Yang Yi, Qiao Liang
Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
Proteomics. 2023 Apr;23(7-8):e2200046. doi: 10.1002/pmic.202200046. Epub 2022 Sep 7.
Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, that is, detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements.
蛋白质翻译后修饰（PTMs）增加了细胞蛋白质组的功能多样性。蛋白质PTMs的准确且高通量鉴定和定量是蛋白质组学研究中的一项关键任务。数据非依赖型采集（DIA）质谱（MS）技术的最新进展已实现了对蛋白质和PTMs的深度覆盖及准确定量。本综述概述了DIA数据处理方法，这些方法涵盖了PTMs分析的三个方面，即PTMs的检测、位点定位以及复杂修饰部分（如糖基化）的表征。此外，还介绍了促进基于DIA的PTMs分析的深度学习方法，包括虚拟谱库生成以及特征评分和错误率控制。同时也讨论了用于PTMs分析的DIA方法的局限性和未来方向。新颖的数据分析方法将利用先进的MS仪器技术，增强DIA MS进行深入且准确的PTMs测量的能力。
