Data Self-Recalibration and Mixture Mass Fingerprint Searching (DASER-MMF) to Enhance Protein Identification within Complex Mixtures

Ryan M. Danell, Severine A. Ouvry-Patat, Cameron O. Scarlett, J. Paul Speir, Christoph H. Borchers

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

A novel algorithm based on Data Self-Recalibration and a subsequent Mixture Mass Fingerprint search (DASER-MMF) has been developed to improve the performance of protein identification from online 1D and 2D-LC-MS/MS experiments conducted on high-resolution mass spectrometers. Recalibration of 40% to 75% of the MS spectra in a human serum dataset is demonstrated with average errors of 0.3 ± 0.3 ppm, regardless of the original calibration quality. With simple protein mixtures, the MMF search identifies new proteins not found in the MS/MS based search and increases the sequence coverage for identified proteins by six times. The high mass accuracy allows proteins to be identified with as little as three peptide mass hits. When applied to very complex samples, the MMF search shows less dramatic performance improvements. However, refinements such as additional discriminating factors utilized within the search space provide significant gains in protein identification ability and indicate that further enhancements are possible in this realm.

Original languageEnglish
Pages (from-to)1914-1925
Number of pages12
JournalJournal of the American Society for Mass Spectrometry
Volume19
Issue number12
DOIs
Publication statusPublished - Dec 2008
Externally publishedYes

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