Proteomic data mining using predicted peptide chromatographic retention times

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	Title:	Proteomic data mining using predicted peptide chromatographic retention times
	Author:	Brian Tripet, Megha Renuka Jayadev, Don Blow, Cao Nguyen, Robert S. Hodges, Krzysztof J. Cios
	Address:	Department of Biochemistry and Molecular Genetics, University of Colorado at Denver and Health Sciences Center, Aurora, CO 80045, USA. ' Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Denver, CO 80217, USA. ' Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Denver, CO 80217, USA. ' Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Denver, CO 80217, USA. ' Department of Biochemistry and Molecular Genetics, University of Colorado at Denver and Health Sciences Center, Aurora, CO 80045, USA. ' Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
	Journal:	International Journal of Bioinformatics Research and Applications 2007 - Vol. 3, No.4 pp. 431 - 445
	Abstract:	Correct identification of proteins from peptide fragments is important for proteomic analyses. Peptides are initially separated by Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC) before Mass Spectrometry (MS) identification. At the present time, peptide fragment retention (separation) time is not used as a useful scoring filter for identification of the peptide fragments and their parent proteins. In the present paper, we present a new web-based tool for the prediction of peptide fragment retention times and its use in compiling a database of ∼133,000 peptide fragments computationally obtained by digestion with trypsin of 4,265 E. coli – K12 proteins. The retention calculation is based on the described formulae and the fragments/protein identification was carried out using a simple search-scoring algorithm.
	Keywords:	reversed-phase liquid chromatography; mass spectrometry; high performance liquid chromatography; RP-HPLC; retention time prediction; tryptic digest; mass frequency; bioinformatics; proteomic data mining; peptide fragments; internet; protein identification.
	DOI:	10.1504/IJBRA.2007.015412
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