pub_soft/ScanNet
3
0
Fork 0
mirror of https://github.com/jertubiana/ScanNet.git synced 2026-06-04 13:44:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

Support for attention coefficient extraction

Browse Source
This commit is contained in:
Jérôme Tubiana
2021-11-10 18:00:29 +02:00
parent 1bd735a0fb
commit 2fc241af6d
4 changed files with 7 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
predict_bindingsites.py
View File

@@ -74,7 +74,7 @@ def write_predictions(csv_file, residue_ids, sequence, interface_prediction):
if interface_prediction.ndim == 1:
columns.append('Binding site probability')
else:
columns += ['Output %s' %i for i in range(len(interface_prediction) )]
columns += ['Output %s' %i for i in range(interface_prediction.shape[-1] )]
with open(csv_file, 'w') as f:
f.write(','.join(columns) + '\n' )
@@ -492,7 +492,7 @@ def predict_interface_residues(
for attention_coeff,neighborhood_graph in zip(attention_coeffs,neighborhood_graphs):
aggregated_attention_coeff = np.zeros(len(attention_coeff),dtype=np.float32)
for s in range( len(attention_coeff) ):
aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s],0).mean(
aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s][:len(neighborhood_graph[s])],0).mean(
-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
# aggregated_attention_coeff[neighborhood_graph[s]] += np.abs(attention_coeff[s]).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
aggregated_attention_coeffs.append(aggregated_attention_coeff)
@@ -556,7 +556,7 @@ def predict_interface_residues(
for attention_coeff,neighborhood_graph in zip(attention_coeffs,neighborhood_graphs):
aggregated_attention_coeff = np.zeros(len(attention_coeff),dtype=np.float32)
for s in range( len(attention_coeff) ):
aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s],0).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s][:len(neighborhood_graph[s])],0).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
aggregated_attention_coeffs.append(aggregated_attention_coeff)
aggregated_attention_coeffs = np.array(aggregated_attention_coeffs)
if layer == 'attention_layer':
@@ -611,6 +611,7 @@ def predict_interface_residues(
if not aggregate_models: # multioutput.
for layer_,prediction in zip(layer,predictions):
if layer_ is None:
prediction = prediction[:,1]
csv_file = query_output_folder + 'predictions_' + query_name + '.csv'
chimera_file = query_output_folder + 'chimera_' + query_names[i]
annotated_pdb_file = query_output_folder + 'annotated_' + query_names[i] + ('.cif' if file_is_cif else '.pdb')