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

Abstract

As a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (K _hib ) 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 _hib sites. Thousands of K _hib sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict K _hib 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 _OH . 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 _OH ranged from 0.82 to 0.87 for different organisms, which is superior to the currently available K _hib 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 _hib sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo.org/DeepKhib.

Keywords: deep learning; lysine 2-hydroxyisobutyrylation; machine learning; modification site prediction; post-translational modification.