PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.

ABSTRACT: Protein post-translational modifications (PTMs) play a critical role in regulating protein functionality and structural diversity. Among them, lysine lactylation (Kla), a newly identified PTM, is involved in energy metabolism, cellular reprogramming, and the progression of various diseases. In this study, we propose PCBert-Kla, a feature-fusion deep learning model based on ProtBert. This model leverages ProtBert to extract deep features from protein sequences, effectively capturing global and local contextual information. It integrated various physicochemical properties, including molecular weight, isoelectric point, amino acid composition, secondary structure content, hydrophobicity, and net charge. An attention mechanism in the fully connected layers enabled the model to select features

SUBMITTER: Zhang HQ 

PROVIDER: S-EPMC12629241 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature