Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
Tulane University School of Medicine, Department of Microbiology and Immunology, New Orleans, Louisiana, USA.
Curr Protein Pept Sci. 2022;23(11):744-756. doi: 10.2174/1389203723666220628121817.
Lysine succinylation is a post-translational modification (PTM) of protein in which a succinyl group (-CO-CH-CH-COH) 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.
赖氨酸琥珀酰化是蛋白质的一种翻译后修饰（PTM），其中琥珀酰基（-CO-CH-CH-COH）被添加到蛋白质的赖氨酸残基上，使赖氨酸的正电荷变为负电荷，并导致蛋白质结构和功能的显著变化。它发生在广泛的蛋白质上，并在真核生物和原核生物的各种细胞和生物过程中发挥重要作用。除了实验鉴定的琥珀酰化位点外，已经有很多使用机器学习方法进行基于序列的预测的研究，因为它有望非常省时、准确、稳健且具有成本效益。尽管这些优势可用于不同物种的赖氨酸琥珀酰化位点的计算预测，但在琥珀酰化位点预测器的设计和开发中仍需要解决一些问题。尽管许多研究使用了不同的统计和机器学习计算工具，但只有少数研究深入探讨了这些生物信息学问题。因此，在本次全面的比较综述中，我们试图介绍预测模型、数据集和在线资源方面的最新进展，以及障碍和限制，为开发更合适、更有效的琥珀酰化位点预测工具提供有益的指导。
