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

TL;DR

This paper introduces an advanced deep learning approach using E(3)-equivariant graph neural networks to accurately predict protein stability changes caused by multiple amino acid substitutions, leveraging a large new dataset for training.

Contribution

It presents a novel method that decouples atomic and residue scales in protein representations, enabling predictions for variable numbers of substitutions with improved accuracy.

Findings

Effective prediction of stability changes for multiple mutations.

Utilization of a large-scale experimental dataset for training.

Promising initial results demonstrating method's potential.

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 ($\Delta\Delta G$). 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.