DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites

Lysine β-hydroxybutyrylation (Kbhb) is a metabolism-linked post-translational modification (PTM) that plays a critical role in regulating gene expression, stress responses, and disease progression. Despite its emerging biological significance, identifying Kbhb sites remains limited due to the cost and complexity of experimental methods. Prior work such as KbhbXG is constrained by its reliance on hand-crafted features and lacks the ability to model contextual dependencies within sequences. To address this challenge, we present DeepKbhb, a deep learning framework designed for human Kbhb site identification. By integrating sequence embeddings and six engineered descriptors through a bilinear attention network, DeepKbhb effectively captures position-dependent relationships essential for accurate Kbhb site prediction. On an independent test set, DeepKbhb achieved state-of-the-art performance with an accuracy of 0.856, an F1-score of 0.863, and a Matthews correlation coefficient of 0.716. Experimental results across multiple evaluation metrics confirm the superior performance of DeepKbhb, highlighting its potential as a valuable tool for advancing Kbhb-related functional and mechanistic studies. This capability can further support disease-oriented research, particularly in cancer, metabolic disorders, and immune regulation. Further sequence analyses revealed distinct local amino acid preferences, supporting the biological relevance of our model. The web interface is accessible at https://awi.cuhk.edu.cn/~DeepKbhb/.