%0 Journal Article %T mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue %A Md. Al Mehedi Hasan %A Shamim Ahmad %J Natural Science %P 370-384 %@ 2150-4105 %D 2018 %I Scientific Research Publishing %R 10.4236/ns.2018.109035 %X Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo 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 leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/. %K Multi-Label PTM Site Predictor %K Sequence-Coupling Model %K General PseAAC %K Data Imbalance Issue %K Different Error Costs %K Support Vector Machine %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=87688