Generating high quality libraries for DIA MS with empirically corrected peptide predictions.

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Data-independent acquisition (DIA) proteomics typically requires extensive, experiment-specific spectral libraries.
• Generating these libraries often involves time-consuming offline fractionation and numerous injections.
• Existing methods are challenging for non-model organisms and non-canonical protein databases.

Purpose of the Study:

• To develop a streamlined workflow for generating comprehensive, experiment-specific peptide spectral libraries.
• To enable rapid proteomic analysis for organisms lacking well-established spectral libraries.
• To facilitate the detection of novel peptides, including those from non-canonical sources.

Main Methods:

• A novel library generation workflow integrating fragmentation and retention time prediction.
• Iterative refinement of predicted peptide libraries using empirical mass spectrometry data.
• Application to the malaria parasite Plasmodium falciparum and detection of missense variants in HeLa cells.

Main Results:

• Demonstrated a method for building proteome-wide peptide libraries without extensive offline fractionation.
• Successfully generated experiment-specific libraries for the non-model organism Plasmodium falciparum.
• Enabled the detection of missense variants using non-canonical databases in HeLa cell proteomic data.

Conclusions:

• The developed workflow significantly accelerates the generation of high-quality spectral libraries for DIA proteomics.
• This approach enhances the applicability of DIA to non-model organisms and complex biological samples.
• It provides a powerful tool for exploring proteomes and identifying genetic variations.