Computational prediction of proteotypic peptides for quantitative proteomics.
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).
Mallick
Parag
P
Institute for Systems Biology, 1441 N. 34th Street, Seattle, Washington 98103, USA.
Schirle
Markus
M
Chen
Sharon S
SS
Flory
Mark R
MR
Lee
Hookeun
H
Martin
Daniel
D
Ranish
Jeffrey
J
Raught
Brian
B
Schmitt
Robert
R
Werner
Thilo
T
Kuster
Bernhard
B
Aebersold
Ruedi
R
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D010455:Peptides / Q000032:analysis / Q000737:chemistry*; D020543:Proteome / Q000032:analysis / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
D010455:Peptides; D020543:Proteome