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

Anal Chem. 2022 Aug 2;94(30):10579-10583. doi: 10.1021/acs.analchem.2c00755. Epub 2022 Jul 18.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatography, Liquid / methods
  • Mass Spectrometry / methods
  • Proteomics* / methods
  • Reproducibility of Results

Grants and funding