Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review

Yanwen       Li; Feng       Pu; Jingru       Wang; Zhiguo       Zhou; Chunhua       Zhang; Fei       He; Zhiqiang       Ma; Jingbo       Zhang
doi:10.2174/1381612826666201112142826
Abstract

Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.
Keywords: Palmitoylation, machine learning methods, benchmark, feature extraction, bioinformatics, post-translational, post-translational lipid modification.
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DOI https://dx.doi.org/10.2174/1381612826666201112142826	Print ISSN 1381-6128
Publisher Name Bentham Science Publisher	Online ISSN 1873-4286
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