MethEvo: an accurate evolutionary information-based methylation site predictor

Sadia Islam, Shafayat Bin Shabbir Mugdha, Shubhashis Roy Dipta, Md. Easin Arafat, Swakkhar Shatabda, Hamid Alinejad-Rokny, Iman Dehzangi

Abstract: Post Translational Modification (PTM) plays an essential role in the biological and molecular mechanisms. They are also considered as a vital element in cell signaling and networking pathways. Among different PTMs, Methylation is regarded as one of the most important types. Methylation plays a crucial role in maintaining the dynamic balance, stability, and remodeling of chromatins. Methylation also leads to different abnormalities in cells and is responsible for many serious diseases. Methylation can be detected by experimental approaches such as methylation-specific antibodies, mass spectrometry, or characterizing methylation sites using the radioactive labeling method. However, these approaches are time-consuming and costly. Therefore, there is a demand for fast and accurate computational techniques to solve these issues. This study proposes a novel machine learning approach called MethEvo to predict methylation sites in proteins. To build this model, we use an evolutionary-based bi-gram profile approach to extract features. We also use SVM as our classification technique to build MethEvo. Our results demonstrate that MethEvo achieves 98.7%, 98.8%, 98.4%, and 0.974 in terms of accuracy, specificity, sensitivity, and Matthews Correlation Coefficient (MCC). MethEvo and its source code are publicly available at: https://github.com/islamsadia88/MethEvo.