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Protein cysteine S-sulfenylation is an essential and reversible post-translational modification that plays a crucial role in transcriptional regulation, stress response, cell signaling and protein function. Studies have shown that S-sulfenylation is involved in many human diseases such as cancer, diabetes and arteriosclerosis. However, experimental identification of protein S-sulfenylation sites is generally expensive and time-consuming. In this study, we proposed a new protein S-sulfenylation sites prediction method SulSite-GTB. First, fusion of amino acid composition, dipeptide composition, encoding based on grouped weight, K nearest neighbors, position-specific amino acid propensity, position-weighted amino acid composition and pseudo-position specific score matrix feature extraction to obtain the initial feature space. Secondly, we use the synthetic minority oversampling technique (SMOTE) algorithm to process the class imbalance data, and the least absolute shrinkage and selection operator (LASSO) are employed to remove the redundant and irrelevant features. Finally, the optimal feature subset is input into the gradient tree boosting classifier to predict the S-sulfenylation sites, and the five-fold cross-validation and independent test set method are used to evaluate the prediction performance of the model. Experimental results showed the overall prediction accuracy is 92.86% and 88.53%, respectively, and the AUC values are 0.9706 and 0.9425, respectively, on the training set and the independent test set. Compared with other prediction methods, the results show that the proposed method SulSite-GTB is significantly superior to other state-of-the-art methods and provides a new idea for the prediction of post-translational modification sites of other proteins. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SulSite-GTB/. [ABSTRACT FROM AUTHOR]

Malonylation is a new protein post-translational modification and regulates a variety of cellular physiological processes. However, it is costly and time-consuming to identify malonylation sites through traditional experiments. Therefore, the prediction of malonylation sites by computational methods plays an important role in experimental design. In this paper, a new prediction model of malonylation sites, Malsite-Deep, is proposed. First, the seven feature extraction methods are used to extract feature information of protein sequences. Then, the under-sampling NearMiss-2 method is applied to handle imbalance data, and the update gate and reset gate of gated recurrent units (GRU) are used to select the optimal feature subset. Finally, the data from GRU layer is input into deep neural networks (DNN) to predict the malonylation sites, and the model performance is evaluated by 10-fold cross-validation and independent test sets. The 10-fold cross-validation shows that the AUC value on the training dataset reaches 0.99. The AUC values on the four independent test datasets all reach above 0.95. Results suggest that Malsite-Deep presented here facilitates the identification of protein malonylation sites. • A novel method (Malsite-Deep) is used to predict malonylation sites. • The BE, PWAA, BPB, EAAC, DC, EBGW and BLOSUM62 methods are fused to extract protein sequence features information. • NearMiss-2 algorithm is used to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the GRU and DNN to predict the malonylation sites. [ABSTRACT FROM AUTHOR]