title: Data Self-Recalibration and Mixture Mass Fingerprint Searching (DASER-MMF) to enhance protein identification within complex mixtures.
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.
authors: Danell Consulting, Greenville, North Carolina, USA.
authors: Ryan M
authors: RM
authors: Danell
authors: Severine A
authors: SA
authors: Ouvry-Patat
authors: Cameron O
authors: CO
authors: Scarlett
authors: J Paul
authors: JP
authors: Speir
authors: Christoph H
authors: CH
authors: Borchers
mesh_terms: D000465:Algorithms; D000818:Animals; D001798:Blood Proteins / Q000737:chemistry; D002417:Cattle; D002853:Chromatography, Liquid; D030562:Databases, Protein; D006801:Humans; D010449:Peptide Mapping / Q000706:statistics & numerical data*; D011506:Proteins / Q000737:chemistry*; D053719:Tandem Mass Spectrometry
chemical_list: D001798:Blood Proteins; D011506:Proteins