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

Nat Biotechnol
Authors
Keywords
Abstract

Protein 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 Publication
2009
Journal
Nat Biotechnol
Volume
27
Issue
2
Pages
190-8
Date Published
2009 Feb
ISSN
1546-1696
URL
DOI
10.1038/nbt.1524
PubMed ID
19169245
PubMed Central ID
PMC2753399
Links
Grant list
R01 GM074024 / GM / NIGMS NIH HHS / United States
R01 CA126219 / CA / NCI NIH HHS / United States
1U24 CA126476 / CA / NCI NIH HHS / United States
U01 HL081341-03 / HL / NHLBI NIH HHS / United States
U01-HL081341 / HL / NHLBI NIH HHS / United States
U24 CA126476-03 / CA / NCI NIH HHS / United States
U01 HL081341 / HL / NHLBI NIH HHS / United States
R01 CA126219-02 / CA / NCI NIH HHS / United States
U24 CA126476 / CA / NCI NIH HHS / United States
R01 GM074024-03 / GM / NIGMS NIH HHS / United States