Current computational tools for protein lysine acylation site prediction

文献类型： 外文期刊

第一作者： Qin, Zhaohui

作者： Qin, Zhaohui;Ren, Haoran;Wang, Kaiyuan;Liu, Huixia;Miao, Chunbo;Du, Yanxiu;Li, Junzhou;Chen, Zhen;Zhao, Pei;Wu, Liuji

作者机构：

关键词： post-translation modification; lysine acylation; deep learning; protein language model; transfer learning

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：7.7； 五年影响因子：8.7 ）

ISSN： 1467-5463

年卷期： 2024 年 25 卷 6 期

页码：

收录情况： SCI

摘要： As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

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