Update code/modbaseModelAdd.py

code/modbaseModelAdd.py

@@ -1,3 +1,152 @@
+# 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]
+#         keep_cols = dataframe.columns
+#         for i in dataframe.index:
+#             coordDict = {}
+#             protein = dataframe.at[i, 'uniprotID']
+#             varPos = int(dataframe.at[i, 'pos'])
+#             wt =  dataframe.at[i, 'wt']
+#             mut = dataframe.at[i, 'mut']
+#             datapoint = dataframe.at[i, 'datapoint']
+            
+#             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')
+#                         st.write('filename', filename)
+#                         st.write('varPos', varPos)
+#                         st.write('wt', wt)
+#                         st.write('protein', protein)
+#                         st.write('path_to_output_files', path_to_output_files)
+#                         dataframe.loc[i, 'sasa'] = sasa(protein, varPos, wt, 1, filename, path_to_output_files, file_type='pdb')
+#                         st.write('sasa', dataframe.loc[i, 'sasa'] )
+#                         st.write('model_id', model_id)
+#                         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:
+#                                 new_row = {'uniprotID': uniprot_id, 'wt': wt,
+#                                                'pos': varPos, 'mut': mut, 'datapoint': datapoint }
+#                                 no_modbase = no_modbase.append(new_row, ignore_index=True)
+                                
+#                 else:
+#                     new_row = {'uniprotID': uniprot_id, 'wt': wt,
+#                                                'pos': varPos, 'mut': mut, 'datapoint': datapoint }
+#                     no_modbase = no_modbase.append(new_row, 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'])]
+#     no_modbase = no_modbase[keep_cols]
+#     return modbase, no_modbase
+
 import requests
 import numpy as np
 import pandas as pd
@@ -73,7 +222,9 @@ def addModbaseModels(dataframe, path_to_input_files, path_to_output_files):
                         st.write('wt', wt)
                         st.write('protein', protein)
                         st.write('path_to_output_files', path_to_output_files)
-                        dataframe.loc[i, 'sasa'] = sasa(protein, varPos, wt, 1, filename, path_to_output_files, file_type='pdb')
+                        sasa_val = sasa(protein, varPos, wt, 1, filename, path_to_output_files, file_type='pdb')
+                        st.write('sasa', sasa_val)
+                        st.write('model_id', model_id)
                         with open(path_to_output_files / 'modbase_structures_individual'/ f'{model_id}.txt', encoding="utf8") as m:
 
                             lines = m.readlines()
@@ -106,13 +257,13 @@ def addModbaseModels(dataframe, path_to_input_files, path_to_output_files):
                                             break
                                 try:
                                     k = pd.Series(
-                                        [uniprot_id, target_begin, target_end,quality_score, model_id, coordDict, AAonPDB, coordVAR])
+                                        [uniprot_id, target_begin, target_end,quality_score, model_id, coordDict, AAonPDB, coordVAR, sasa_val])
                                     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}
+                                               'AAonPDB': AAonPDB, 'coordVAR': coordVAR, 'sasa':sasa_val}
                                     modbase_reduced = modbase_reduced.append(new_row, ignore_index=True)
-                                    modbase_reduced = modbase_reduced[['uniprotID', 'quality_score', 'model_id', 'coordinates', 'AAonPDB', 'coordVAR']]           
+                                    modbase_reduced = modbase_reduced[['uniprotID', 'quality_score', 'model_id', 'coordinates', 'AAonPDB', 'coordVAR', 'sasa']]           
                                     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)
@@ -142,6 +293,6 @@ def addModbaseModels(dataframe, path_to_input_files, path_to_output_files):
     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'])]
+    st.write(modbase['datapoint', 'model_id', 'sasa'])
     no_modbase = no_modbase[keep_cols]
-    return modbase, no_modbase
-    
+    return modbase, no_modbase