Data-independent acquisition proteomics methods for analyzing post-translational modifications

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.

Automated summary

Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate quantification and identification of protein PTMs is crucial in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) have enabled deep coverage and accurate quantification of PTMs. This review provides an overview of DIA data processing methods for PTMs analysis, as well as deep learning methods that enhance PTMs analysis. The limitations and future directions of DIA methods for PTMs analysis are also discussed.