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

@article{citeulike:3969969, 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.}, author = {Fusaro, Vincent A. and Mani, D. R. and Mesirov, Jill P. and Carr, Steven A.}, citeulike-article-id = {3969969}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nbt.1524}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nbt.1524}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19169245}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19169245}, day = {25}, doi = {10.1038/nbt.1524}, issn = {1546-1696}, journal = {Nat Biotech}, keywords = {biomarkers, cancer, prostate}, month = {February}, number = {2}, pages = {190--198}, posted-at = {2009-06-29 18:00:29}, priority = {2}, publisher = {Nature Publishing Group}, title = {Prediction of high-responding peptides for targeted protein assays by mass spectrometry}, url = {http://dx.doi.org/10.1038/nbt.1524}, volume = {27}, year = {2009} }

TY - JOUR ID - citeulike:3969969 L3 - citeulike-article-id:3969969 N2 - 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. IS - 2 JF - Nat Biotech SN - 1546-1696 EP - 198 TI - Prediction of high-responding peptides for targeted protein assays by mass spectrometry PB - Nature Publishing Group VL - 27 SP - 190 KW - biomarkers KW - cancer KW - prostate AU - Fusaro, Vincent AU - Mani, DR AU - Mesirov, Jill AU - Carr, Steven PY - 2009/02/25/ UR - http://dx.doi.org/10.1038/nbt.1524 ER -