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Characterizing complex peptide mixtures using a multi-dimensional liquid chromatography-mass spectrometry system: Saccharomyces cerevisiae as a model system.

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  • 1Laboratory of Neurotoxicology, National Institute of Mental Health, NIH, Bethesda, MD 20892-1262, USA. maynardd@mail.nih.gov

Abstract

A rugged, reproducible, multi-dimensional LC-MS system was developed to identify and characterize proteins involved in protein-protein interactions and/or protein complexes. Our objective was to optimize chromatographic parameters for complex protein mixture analyses using automated peptide sequence recognition as an analytical end-point. The chromatographic system uses orthogonal separation mechanisms by employing strong cation exchange (SCX) in the first dimension and reversed phase (RP) in the second dimension. The system is fully automated and sufficiently robust to handle direct injections of protein digests. This system incorporates a streamlined post analysis results comparison, called DBParser, which permitted comprehensive evaluation of sample loading and chromatographic conditions to optimize the performance and reproducibility. Peptides obtained from trypsin digestion of a yeast soluble extract provided an open-ended model system containing a wide variety and dynamic range of components. Conditions are described that resulted in an average (n = 4) of 1489 unique peptide identifications, corresponding to 459 non-redundant protein sequence database records (SDRs) in the 20 microg soluble fraction digest.

PMID:
15358309
[PubMed - indexed for MEDLINE]
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