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Membrane Proteomics | 3716
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Membrane proteomics

2nd International Conference on Proteomics & Bioinformatics

Lifeng Peng, Eugene A. Kapp, Dany McLauchlan and T. William Jordan

ScientificTracks Abstracts: J Proteomics Bioinform

DOI: 10.4172/0974-276X.S1.060

Abstract
Although there are now multiple methods for analysis of membrane proteomes there is relatively little systematic characterization of proteomic workflows for membrane proteins. Here we describe the preparation and characterization of mouse liver microsomal membrane proteins using subcellular fraction and carbonate wash followed by SDS PAGE LC MS/ MS. The numbers of transmembrane domains and lipid anchors, GRAVY scores, protein abundance and GO term enrichment of the identified proteins were assessed. This strategy achieved great coverage of membrane proteins with 47.1% of the identified proteins being transmembrane proteins and 19.4% of the proteins showing positive GRAVY scores. GO term enrichment analysis showed that biological processes involving transport, lipid metabolism, cell communication, cell adhesion and cellular component organization were significantly enriched. Comparison of the present data with previously published reports on mouse liver proteomes indicated that the present strategy enabled new identification of some low-abundance membrane proteins.
Biography
Lifeng Peng received her PhD in metabolic engineering from Kyushu Institute of Technology, Japan in 2004. She was then a postdoctoral fellow in cell and molecular biology and proteomics at School of Biological Sciences, Victoria University of Wellington, New Zealand and became a lecturer in 2009. Her research interests are in the following areas: (1) bioinformatics-assisted mass spectrometry strategies for comprehensive signatures of membrane proteome and its post-translational modifications associated with biological status, genetic and environmental stimulus, disease and drug treatment; (2) quantitative proteomics for biomarker discovery; (3) metabolomics and metabolic flux analysis based on 13C-label and metabolite measurements.
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