Protein post-translational modifications (PTMs) are critical for modulating protein structure and function. Among these, lysine lactylation (Kla) has garnered significant attention in recent years as a newly discovered PTM. Although Kla has been thoroughly investigated in eukaryotic systems, its study in prokaryotes, especially bacteria, remains comparatively limited. Emerging research highlights that bacterial Kla, operating through dynamic modification mechanisms, is pivotal in processes such as growth and metabolism, virulence control, pathogenicity, and host-pathogen interactions. This article provides a comprehensive overview of the latest progress in bacterial Kla research, emphasizing its historical discovery, distinct modification features, and underlying molecular regulatory mechanisms. We further explore the regulatory roles of this modification in bacterial physiological processes and pathogenesis, concluding with a discussion of current research challenges and prospective future developments.
Protein lysine β-hydroxybutyrylation (Kbhb) is a newly discovered post-translational modification associated with a wide range of biological processes. Identifying Kbhb sites is critical to better understanding its mechanism of action. However, biochemical experimental methods for probing Kbhb sites are costly and have a long cycle. Therefore, a feature embedding learning method based on the Transformer encoder was proposed to predict Kbhb sites. In this method, amino acid residues were mapped into numerical vectors according to their amino acid class and position in a learnable feature embedding method, and then the Transformer encoder was used to extract discriminating features, and the bidirectional long short-term memory network (BiLSTM) was used to capture the correlation between different features. In this paper, a benchmark dataset was constructed, and a Kbhb site predictor, AutoTF-Kbhb, was implemented based on the proposed method. Experimental results showed that the proposed feature embedding learning method could extract effective features. AutoTF-Kbhb achieved an area under curve (AUC) of 0.87 and a Matthews correlation coefficient (MCC) of 0.37 on the independent test set, significantly outperforming other methods in comparison. Therefore, AutoTF-Kbhb can be used as an auxiliary means to identify Kbhb sites.