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 automatically. PCBert-Kla exhibited exceptional accuracy and reliability in Kla site identification and demonstrated excellent generalization capability to outperform the existing models. In addition, we further enhanced the interpretability of the PCBert-Kla model by incorporating average attention maps. This model provided powerful tools for studying the functions of Kla and elucidating the mechanisms of related diseases, which can advance biomedical research and drug development. We also developed a free web service, available at http://pcbert-kla.lin-group.cn/, to provide users with easy access and usage.