Protein-protein interactions (PPIs) play a crucial role in numerous
biological processes. Developing methods that predict binding affinity changes
under substitution mutations is fundamental for modelling and re-engineering
biological systems. Deep learning is increasingly recognized as a powerful tool
capable of bridging the gap between in-silico predictions and in-vitro
observations. With this contribution, we propose eGRAL, a novel SE(3)
equivariant graph neural network (eGNN) architecture designed for predicting
binding affinity changes from multiple amino acid substitutions in protein
complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to
features extracted from protein large language models. To address the limited
availability of large-scale affinity assays with structural information, we
generate a simulated dataset comprising approximately 500,000 data points. Our
model is pre-trained on this dataset, then fine-tuned and tested on
experimental data.

通过深度学习及图神经网络，使用蛋白质的大规模语言模型提取特征，针对蛋白质复合物中的多个氨基酸替代预测结合亲和力的变化

应用 eGNNs 在残基和原子图与语言模型信息相结合下，预测多突变下的蛋白结合亲和力：eGRAL

Protein binding affinity prediction under multiple substitutions  applying eGNNs on Residue and Atomic graphs combined with Language model  information: eGRAL

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。