Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of chemical information and modeling
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/.
14 July 2026
Read appraisal →Biogerontology
RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis
Aging is a progressive and irreversible biological process that contributes to the pathogenesis of numerous age-related diseases. Elucidating the molecular mechanisms of aging is crucial for promoting healthy aging and extending healthspan. RNA-binding proteins (RBPs) are pivotal regulators of post-transcriptional gene expression and play essential roles in diverse biological processes. RBPs interact with both coding and non-coding RNAs to regulate RNA metabolism, stability, localization, and translation. Dysregulated RBP-RNA interactions have been closely associated with aging and age-related diseases. This review systematically summarizes the structural characteristics of RBPs and the evolution of methods used to study them. We focus on the molecular mechanisms of six key RBPs, namely HuR, AUF1, TTP, IGF2BP2, QKI, and LARP7, in the context of aging and age-related diseases. In addition, we discuss the regulatory functions of post-translational modifications of RBPs. Furthermore, we provide a multidimensional overview of RBP involvement in aging and age-related diseases through large language model (LLM)-based text-mining analysis. Our study provides a foundation for the comprehensive characterization of RBPs in aging and age-related diseases.
22 June 2026
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