Deepkcrot: A deep-learning architecture for general and species-specific lysine crotonylation site prediction

Abstract

Lysine crotonylation (Kcrot), as a post-translational modification (PTM) originally identified at histone proteins, is involved in diverse biological processes. Several conventional machine-learning (ML) predictors were developed based on the Kcrot sites from histone proteins. Recently, thousands of Kcrot sites have been experimentally verified on non-histone proteins from multiple species. Accordingly, a few predictors have been developed for predicting the Krot sites for specific organisms (i.e. humans and papaya). Nevertheless, there is a lack of research on the comparison of the crotonylomes of different organisms. Here, we collected around 20,000 Kcrot sites experimentally identified from four different species as the benchmark data set. We present the deep-learning (DL) architecture dubbed DeepKcrot for predicting Kcrot sites on the proteomes across various species. DeepKcrot includes species-specific and general classifiers using a convolutional neural network with the word embedding (CNNWE) encoding approach. CNNWE performs better than both the traditional ML-based and other DL-based classifiers in terms of ten-fold cross-validation and independent test, independent of the size of the training set. Additionally, cross-species performance for each species-specific predictor is not as good as the self-species performance whereas the cross-species performance generally increases with the size of the training dataset. Moreover, the predictors developed based on the non-histone Kcrot sites are unsuccessful for the histone Kcrot prediction, suggesting that the Kcrot-containing peptides from non-histone and histone proteins have significantly different characteristics and data integration is required. Overall, DeepKcrot is an efficient prediction tool and freely available at http://www.bioinfogo.org/deepkcrot.