Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
Department of Biomedical Sciences, The University of Hong Kong, Hong Kong, China.
Methods Mol Biol. 2022;2499:275-283. doi: 10.1007/978-1-0716-2317-6_14.
Posttranslational modifications (PTMs), which are processes of adding covalent groups in protein amino acids after the translation, play an important role in regulating proteins' localization, degradation, and functions. Different PTMs both within a single protein and across multiple proteins can work together or regulate reciprocally, known as PTM cross talk. However, high-throughput experimental identifications of PTM cross talk are lack due to technical limitations. In this chapter, we review in silico prediction approaches and illustrate the usage of PTM-X, a suite of recently proposed machine learning methods to predict both intra- and interprotein PTM cross talk.
翻译后修饰（PTMs）是蛋白质翻译后在氨基酸上添加共价基团的过程，在调节蛋白质的定位、降解和功能方面发挥着重要作用。同一蛋白质内和多个蛋白质之间的不同 PTM 可以协同作用或相互调节，称为 PTM 串扰。然而，由于技术限制，高通量实验鉴定 PTM 串扰的方法还很缺乏。在本章中，我们回顾了计算预测方法，并说明了 PTM-X 的使用，这是一套最近提出的机器学习方法，用于预测蛋白质内和蛋白质间的 PTM 串扰。
