Supplementary Figure 2: Analysis of MaSIF-ligand performance for specific cofactors.
From: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
a. Confusion matrix of ligand specificity on a MaSIF-ligand neural network trained with all features. Number of pockets in each category: ADP:146, CoA:46, FAD:71, HEME:68, NAD:49, NADP:28, SAM:43. b. Subset of the confusion matrices showing the importance of the features in distinguishing pockets between highly similar ligands. Number of pockets in each category: ADP:146, NAD:49, NADP:28, SAM:43. c. Analysis of MaSIF-ligand’s discrimination between NADP and NAD on two specific examples: a bacterial oxidoreductase and a human dehydrogenase. The bacterial dehydrogenase in the test set binds to NAD (PDB ID 2O4C), while its closest structural homologue in the training set corresponds to a mammalian oxidoreductase (PDB ID 2YJZ), which binds to NADP. Here we scored the pocket surface by a discrimination score, which scores each point in the protein surface by its weight in the neural network’s distinction between NADP and NAD. Surface regions with high importance are shown in red, while those of low importance are shown in blue.
