DSSA-PPI: Enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention

Abstract

Accurate assessment of the effects of mutations on protein-protein interactions (PPIs) is crucial for understanding disease pathogenesis and the development of targeted therapies. Here, we present DSSA-PPI, a hybrid deep learning framework that enhances the prediction of mutation-induced binding affinity changes (ΔΔG) by leveraging structural and sequence information through a disentangled attention mechanism. Building upon the complementary strengths of a geometric equivariant graph neural network PPIFormer and a protein language model ESM-2, our framework employs a novel representation learning strategy that integrates sequence- and structure-specific contributions, thereby improving the precision of PPI ΔΔG predictions. DSSA-PPI demonstrates robust performance across diverse mutational contexts on the standard protein binding affinity SKEMPI v2 dataset and outperforms existing methods on multiple benchmarks under identical cross-validation. In a case study of the SARS-CoV-2 receptor-binding motif (RBM) interaction with angiotensin-converting enzyme 2 (ACE2), our model accurately identifies top-ranking mutations that enhance binding affinity. Additionally, it guided the optimization of a peptide inhibitor, improving its inhibitory activity against activated factor XI (FXIa) by over 40-fold. These results highlight DSSA-PPI as a versatile and reliable tool for predicting mutation-induced perturbations in PPIs.