Pharmaceutical Sciences Faculty Publications
Computational Prediction of Proteotypic Peptides
Document Type
Article
Publication Date
2007
Journal Title
Expert Review of Proteomics
Volume
4
Issue
3
First Page
351
Last Page
354
DOI
10.1586/14789450.4.3.351
Abstract
Evaluation of: Mallick P, Schirle M, Chen SS et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25(1), 125–131 (2007).
Mass spectrometry, the driving analytical force behind proteomics, is primarily used to identify and quantify as many proteins in a complex biological mixture as possible. While there are many ways to prepare samples, one aspect that is common to a vast majority of bottom-up proteomic studies is the digestion of proteins into tryptic peptides prior to their analysis by mass spectrometry. As correctly highlighted by Mallick and colleagues, only a few peptides are repeatedly and consistently identified for any given protein within a complex mixture. While the existence of these proteotypic peptides (to borrow the authors’ terminology) is well known in the proteomics community, there has never been an empirical method to recognize which peptides may be proteotypic for a given protein. In this study, the investigators discovered over 16,000 proteotypic peptides from a collection of over 600,000 peptide identifications obtained from four different analytical platforms. The study examined a number of physicochemical parameters of these peptides to determine which properties were most relevant in defining a proteotypic peptide. These characteristic properties were then used to develop computational tools to predict proteotypic peptides for any given protein within an organism.
Keywords
Computational, prediction, proteotypic, peptides, mass spectrometry, proteomics
Recommended Citation
Blonder, Josip and Veenstra, Timothy D., "Computational Prediction of Proteotypic Peptides" (2007). Pharmaceutical Sciences Faculty Publications. 365.
https://digitalcommons.cedarville.edu/pharmaceutical_sciences_publications/365