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Profiling human brain proteome by multi‐dimensional separations coupled with MS
Author(s) -
Park Young Mok,
Kim Jin Young,
Kwon KyungHoon,
Lee Sang Kwang,
Kim Young Hye,
Kim SeYoung,
Park Gun Wook,
Lee Jeong Hwa,
Lee Bonghee,
Yoo Jong Shin
Publication year - 2006
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200600098
Subject(s) - proteome , profiling (computer programming) , computational biology , proteomics , biology , computer science , bioinformatics , genetics , operating system , gene
In our initial attempt to analyze the human brain proteome, we applied multi‐dimensional protein separation and identification techniques using a combination of sample fractionation, 1‐D SDS‐PAGE, and MS analysis. The complexity of human brain proteome requires multiple fractionation strategies to extend the range and total number of proteins identified. According to the method of Klose ( Methods Mol. Biol. 1999, 112 , 67), proteins of the temporal lobe of human brain were fractionated into (i) cytoplasmic and nucleoplasmic, (ii) membrane and other structural, and (iii) DNA‐binding proteins. Each fraction was then separated by SDS‐PAGE, and the resulting gel line was cut into approximately 50 bands. After trypsin digestion, the resulting peptides from each band were analyzed by RP‐LC/ESI‐MS/MS using an LTQ spectrometer. The SEQUEST search program, which searched against the IPI database, was used for peptide sequence identification, and peptide sequences were validated by reversed sequence database search and filtered by the Protein Hit Score. Ultimately, 1533 proteins could be detected from the human brain. We classified the identified proteins according to their distribution on cellular components. Among these proteins, 24% were membrane proteins. Our results show that the multiple separation strategy is effective for high‐throughput characterization of proteins from complex proteomic mixtures.