Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning

Recently, lysine lactylation (Kla), a novel post-translational modification (PTM), which can be stimulated by lactate, has been found to regulate gene expression and life activities. Therefore, it is imperative to accurately identify Kla sites. Currently, mass spectrometry is the fundamental method for identifying PTM sites. However, it is expensive and time-consuming to achieve this through experiments alone. Herein, we proposed a novel computational model, Auto-Kla, to quickly and accurately predict Kla sites in gastric cancer cells based on automated machine learning (AutoML). With stable and reliable performance, our model outperforms the recently published model in the 10-fold cross-validation. To investigate the generalizability and transferability of our approach, we evaluated the performance of our models trained on two other widely studied types of PTM, including phosphorylation sites in host cells infected with SARS-CoV-2 and lysine crotonylation sites in HeLa cells. The results show that our models achieve comparable or better performance than current outstanding models. We believe that this method will become a useful analytical tool for PTM prediction and provide a reference for the future development of related models. The web server and source code are available at http://tubic.org/Kla and https://github.com/ tubic/Auto-Kla, respectively.

Automated summary

Recently, a novel post-translational modification called lysine lactylation (Kla) has been discovered, which regulates gene expression and life activities through lactate stimulation. The accurate identification of Kla sites is essential. Mass spectrometry is currently the primary method for identifying PTM sites, but it is costly and time-consuming. Therefore, we proposed a novel computational model, Auto-Kla, based on automated machine learning (AutoML), which can predict Kla sites in gastric cancer cells quickly and accurately.