Computational prediction of proteotypic peptides for quantitative proteomics.

Abstract:

: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).

journal_name

Nat Biotechnol

journal_title

Nature biotechnology

authors

Mallick P,Schirle M,Chen SS,Flory MR,Lee H,Martin D,Ranish J,Raught B,Schmitt R,Werner T,Kuster B,Aebersold R

doi

10.1038/nbt1275

subject

Has Abstract

pub_date

2007-01-01 00:00:00

pages

125-31

issue

1

eissn

1087-0156

issn

1546-1696

pii

nbt1275

journal_volume

25

pub_type

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