Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments

Area of Science:

• Proteomics
• Analytical Chemistry
• Bioinformatics

Background:

• Targeted mass spectrometry, particularly selected reaction monitoring (SRM), is vital for quantifying low-abundant proteins.
• Designing SRM assays is challenging due to variable peptide signal responses and the difficulty in selecting representative peptides.

Purpose of the Study:

• To introduce PREGO, a novel software tool for predicting high-responding peptides for SRM experiments.
• To improve the efficiency and accuracy of SRM assay design for proteomic quantification.

Main Methods:

• Developed PREGO using an artificial neural network trained on 11 minimally redundant, relevant properties.
• Trained the model using fragment ion intensities of equimolar synthetic peptides from data-independent acquisition (DIA) experiments.
• Validated PREGO's predictions against a large-scale SRM experiment.

Main Results:

• PREGO demonstrates a 40-85% improvement over existing methods in selecting high-responding peptides.
• The software shows significant advantages over commonly used rules-based peptide selection approaches.
• Relative peptide responses from DIA experiments serve as a suitable substitute for SRM experiments.

Conclusions:

• PREGO offers a substantial advancement in selecting peptides for SRM assays, enhancing proteomic studies.
• The tool streamlines the laborious process of SRM assay design, leading to more reliable quantitative results.
• PREGO's predictive capability facilitates more effective detection of low-abundant proteins.