update recalculation function

KosinskiLab · Jun 17, 2024 · 3655f21 · 3655f21
1 parent 82f2b3b
commit 3655f21
Showing 1 changed file with 35 additions and 33 deletions.
diff --git a/alphapulldown/folding_backend/alphafold_backend.py b/alphapulldown/folding_backend/alphafold_backend.py
@@ -441,40 +441,43 @@ def recalculate_confidence(prediction_results: Dict, multimer_mode:bool,
         A method that remove pae values of padded residues and recalculate iptm_ptm score again 
         Modified based on https://github.com/KosinskiLab/alphafold/blob/c844e1bb60a3beb50bb8d562c6be046da1e43e3d/alphafold/model/model.py#L31
         """
-        output = {}
-        plddt = prediction_results['plddt'][:total_num_res]
-        if 'predicted_aligned_error' in prediction_results:
-            ptm = confidence.predicted_tm_score(
-            logits=prediction_results['predicted_aligned_error']['logits'][:total_num_res,:total_num_res],
-            breaks=prediction_results['predicted_aligned_error']['breaks'],
-            asym_id=None)
-            output['ptm'] = ptm
-
-            pae = confidence.compute_predicted_aligned_error(
-            logits=prediction_results['predicted_aligned_error']['logits'],
-            breaks=prediction_results['predicted_aligned_error']['breaks'])
-            max_pae = pae.pop('max_predicted_aligned_error')
-
-            for k,v in pae.items():
-                output.update({k:v[:total_num_res, :total_num_res]})
-            output['max_predicted_aligned_error'] = max_pae
-            if multimer_mode:
-            # Compute the ipTM only for the multimer model.
-                iptm = confidence.predicted_tm_score(
+        if type(prediction_results['predicted_aligned_error']) == np.ndarray:
+            return prediction_results
+        else:
+            output = {}
+            plddt = prediction_results['plddt'][:total_num_res]
+            if 'predicted_aligned_error' in prediction_results:
+                ptm = confidence.predicted_tm_score(
                 logits=prediction_results['predicted_aligned_error']['logits'][:total_num_res,:total_num_res],
                 breaks=prediction_results['predicted_aligned_error']['breaks'],
-                asym_id=prediction_results['predicted_aligned_error']['asym_id'][:total_num_res],
-                interface=True)
-                output.update({'iptm' : iptm})
-                ranking_confidence = 0.8 * iptm + 0.2 * ptm
-                output.update({'ranking_confidence' : ranking_confidence})
-            if not multimer_mode:
-                # Monomer models use mean pLDDT for model ranking.
-                ranking_confidence =  np.mean(
-                    plddt)
-                output.update({'ranking_confidence' : ranking_confidence})
-
-            return output
+                asym_id=None)
+                output['ptm'] = ptm
+
+                pae = confidence.compute_predicted_aligned_error(
+                logits=prediction_results['predicted_aligned_error']['logits'],
+                breaks=prediction_results['predicted_aligned_error']['breaks'])
+                max_pae = pae.pop('max_predicted_aligned_error')
+
+                for k,v in pae.items():
+                    output.update({k:v[:total_num_res, :total_num_res]})
+                output['max_predicted_aligned_error'] = max_pae
+                if multimer_mode:
+                # Compute the ipTM only for the multimer model.
+                    iptm = confidence.predicted_tm_score(
+                    logits=prediction_results['predicted_aligned_error']['logits'][:total_num_res,:total_num_res],
+                    breaks=prediction_results['predicted_aligned_error']['breaks'],
+                    asym_id=prediction_results['predicted_aligned_error']['asym_id'][:total_num_res],
+                    interface=True)
+                    output.update({'iptm' : iptm})
+                    ranking_confidence = 0.8 * iptm + 0.2 * ptm
+                    output.update({'ranking_confidence' : ranking_confidence})
+                if not multimer_mode:
+                    # Monomer models use mean pLDDT for model ranking.
+                    ranking_confidence =  np.mean(
+                        plddt)
+                    output.update({'ranking_confidence' : ranking_confidence})
+
+                return output
 
     @staticmethod
     def postprocess(
@@ -540,7 +543,6 @@ def postprocess(
         for model_name, prediction_result in prediction_results.items():
             prediction_result.update(AlphaFoldBackend.recalculate_confidence(prediction_result,multimer_mode,
                                                                          total_num_res))
-            logging.info(f"prediction_result has keys : {prediction_result.keys()}")
             # Remove jax dependency from results
             np_prediction_result = _jnp_to_np(dict(prediction_result))
             # Save prediction results to pickle file