MOTIVATION: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). RESULTS: We present a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of 0.8 with a SD of <0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. AVAILABILITY: http://omics.pnl.gov/software/STEPP.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). RESULTS: We present a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of 0.8 with a SD of <0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. AVAILABILITY: http://omics.pnl.gov/software/STEPP.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Authors: Christin Christin; Huub C J Hoefsloot; Age K Smilde; B Hoekman; Frank Suits; Rainer Bischoff; Peter Horvatovich Journal: Mol Cell Proteomics Date: 2012-10-31 Impact factor: 5.911
Authors: Andrew N Hoofnagle; Jeffrey R Whiteaker; Steven A Carr; Eric Kuhn; Tao Liu; Sam A Massoni; Stefani N Thomas; R Reid Townsend; Lisa J Zimmerman; Emily Boja; Jing Chen; Daniel L Crimmins; Sherri R Davies; Yuqian Gao; Tara R Hiltke; Karen A Ketchum; Christopher R Kinsinger; Mehdi Mesri; Matthew R Meyer; Wei-Jun Qian; Regine M Schoenherr; Mitchell G Scott; Tujin Shi; Gordon R Whiteley; John A Wrobel; Chaochao Wu; Brad L Ackermann; Ruedi Aebersold; David R Barnidge; David M Bunk; Nigel Clarke; Jordan B Fishman; Russ P Grant; Ulrike Kusebauch; Mark M Kushnir; Mark S Lowenthal; Robert L Moritz; Hendrik Neubert; Scott D Patterson; Alan L Rockwood; John Rogers; Ravinder J Singh; Jennifer E Van Eyk; Steven H Wong; Shucha Zhang; Daniel W Chan; Xian Chen; Matthew J Ellis; Daniel C Liebler; Karin D Rodland; Henry Rodriguez; Richard D Smith; Zhen Zhang; Hui Zhang; Amanda G Paulovich Journal: Clin Chem Date: 2016-01 Impact factor: 8.327
Authors: Nedim Mujezinovic; Georg Schneider; Michael Wildpaner; Karl Mechtler; Frank Eisenhaber Journal: BMC Genomics Date: 2010-02-10 Impact factor: 3.969