Abstract
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.
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