Affiliation:
1. Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
2. Tulane University
School of Medicine, Department of Microbiology and Immunology, New Orleans, Louisiana, USA
Abstract
Abstract:
Lysine succinylation is a post-translational modification (PTM) of protein in which a succinyl
group (-CO-CH2-CH2-CO2H) is added to a lysine residue of protein that reverses lysine's positive
charge to a negative charge and leads to the significant changes in protein structure and function. It occurs
on a wide range of proteins and plays an important role in various cellular and biological processes
in both eukaryotes and prokaryotes. Beyond experimentally identified succinylation sites, there have
been a lot of studies for developing sequence-based prediction using machine learning approaches, because
it has the promise of being extremely time-saving, accurate, robust, and cost-effective. Despite
these benefits for computational prediction of lysine succinylation sites for different species, there are a
number of issues that need to be addressed in the design and development of succinylation site predictors.
In spite of the fact that many studies used different statistical and machine learning computational
tools, only a few studies have focused on these bioinformatics issues in depth. Therefore, in this comprehensive
comparative review, an attempt is made to present the latest advances in the prediction models,
datasets, and online resources, as well as the obstacles and limits, to provide an advantageous guideline
for developing more suitable and effective succinylation site prediction tools.
Funder
BANBEIS research project, Bangladesh
Publisher
Bentham Science Publishers Ltd.
Subject
Cell Biology,Molecular Biology,Biochemistry,General Medicine
Cited by
3 articles.
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