Deep learning model of post-translational modification regulating liquid-liquid phase separation.

ABSTRACT: Liquid-liquid phase separation (LLPS) drives the formation of various membraneless organelles, which are crucial for biological processes and disease development. Despite the significant regulatory effects on LLPS of protein post-translational modifications (PTMs), specific data resource and predictor are still lacking. First, we constructed a well-curated database of PTM regulation on liquid-liquid Phase Separation (PTMPhaSe) ( https://ptmphase.sjtu.edu.cn ) that contains manually curated complete experimental evidence. Second, we developed graph neural network-based deep learning model (named PhosLLPS) to predict functional phosphorylation sites regulating LLPS, which achieved better identification performance (AUC = 0.9116) than four baseline models and the existing FuncPhos-SEQ method.

SUBMITTER: Hong X 

PROVIDER: S-EPMC12686406 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature