ProAffinity++

Proteins are essential biological macromolecules that play a crucial role in living organisms. Protein-protein interactions, which govern various biological processes such as signal transduction, cell metabolism, and cell growth, are key aspect of protein function. The strength of these interactions, characterized by protein-protein binding affinity( Δ G), is a critical thermodynamic property of protein complexes. Accurate prediction of protein-protein binding affinity is meaningful for understanding the mechanisms of biological systems and improving the speed of binding affinity determination. However, current methods are limited in their accuracy and are primarily designed for binary complexes. To address these limitations, we propose ProAffinity++, an end-to-end algorithm that predicts binding affinities using a combined representation of sequence and structure. The core idea of ProAffinity++ is to model the binding regions of protein complexes and utilize graph neural networks to capture the local microenvironment of residues. Our experimental evaluations demonstrate that ProAffinity++ outperforms state-of-the-art methods in predicting protein-protein binding affinities. Moreover, it exhibits remarkable performance in challenging scenarios, such as antigen-antibody interactions and missense mutation problems, where existing methods have limitations.