  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.
