Trends Assessment of the Genetic Mutation Induced Protein-Protein Interaction Variation via Protein Large Language Driven Method

Abstract

Protein-protein interaction (PPI) variations are widely observed in many principal biological processes related to various diseases, while genetic mutation is one of the most common factors resulting in those variations. Unraveling these underlying biomolecular interactions is a key challenge that hinders a profound understanding of disease mechanisms. Although current models can accurately predict quantitative binding affinity, their application in pathogenic research is limited by difficulties in defining trend types, particularly extreme "disrupting" cases, along with poor generalizability beyond single-point mutations (SNP) and insufficient biological interpretability. Here, we present TAPPI, an interpretable large language model-driven deep learning framework, to perform Trends Assessment of Protein-Protein Interaction. It categorizes PPI variations trends into four types: disrupting, decreasing, no effect, and increasing, enabling fine-grained functional assessments and bridging genetic mutation and disease mechanisms. The framework generalizes effectively across both single-point and multi-point mutations and captures complex trend outcomes relevant to diverse disease landscapes. In benchmark evaluations, TAPPI achieved the state-of-the-art prediction performance and demonstrated biological relevance through interpretable results. In external datasets from Autism Spectrum Disorder, Lennox-Gastaut syndrome, and Epileptic Encephalopathies, given the premise that TAPPIs predicted results align with pathogenic patterns, these results reveal how genetic mutations indirectly perturb disease-associated pathways and supporting our perspective of "crucial disrupting" and "bridging mutations-disease". In summary, accurate, interpretable, and pathogenically relevant prediction of PPI variations trend predicted by TAPPI provides a novel approach for mechanistic discovery.