Data Resources and Computational Methods for Lactylation Site Prediction: A Mini-Review

Lysine lactylation (Kla), a novel post-translational modification (PTM) discovered in 2019, establishes a critical link between cellular metabolism and epigenetic regulation. A growing number of studies have reported that it is involved in several physiological and pathological processes. Traditional experimental methods for identifying Kla sites are time-consuming and labor-intensive; in contrast, computational prediction models offer efficient and systematic alternatives for high-throughput screening of potential modification sites. In this review, we summarize computational methods and data resources used for Kla site prediction. The biological roles of Kla in major human diseases, such as cancers, cardiovascular diseases, and neurological diseases, were summarized. Furthermore, a summary and comprehensive overview of seven Kla site prediction models is presented, covering dataset construction, methodological principles, and evaluation methods. Finally, the challenges and future trends in Kla prediction have been discussed.