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Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

General Review Article

A Comprehensive Comparative Review of Protein Sequence-Based Computational Prediction Models of Lysine Succinylation Sites

Author(s): Samme Amena Tasmia, Md. Kaderi Kibria, Md. Ariful Islam, Mst Shamima Khatun and Md. Nurul Haque Mollah*

Volume 23, Issue 11, 2022

Published on: 10 August, 2022

Page: [744 - 756] Pages: 13

DOI: 10.2174/1389203723666220628121817

Price: $65

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.

Keywords: Post-translational modification, Lysine succinylation, sequence analysis, machine learning, tool development, feature descriptor.

Graphical Abstract


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