A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction
This is an incremental review that addresses the problem of improving phosphorylation site prediction for bioinformatics and medical researchers, but it does not introduce new methods.
This paper reviewed machine learning techniques for predicting protein phosphorylation sites, and found that existing online tools perform poorly on new phospho-proteins from the 2022 dbPTM database, with results indicating weak performance.
Post-translational modifications (PTMs) have vital roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays significant roles in many biological processes. Disorders in the phosphorylation process lead to multiple diseases including neurological disorders and cancers. At first, this study comprehensively reviewed all databases related to phosphorylation sites (p-sites). Secondly, we introduced all steps regarding dataset creation, data preprocessing and method evaluation in p-sites prediction. Next, we investigated p-sites prediction methods which fall into two computational and Machine Learning (ML) groups. Additionally, it was shown that there are basically two main approaches for p-sites prediction by ML: conventional and End-to-End learning, which were given an overview for both of them. Moreover, this study introduced the most important feature extraction techniques which have mostly been used in ML approaches. Finally, we created three test sets from new proteins related to the 2022th released version of the dbPTM database based on general and human species. After evaluating available online tools on the test sets, results showed that the performance of online tools for p-sites prediction are quite weak on new reported phospho-proteins.