DeepKPred: Prediction and Functional Analysis of Lysine 2-Hydroxyisobutyrylation Sites Based on Deep Learning

Abstract

Protein 2-hydroxyisobutyrylation (Khib), a newly identified post-translational modification, plays a role in various cellular processes. To gain a comprehensive understanding of its regulatory mechanisms, it is crucial to identify the sites of 2-hydroxyisobutyrylation. Therefore, we developed a novel ensemble method, DeepKPred, for predicting species-specific 2-hydroxyisobutyrylation sites. We employed one-hot and AAindex encoding schemes to construct features from protein sequences and integrated two densely convolutional neural networks and two long short-term memory networks to build the model. In the 5-fold cross-validation dataset, DeepKPred achieved AUC values of 0.859, 0.804, 0.821, and 0.819 for Human, Candida albicans, Rice, Wheat, and Physcomitrella patens. Additionally, function analysis further indicated that different organisms tend to engage in distinct biological processes and pathways. Detailed analysis can help us learn more about the mechanism of 2-hydroxyisobutyrylation and provide insights for associated experimental verification.