title: A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://omics.pnl.gov/software/STEPP.php.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov
authors.forename: Bobbie-Jo M
authors.initials: BJ
authors.lastname: Webb-Robertson
authors.forename: William R
authors.initials: WR
authors.lastname: Cannon
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Oehmen
authors.forename: Anuj R
authors.initials: AR
authors.lastname: Shah
authors.forename: Vidhya
authors.initials: V
authors.lastname: Gurumoorthi
authors.forename: Mary S
authors.initials: MS
authors.lastname: Lipton
authors.forename: Katrina M
authors.initials: KM
authors.lastname: Waters
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry*; D003198:Computer Simulation; D008956:Models, Chemical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D010455:Peptides / Q000737:chemistry*; D020543:Proteome / Q000737:chemistry*; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D001426:Bacterial Proteins; D010455:Peptides; D020543:Proteome