A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction
Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Elham Yavari
Reversible Post-Translational Modifications (PTMs) have vital roles in
extending the functional diversity of proteins and effect meaningfully the
regulation of protein functions in prokaryotic and eukaryotic organisms. PTMs
have happened as crucial molecular regulatory mechanisms that are utilized to
regulate diverse cellular processes. Nevertheless, among the most well-studied
PTMs can say mainly types of proteins are containing phosphorylation and
significant roles in many biological processes. Disorder in this modification
can be caused by multiple diseases including neurological disorders and
cancers. Therefore, it is necessary to predict the phosphorylation of target
residues in an uncharacterized amino acid sequence. Most experimental
techniques for predicting phosphorylation are time-consuming, costly, and
error-prone. By the way, computational methods have replaced these techniques.
These days, a vast amount of phosphorylation data is publicly accessible
through many online databases. In this study, at first, all datasets of PTMs
that include phosphorylation sites (p-sites) were comprehensively reviewed.
Furthermore, we showed that there are basically two main approaches for
phosphorylation prediction by machine learning: End-to-End and conventional. We
gave an overview for both of them. Also, we introduced 15 important feature
extraction techniques which mostly have been used for conventional machine
learning methods