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Lysine lactylation (Kla), a novel lactate-derived post-translational modification, is involved in a myriad of biological processes and complex diseases. While several computational methods have been developed to identify Kla sites, these approaches still suffer from small datasets. In this work, we collected 23 984 Kla sites in 7297 proteins from the literature to construct the benchmark dataset. Leveraging recent advances in feature encoding, we tailored a multi-feature hybrid system, which integrated eight complementary feature–encoding strategies derived from two automated encoders and a composition-based module. Combining the hybrid system with deep learning, we presented our newly designed predictor named HybridKla, achieving an area under the curve (AUC) value of 0.8460. Compared to existing tools, HybridKla achieved &gt;28.90% improvement of the AUC value (0.8460 versus 0.6563). we also conducted a proteome-wide search and provided a systematic prediction of Kla sites. The friendly online service of HybridKla is freely accessible for academic research at  http://transkla.zzu.edu.cn/ .

HybridKla: a hybrid deep learning framework for lactylation site prediction