A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction  

作　　者：Farzaneh Esmaili Mahdi Pourmirzaei Shahin Ramazi Seyedehsamaneh Shojaeilangari Elham Yavari 

机构地区：[1]Department of Information Technology,Tarbiat Modares University,Tehran 14115-111,Iran [2]Department of Biophysics,Faculty of Biological Sciences,Tarbiat Modares University,Tehran 14115-111,Iran [3]Biomedical Engineering Group,Department of Electrical Engineering and Information Technology,Iranian Research Organization for Science and Technology(IROST),Tehran 33535-111,Iran

出　　处：《Genomics, Proteomics & Bioinformatics》2023年第6期1266-1285,共20页基因组蛋白质组与生物信息学报（英文版）

摘　　要：Post-translational modifications(PTMs)have key 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 a significant role in many biological processes.Disorders in the phosphorylation process lead to multiple diseases,including neurological disorders and cancers.The purpose of this review is to organize this body of knowledge associated with phosphorylation site(p-site)prediction to facilitate future research in this field.At first,we comprehensively review all related databases and introduce all steps regarding dataset creation,data preprocessing,and method evaluation in p-site prediction.Next,we investigate p-site prediction methods,which are divided into two computational groups:algorithmic and machine learning(ML).Additionally,it is shown that there are basically two main approaches for p-site prediction by ML:conventional and end-to-end deep learning methods,both of which are given an overview.Moreover,this review introduces the most important feature extraction techniques,which have mostly been used in p-site prediction.Finally,we create three test sets from new proteins related to the released version of the database of protein post-translational modifications(dbPTM)in 2022 based on general and human species.Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release,distinct from those in the dbPTM 2019 release,reveals their limitations.In other words,the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research pape.

关 键 词：PHOSPHORYLATION MACHINELEARNING Deep learning Post-translational modification DATABASE 

分 类 号：Q811.4[生物学—生物工程]