A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics.
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.
Webb-Robertson
Bobbie-Jo M
BJ
Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov
Cannon
William R
WR
Oehmen
Christopher S
CS
Shah
Anuj R
AR
Gurumoorthi
Vidhya
V
Lipton
Mary S
MS
Waters
Katrina M
KM
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
D001426:Bacterial Proteins; D010455:Peptides; D020543:Proteome