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.

本文提出了一种基于 E (3)-equivariant graph neural networks (EGNNs) 的蛋白稳定性预测模型，该模型可以预测可变数目的氨基酸替换对蛋白稳定性的影响，并使用 Mega-scale 数据集进行有效训练。