DIA-MS2pep: a library-free framework for comprehensive peptide identification from data-independent acquisition data

Area of Science:

• Proteomics and Mass Spectrometry
• Bioinformatics and Computational Biology

Background:

• Data-independent acquisition (DIA) mass spectrometry enables deep proteome profiling but faces challenges in peptide identification due to complex MS/MS spectra.
• Conventional spectral library-based methods limit discovery potential and are constrained by library depth.

Purpose of the Study:

• To introduce DIA-MS2pep, a library-free computational framework for comprehensive peptide identification directly from DIA data.
• To overcome limitations of existing methods and enhance the discovery potential of DIA datasets.

Main Methods:

• DIA-MS2pep employs a data-driven algorithm for MS/MS spectrum demultiplexing using fragment ion data, independent of precursor information.
• A large precursor mass tolerance database search is utilized to identify peptides and their potential modifications.

Main Results:

• DIA-MS2pep demonstrates improved accuracy and sensitivity in peptide identification compared to existing library-free tools across diverse DIA datasets (e.g., HeLa, phosphopeptides, plasma).
• Spectral libraries generated using DIA-MS2pep from DIA data enhance the accuracy and reproducibility of quantitative proteomic analyses.

Conclusions:

• DIA-MS2pep provides a robust library-free solution for comprehensive peptide identification in DIA proteomics.
• The framework expands the utility of DIA data, enabling deeper proteome coverage and more reliable quantification.