1016-eSIG-Net: Decoding Protein Interaction Changes

Paper Talk · May 23, 2026 · 20m

The paper introduces eSIG-Net, a novel interaction language model designed to predict how single missense mutations disrupt protein-protein interactions (PPIs). Unlike traditional tools that rely on static structures or basic sequences, this framework utilizes mutation-centric encoding and constrained discrepancy learning to identify subtle "interaction cliffs" where tiny genetic changes cause massive functional shifts. Benchmark tests demonstrate that eSIG-Net significantly outperforms existing sequence-based and structure-based methods, achieving over 20% higher accuracy in identifying…