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Prediction of high-responding peptides for targeted protein assays by mass spectrometry.

Publication TypeJournal Article
AuthorsFusaro, VA, Mani D. R., Mesirov J. P., and Carr SA
AbstractProtein biomarker discovery produces lengthy lists of candidates that must subsequently be verified in blood or other accessible biofluids. Use of targeted mass spectrometry (MS) to verify disease- or therapy-related changes in protein levels requires the selection of peptides that are quantifiable surrogates for proteins of interest. Peptides that produce the highest ion-current response (high-responding peptides) are likely to provide the best detection sensitivity. Identification of the most effective signature peptides, particularly in the absence of experimental data, remains a major resource constraint in developing targeted MS-based assays. Here we describe a computational method that uses protein physicochemical properties to select high-responding peptides and demonstrate its utility in identifying signature peptides in plasma, a complex proteome with a wide range of protein concentrations. Our method, which employs a Random Forest classifier, facilitates the development of targeted MS-based assays for biomarker verification or any application where protein levels need to be measured.
Year of Publication2009
JournalNature biotechnology
Volume27
Issue2
Pages190-8
Date Published (YYYY/MM/DD)2009/02/01
ISSN Number1087-0156
DOI10.1038/nbt.1524
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed/19169245?dopt=Abstract