Prediction of high-responding peptides for targeted protein assays by mass spectrometry.
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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
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Journal | Nat Biotechnol
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Volume | 27
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Issue | 2
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Pages | 190-8
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Date Published | 2009 Feb
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ISSN | 1546-1696
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URL | |
DOI | 10.1038/nbt.1524
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PubMed ID | 19169245
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PubMed Central ID | PMC2753399
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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
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