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

• URL: http://arxiv.org/abs/2305.19801v1
• Date: Tue, 30 May 2023 14:48:06 GMT
• Title: Predicting protein stability changes under multiple amino acid substitutions using equivariant graph neural networks
• Authors: Sebastien Boyer, Sam Money-Kyrle, Oliver Bent
• Abstract summary: We propose improvements to state-of-the-art Deep learning (DL) protein stability prediction models. This was achieved using E(3)-equivariant graph neural networks (EGNNs) for both atomic environment (AE) embedding and residue-level scoring tasks. We demonstrate the immediately promising results of this procedure, discuss the current shortcomings, and highlight potential future strategies.
• Score: 2.5137859989323537
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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