Improving mass and liquid chromatography based identification of proteins using bayesian scoring.
We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.
Chen
Sharon S
SS
University of Washington, Department of Bioengineering, Seattle, Washington 98105, USA.
Deutsch
Eric W
EW
Yi
Eugene C
EC
Li
Xiao-jun
XJ
Goodlett
David R
DR
Aebersold
Ruedi
R
D000465:Algorithms; D001499:Bayes Theorem; D002851:Chromatography, High Pressure Liquid; D002853:Chromatography, Liquid / Q000379:methods*; D030562:Databases, Protein; D005656:Fungal Proteins / Q000737:chemistry; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D021241:Spectrometry, Mass, Electrospray Ionization; D015003:Yeasts / Q000378:metabolism
D005656:Fungal Proteins; D010455:Peptides; D011506:Proteins