DeepVASP-E: A Flexible Analysis of Electrostatic Isopotentials for Finding and Explaining Mechanisms that Control Binding Specificity

Abstract

Amino acids that play a role in binding specificity can be identified with many methods, but few techniques identify the biochemical mechanisms by which they act. To address a part of this problem, we present DeepVASP-E, an algorithm that can suggest electrostatic mechanisms that influence specificity. DeepVASP-E uses convolutional neural networks to classify an electrostatic representation of ligand binding sites into specificity categories. It also uses class activation mapping to identify regions of electrostatic potential that are salient for classification. We hypothesize that electrostatic regions that are salient for classification are also likely to play a biochemical role in achieving specificity. Our findings, on two families of proteins with electrostatic influences on specificity, demonstrate that large salient regions can identify amino acids that have an electrostatic role in binding, and that DeepVASP-E is an effective classifier of ligand binding sites.