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Membrane proteomics, the large-scale analysis of membrane proteins, is often constrained by the difficulties of achieving fully resolvable separation and resistance to proteolysis, both of which could lead to low recovery and low identification rates of membrane proteins. Here, we introduce a novel integrated approach, GELFrEE Optimized FASP Technology (GOFAST) for large-scale and comprehensive membrane proteins analysis. Using an array of sample preparation techniques including gel-eluted liquid fraction entrapment electrophoresis (GELFrEE), filter-aided sample preparation (FASP), and microwave-assisted on-filter enzymatic digestion, we identified 2 090 proteins from the membrane fraction of a leukemia cell line (K562). Of these, 37% are annotated as membrane proteins according to gene ontology analysis, resulting in the largest membrane proteome of leukemia cells reported to date. Our approach combines the advantages of GELFrEE high-loading capacity, gel-free separation, efficient depletion of detergents, and microwave-assisted on-filter digestion, minimizing sample losses and maximizing MS-detectable sequence coverage of individual proteins. In addition, this approach also shows great potential for the identification of alternative splicing products.

Background: Proteogenomic mapping is an approach that uses mass spectrometry data from proteins to directly map protein-coding genes and could aid in locating translational regions in the human genome. In concert with the ENcyclopedia of DNA Elements (ENCODE) project, we applied proteogenomic mapping to produce proteogenomic tracks for the UCSC Genome Browser, to explore which putative translational regions may be missing from the human genome. Results: We generated ~1 million high-resolution tandem mass (MS/MS) spectra for Tier 1 ENCODE cell lines K562 and GM12878 and mapped them against the UCSC hg19 human genome, and the GENCODE V7 annotated protein and transcript sets. We then compared the results from the three searches to identify the best-matching peptide for each MS/MS spectrum, thereby increasing the confidence of the putative new protein-coding regions found via the whole genome search. At a 1% false discovery rate, we identified 26,472, 24,406, and 13,128 peptides from the protein, transcript, and whole genome searches, respectively; of these, 481 were found solely via the whole genome search. The proteogenomic mapping data are available on the UCSC Genome Browser at http://genome. ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeUncBsuProt. Conclusions: The whole genome search revealed that ~4% of the uniquely mapping identified peptides were located outside GENCODE V7 annotated exons. The comparison of the results from the disparate searches also identified 15% more spectra than would have been found solely from a protein database search. Therefore, whole genome proteogenomic mapping is a complementary method for genome annotation when performed in conjunction with other searches. [ABSTRACT FROM AUTHOR]

Membrane proteomics, the large-scale analysis of membrane proteins, is often constrained by the difficulties of achieving fully resolvable separation and resistance to proteolysis, both of which could lead to low recovery and low identification rates of membrane proteins. Here, we introduce a novel integrated approach, GELFrEE Optimized FASP Technology (GOFAST) for large-scale and comprehensive membrane proteins analysis. Using an array of sample preparation techniques including gel-eluted liquid fraction entrapment electrophoresis (GELFrEE), filter-aided sample preparation (FASP), and microwave-assisted on-filter enzymatic digestion, we identified 2?090 proteins from the membrane fraction of a leukemia cell line (K562). Of these, 37% are annotated as membrane proteins according to gene ontology analysis, resulting in the largest membrane proteome of leukemia cells reported to date. Our approach combines the advantages of GELFrEE high-loading capacity, gel-free separation, efficient depletion of detergents, and microwave-assisted on-filter digestion, minimizing sample losses and maximizing MS-detectable sequence coverage of individual proteins. In addition, this approach also shows great potential for the identification of alternative splicing products. [ABSTRACT FROM AUTHOR]