Mapping Protein–Protein Interactions Using Data-Dependent Acquisition without Dynamic Exclusion

• Published in: Analytical chemistry (Washington) Vol. 94; no. 30; pp. 10579 - 10583
• Main Authors: Zhang, Shen, Larsen, Brett, Colwill, Karen, Wong, Cassandra J., Youn, Ji-Young, Gingras, Anne-Claude
• Format: Journal Article
• Language: English
• Published: Washington American Chemical Society 02.08.2022
• Subjects: Analytical chemistry; Chemistry; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Peptide mapping; Protein interaction; Proteins; Proteomics; Reproducibility; Spectral sensitivity; Standard data
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.2c00755

Systematic analysis of affinity-purified samples by liquid chromatography coupled to mass spectrometry (LC-MS) requires high coverage, reproducibility, and sensitivity. While data-independent acquisition (DIA) approaches improve the reproducibility of protein–protein interaction detection as compared to standard data-dependent acquisition approaches, the need for library generation reduces their throughput, and analysis pipelines are still being optimized. In this study, we report the development of a simple and robust approach, termed turboDDA, to improve interactome analysis using spectral counting and data-dependent acquisition (DDA) by eliminating the dynamic exclusion (DE) step and optimizing the acquisition parameters. Using representative interaction and proximity proteomics samples, we detected increases in identified interactors of 18–71% compared to all samples analyzed by standard DDA with dynamic exclusion and for most samples analyzed by DIA with the MSPLIT-DIA spectral counting approach. In summary, turboDDA provides better sensitivity and identifies more high-confident interactors than the optimized DDA with DE and comparable or better sensitivity than DIA spectral counting approaches.