PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.

ABSTRACT: Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the perfo

SUBMITTER: Poretsky E 

PROVIDER: S-EPMC10732782 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature