TY - JOUR
T1 - Computational prediction of proteotypic peptides for quantitative proteomics
AU - Mallick, Parag
AU - Schirle, Markus
AU - Chen, Sharon S.
AU - Flory, Mark R.
AU - Lee, Hookeun
AU - Martin, Daniel
AU - Ranish, Jeffrey
AU - Raught, Brian
AU - Schmitt, Robert
AU - Werner, Thilo
AU - Kuster, Bernhard
AU - Aebersold, Ruedi
N1 - Funding Information:
The authors are grateful to Julien Gagneur for fruitful discussions and the Cellzome biochemistry, mass spectrometry and informatics teams for generating and managing data. The work was supported in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under contract N01-HV-28179.
PY - 2007/1/5
Y1 - 2007/1/5
N2 - Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).
AB - Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).
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U2 - 10.1038/nbt1275
DO - 10.1038/nbt1275
M3 - Article
C2 - 17195840
AN - SCOPUS:33846133955
SN - 1087-0156
VL - 25
SP - 125
EP - 131
JO - Nature biotechnology
JF - Nature biotechnology
IS - 1
ER -