Optimization of Data-Dependent Acquisition Parameters for Coupling High-Speed Separations with LC-MS/MS for Protein Identifications
Year: 2013
Type: article
Abstract: Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.
Authors: Darryl Johnson, Barry Boyes, Taylor Fields, Rachel Kopkin, Ron Orlando
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: bronze
Awards: G3023704115, Novel Materials and Methods for Separations of Glycopeptides and Glycans, Fused-core Particles for Ultra-fast Proteomic and Genomic Separations, G6709846760, Integrated Technology Resource for Biomedial Glycomics, Controlled Surface Porosity Particles for Ultra-fast Life Science Separations, Novel Materials and Methods for Separations of Glycopeptides and Glycans
OpenAlex ID: W2033206987