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Article

Identification of Protein Fragments as Pattern Features in MALDI−MS Analyses of Serum

Supporting Info

Lisa J. Zimmerman,*^†^§ Gregory R. Wernke,^§ Richard M. Caprioli,^†^‡^§ and Daniel C. Liebler^†^‡^§

Departments of Biochemistry and Pharmacology and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37232

J. Proteome Res., 2005, 4 (5), pp 1672–1680

DOI: 10.1021/pr050138m

Publication Date (Web): August 6, 2005

To whom correspondence should be addressed. Mass Spectrometry Research Center, Vanderbilt University School of Medicine, 9160 Medical Research Building III, 465 21st Avenue South, Nashville, TN 37232-8575. Tel: (615) 343-8431. Fax: (615) 343-8372. E-mail: lisa.j.zimmerman@ vanderbilt.edu.

†

Department of Biochemistry.

Mass Spectrometry Research Center.

‡

Department of Pharmacology.

Abstract

The use of matrix-assisted laser desorption ionization mass spectrometry (MALDI−MS) to acquire spectral profiles has become a common approach to detect proteomic biomarkers of disease. MALDI−MS signals may represent both intact proteins as well as proteolysis products. Liquid chromatography-tandem mass spectrometry (LC−MS/MS) analysis can tentatively identify the corresponding proteins Here, we describe the application of a data analysis utility called FragMint, which combines MALDI−MS spectral data with LC−MS/MS based protein identifications to generate candidate protein fragments consistent with both types of data. This approach was used to identify protein fragments corresponding to spectral signals in MALDI−MS analyses of unfractionated human serum. The serum also was analyzed by one-dimensional SDS-PAGE and bands corresponding to the MALDI−MS signal masses were excised and subjected to in-gel digestion and LC−MS/MS analysis. Database searches mapped all of the identified peptides to abundant blood proteins larger than the observed MALDI−MS signals. FragMint identified fragments of these proteins that contained the MS/MS identified sequences and were consistent with the observed MALDI−MS signals. This approach should be generally applicable to identify protein species corresponding to MALDI−MS signals.

Keywords: serum • MALDI • LC−MS/MS • bioinformatics • proteolysis

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