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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