Key Laboratory of Rice Biology in Henan Province, Henan Agricultural University, Zhengzhou, China.
Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
Methods Mol Biol. 2022;2499:205-219. doi: 10.1007/978-1-0716-2317-6_11.
Among various types of protein post-translational modifications (PTMs), lysine PTMs play an important role in regulating a wide range of functions and biological processes. Due to the generation and accumulation of enormous amount of protein sequence data by ongoing whole-genome sequencing projects, systematic identification of different types of lysine PTM substrates and their specific PTM sites in the entire proteome is increasingly important and has therefore received much attention. Accordingly, a variety of computational methods for lysine PTM identification have been developed based on the combination of various handcrafted sequence features and machine-learning techniques. In this chapter, we first briefly review existing computational methods for lysine PTM identification and then introduce a recently developed deep learning-based method, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs). Specifically, MUscADEL employs bidirectional long short-term memory (BiLSTM) recurrent neural networks and is capable of predicting eight major types of lysine PTMs in both the human and mouse proteomes. The web server of MUscADEL is publicly available at http://muscadel.erc.monash.edu/ for the research community to use.
在各种类型的蛋白质翻译后修饰（PTMs）中，赖氨酸 PTMs 在调节广泛的功能和生物过程中起着重要作用。由于正在进行的全基因组测序项目产生和积累了大量的蛋白质序列数据，因此系统地识别整个蛋白质组中不同类型的赖氨酸 PTM 底物及其特定的 PTM 位点变得越来越重要，并因此受到了广泛关注。相应地，已经基于各种手工制作的序列特征和机器学习技术的组合开发了各种用于赖氨酸 PTM 识别的计算方法。在本章中，我们首先简要回顾现有的赖氨酸 PTM 识别计算方法，然后介绍一种新开发的基于深度学习的方法，称为 MUscADEL（用于赖氨酸 PTM 的多可扩展准确深度学习器）。具体来说，MUscADEL 采用双向长短期记忆（BiLSTM）递归神经网络，能够预测人类和小鼠蛋白质组中的八种主要类型的赖氨酸 PTM。MUscADEL 的网络服务器可在 http://muscadel.erc.monash.edu/ 上公开获取，供研究界使用。
