Predicting protein stability changes under multiple amino acid substitutions using equivariant graph neural networks

Sebastien Boyer, Sam Money-Kyrle, Oliver Bent

Keywords: Geometric Deep Learning, Protein Design, Equivariant Graph Neural Networks

TL;DR: Equivariant Graph Neural Networks applied to prediction protein stability changes under multiple amino acid substitutions, on both atomic and residue scales.

Abstract: The accurate prediction of changes in protein stability under multiple amino acid substitutions is essential for realising true in-silico protein re-design. To this purpose, we propose improvements to state-of-the-art Deep learning (DL) protein stability prediction models, enabling first-of-a-kind predictions for variable numbers of amino acid substitutions, on structural representations, by decoupling the atomic and residue scales of protein representations. This was achieved using E(3)-equivariant graph neural networks (EGNNs) for both atomic environment (AE) embedding and residue-level scoring tasks. Our AE embedder was used to featurise a residue-level graph, then trained to score mutant stability (∆∆G). To achieve effective training of this predictive EGNN we have leveraged the unprecedented scale of a new high-throughput protein stability experimental data-set, Mega-scale. Finally, we demonstrate the immediately promising results of this procedure, discuss the current shortcomings, and highlight potential future strategies.