E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.

ABSTRACT: Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to i

SUBMITTER: Roche R 

PROVIDER: S-EPMC10499216 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature