbase.append(k, ignore_index=True)
+
+    no_modbase_no_Coord = modbase[pd.isna(modbase['coordVAR'])]
+    no_modbase = pd.concat([no_modbase, no_modbase_no_Coord])
+    modbase = modbase[~pd.isna(modbase['coordVAR'])]
+
+    return modbase, no_modbase