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The web-server mLysPTMpred used to predict the multiple K-PTM sites in protein. Protein post-translational modification (PTM) increases the functional diversity of the proteome and plays major role not only in orchestrating various biological processes but also associated with some diseases. It does modification by introducing new functional groups to the side chain of amino acid a protein. Among all amino acid residues, the side chain of lysine can experience many types of PTM, called K-PTM, such as ‘acetylation’, ‘crotonylation’, ‘methylation’ and ‘succinylation’ and also responsible for occurring multiple PTM in the same lysine of a protein which lead to the requirement of multi-label PTM site identification. In this context, in order to avoid the costly and time-consuming experimental technologies, an accurate computational method for predicting multi-label PTM sites is an urgent issue which can be useful for drug development. However, most of the existing computational methods have been established to predict various single-label PTM site. Therefore, in this study, a novel computational tool termed mLysPTMpred has been developed to predict multi-label lysine PTM sites by (1) incorporating the sequence-coupled information in to the general pseudo amino acid composition, (2) balancing the effect of skewed training dataset by Different Error Costs (DEC) method, and (3) constructing a multi-label predictor using a combination of support vector machine (SVM) as classifier. This mLysPTMpred predictor achieves an average accuracy score of 83.73 % in predicting the multi-label PTM site of K-PTM types. All of the experimental results along with accuracy are found from the average of 5 times complete run of 5-fold cross-validation set and indicate the significantly better performance than the existing predictors iPTM-mLys. A user-friendly web server for the mLysPTMpred is available at