editions added after revisions

Author: AmirAsgary

AFfine/run_prediction.py

@@ -170,7 +170,7 @@
         #(template_pdbfile, target_to_template_alignstring,
         # identities, target_len, template_len) = line[cols]
 
-        assert row.target_len == len(query_sequence), f'{row.target_len},{len(query_sequence)}'
+        assert row.target_len == len(query_sequence), f'{row.target_len},{len(query_sequence), row}'
         target_to_template_alignment = {
             int(x.split(':')[0]) : int(x.split(':')[1]) # 0-indexed
             for x in row.target_to_template_alignstring.split(';')
@@ -199,12 +199,6 @@
         msa = [query_sequence] + msa
 
 
-    # ── Build peptide mask per-target if sampling requested ──
-    all_metrics = process_target_with_ig_pipeline(
-            args, ig_config, targetl, query_sequence, query_chainseq,
-            all_template_features, model_runners, outfile_prefix,
-            crop_size, msa, deletion_matrix,
-        )
     # -------- End of V2 after sampling mode -------------- #
     all_metrics = process_target_with_ig_pipeline(
         args, ig_config, targetl, query_sequence, query_chainseq,

PANDORA/PANDORA/Pandora/Modelling_functions.py

@@ -22,6 +22,7 @@
 from copy import deepcopy
 import re
 import json
+import pandas as pd
 
 def _parse_release_date(s):
     """Parse a release-date string in any of the common formats. Returns a
@@ -680,7 +681,7 @@ def find_template(target, database, best_n_templates=1,
                 t_date = _parse_release_date(benchmark_release_dates_map[key])
                 if t_date is None:
                     continue
-                if t_date >= tgt_date:
+                if t_date > tgt_date:
                     excluded_by_date.append(ID)
                     putative_templates.pop(ID, None)
             print(f'[benchmark_before_date] Target {target.id} '

PANDORA/PANDORA/Pandora/Pandora.py

@@ -120,9 +120,7 @@ def find_template(self, best_n_templates=1, benchmark=False,
                                                                                         benchmark_exclude_ids=benchmark_exclude_ids,
                                                                                         benchmark_release_dates_map=benchmark_release_dates_map,  
                                                                                         target_release_date=target_release_date,
-                                                                                        benchmark_release_dates_map=benchmark_release_dates_map,   
-                                                                                        target_release_date=target_release_date,
-                                                                                        verbose=verbose                 
+                                                                                                        
                                                                                         )
 
             self.target.templates = [i.id for i in self.template]
@@ -460,7 +458,9 @@ def model(self, n_loop_models=20, n_homology_models=1,
                    benchmark=benchmark,
                    benchmark_similarity_threshold=benchmark_similarity_threshold, # added after review --> similarity threshold
                    benchmark_exclude_ids=benchmark_exclude_ids,
-                   verbose=verbose) 
+                   benchmark_release_dates_map=benchmark_release_dates_map,
+                   target_release_date=target_release_date,
+                   ) 
             except:
                 self.__log(self.target.id, 'None', 'Could not find a template')
                 raise Exception('Could not find a template')

run_utils.py

@@ -1031,7 +1031,8 @@ def run_single_proteinmpnn(path, directory, args, anchor_and_peptide=None):
         binder_pred=args.binder_pred,
         fix_anchors=args.fix_anchors,
         anchor_and_peptide=anchor_and_peptide,
-        model_name=args.proteinmpnn_model_name
+        model_name=args.proteinmpnn_model_name,
+        only_multichain=False, 
     )
     runner_mpnn.run() #