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Precursor-Ion Mass Re-Estimation Improves Peptide Identification on Hybrid Instruments

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Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, Los Angeles, California 90048, Department of Chemistry & Biochemistry, University of California, Los Angeles, California 90095, Labkey Software, 312 N. 49th Street, Seattle, Washington 98103, and Fred Hutchinson Cancer Center, 312 N. 49th Street, Seattle, Washington 98103
* Address correspondence to: Parag Mallick, Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, 8750 West Beverly Blvd, Los Angeles, CA 90048. E-mail: [email protected]. Phone: 310-423-7600. Fax: 310-423-8543.
†Cedars-Sinai Medical Center.
‡University of California.
§Labkey Software.
∥Fred Hutchinson Cancer Center.
Cite this: J. Proteome Res. 2008, 7, 9, 4031–4039
Publication Date (Web):August 16, 2008
https://doi.org/10.1021/pr800307m
Copyright © 2008 American Chemical Society

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    Abstract

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    Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1−2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide’s parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.

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