PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.

ABSTRACT: Understanding the impact of amino acid mutations on protein-protein binding free energy is fundamental to drug design and functional biology. We propose a novel approach utilizing Protein Large Language Models (PLMs) to characterize both wild-type and mutant proteins, enabling accurate predictions of mutational effects. We employed three state-of-the-art PLMsEsm2, EsmC, and ProtT5to generate sequence-based representations. These representations were subsequently integrated into seven distinct model architectures. Through a rigorous 5-fold cross-validation, we selected the optimal model and feature combination before training on a large-scale data set. Our results show that this PLM-based method significantly outperforms traditional approaches, achieving state-of-the-art (SOTA) predictive

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12631657 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature