alexa Characterization of the Low Molecular Weight Human Serum Proteome
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Proteomics & Bioinformatics

Author(s): Tirumalai RS, Chan KC, Prieto DA, Issaq HJ, Conrads TP

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Serum potentially carries an archive of important histological information whose determination could serve to improve early disease detection. The analysis of serum, however, is analytically challenging due to the high dynamic concentration range of constituent protein/peptide species, necessitating extensive fractionation prior to mass spectrometric analyses. The low molecular weight (LMW) serum proteome is that protein/peptide fraction from which high molecular weight proteins, such as albumin, immunoglobulins, transferrin, and lipoproteins, have been removed. This LMW fraction is made up of several classes of physiologically important proteins such as cytokines, chemokines, peptide hormones, as well as proteolytic fragments of larger proteins. Centrifugal ultrafiltration of serum was used to remove the large constituent proteins resulting in the enrichment of the LMW proteins/peptides. Because albumin is known to bind and transport small molecules and peptides within the circulatory system, the centrifugal ultrafiltration was conducted under solvent conditions effecting the disruption of protein-protein interactions. The LMW serum proteome sample was digested with trypsin, fractionated by strong cation exchange chromatography, and analyzed by microcapillary reversed-phase liquid chromatography coupled on-line with electrospray ionization tandem mass spectrometry. Analysis of the tandem mass spectra resulted in the identification of over 340 human serum proteins; however, not a single peptide from serum albumin was observed. The large number of proteins identified demonstrates the efficacy of this method for the removal of large abundant proteins and the enrichment of the LMW serum proteome.

This article was published in Mol Cell Proteomics and referenced in Journal of Proteomics & Bioinformatics

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