  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 paper 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 reviewed all related databases and
introduced all steps regarding dataset creation, data preprocessing and method
evaluation in p-site prediction. Next, we investigated p-sites prediction
methods which fall into two computational groups: Algorithmic and Machine
Learning (ML). Additionally, it was shown that there are basically two main
approaches for p-sites prediction by ML: conventional and End-to-End deep
learning methods, 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 p-site prediction. 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. Evaluation of the available online tools on the
test sets showed quite poor performance for p-sites prediction. Keywords:
Phosphorylation, Machine Learning, Deep Learning, Post Translation
Modification, Databases
