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Prediction of high-responding peptides for targeted protein assays by mass spectrometry.
|Publication Type||Journal Article|
|Authors||Fusaro, VA, Mani D. R., Mesirov J. P., and Carr SA|
|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|
|Date Published (YYYY/MM/DD)||2009/02/01|