DeepKhib: A Deep-Learning Framework for Lysine 2-Hydroxyisobutyrylation Sites Prediction.

As a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (K ) plays an important role in gene transcription and signal transduction. In order to understand its regulatory mechanism, the essential step is the recognition of K sites. Thousands of K sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict K sites for limited species, lacking a general prediction algorithm. We constructed a deep-learning algorithm based on convolutional neural network with the one-hot encoding approach, dubbed CNN . It performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the ROC curve (AUC) values for CNN ranged from 0.82 to 0.87 for different organisms, which is superior to the currently available K predictors. Moreover, we developed the general model based on the integrated data from multiple species and it showed great universality and effectiveness with the AUC values in the range of 0.79-0.87. Accordingly, we constructed the on-line prediction tool dubbed DeepKhib for easily identifying K sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo.org/DeepKhib.