Computational prediction of proteotypic peptides for quantitative proteomics

Parag Mallick, Markus Schirle, Sharon S. Chen, Mark R. Flory, Hookeun Lee, Daniel Martin, Jeffrey Ranish, Brian Raught, Robert Schmitt, Thilo Werner, Bernhard Kuster, Ruedi Aebersold

Research output: Contribution to journalArticlepeer-review

590 Scopus citations


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).

Original languageEnglish
Pages (from-to)125-131
Number of pages7
JournalNature Biotechnology
Issue number1
StatePublished - 5 Jan 2007
Externally publishedYes


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