mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

• 作者: Md. Al Mehedi Hasan Shamim Ahmad
• 来源期刊: 自然科学期刊(英文)
• 学科: 医学
• 关键词: MULTI-LABEL PTM Site Predictor Sequence-Coupling Model General PseAAC DATA IMBALANCE ISSUE Different Error Costs Support Vector Machine
• 年，卷（期）: 2018,（9）
• 页码范围: 370-384
• 页数: 15页
• 分类号: R73

摘要

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/.