Exploiting the Dynamic Relationship between Peptide Separation Quality and Peptide Coisolation in a Multiple-Peptide Matches-per-Spectrum Approach Offers a Strategy To Optimize Bottom-Up Proteomics Throughput and Depth

• Published in: Analytical chemistry (Washington) Vol. 91; no. 11; pp. 7273 - 7279
• Main Authors: Villalobos Solis, Manuel I, Giannone, Richard J, Hettich, Robert L, Abraham, Paul E
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 04-06-2019; American Chemical Society (ACS)
• Subjects: Algorithms; Analytical chemistry; Chemistry; High performance liquid chromatography; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Liquid chromatography; Peptides; Proteomics; Separation; Spectra; Strategy
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.9b00819

Peptide cofragmentation leads to chimeric MS/MS spectra that negatively impact traditional single-peptide match-per-spectrum (sPSM) search strategies in proteomics. The collection of chimeric spectra is influenced by peptide coelution and the width of precursor isolation windows. Although peptide cofragmentation can be reduced by advanced chromatography, such as UHPLC and 2D-HPLC separation schemes, and narrower isolation windows, chimeric spectra can still be as high as 30–50% of the total MS/MS spectra collected. Alternatively, cofragmented peptides in chimeric spectra and the use of wider isolation windows benefit multiple-peptide matches-per-spectrum (mPSM) algorithms, such as CharmeRT, which facilitate the identification of several cofragmented peptides. Considering recent advancements in LC and mPSM methodologies, we present a comprehensive examination of the levels of chimeric spectra collected in the analysis of a HeLa digest measured using different LC modes of separation and isolation windows and compare the depth of identifications obtained when these data are annotated using a sPSM or a mPSM approach. Our results demonstrate that MS/MS data derived from 1D-HPLC strategies under different gradient schemes and searched with CharmeRT yielded higher average numbers of PSMs (11%–49%), peptide analytes (10%–16%), and peptide sequences (3%–10%) compared to data derived from 1D-UHPLC runs but searched with a sPSM strategy. Interestingly, data from a 2D-HPLC separation strategy benefits more from the application of CharmeRT results when compared to a 50 cm 1D-UHPLC column employing a 500 min gradient. Overall, these results provide new insights into how to better configure LC-MS/MS measurements for improved throughput and peptide identification in complex proteomes.