An efficient machine-learning framework for predicting protein post-translational modification sites.

ABSTRACT: Post-Translational Modifications (PTMs), particularly lysine 2-hydroxyisobutyrylation (Khib), represent critical regulatory mechanisms governing protein structure and function, with mounting evidence underscoring their important implications in cellular metabolism, transcriptional regulation, and pathological processes. Despite this significance, the experimental identification of Khib sites remains constrained by resource-intensive methodologies and the transient nature of these modifications. To overcome these limitations, we introduce HyLightKhib, a computational framework that leverages Light Gradient Boosting Machine architecture for accurate Khib site prediction. Our approach depends on a hybrid feature extraction strategy, integrating Evolutionary Scale Modeling (ESM-2) embeddings w

SUBMITTER: Elreify HM 

PROVIDER: S-EPMC12379237 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature