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

Nature Biotechnology volume 27, pages 190–198 (2009)Cite this article

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.

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Figure 1: ESP application and model development overview.
Figure 2: ESP predictor validation and method comparison.
Figure 3: ESP predictions translate into experimentally validated MRM peptides.
Figure 4: Analysis of important physicochemical properties in predicting high-responding peptides.

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Acknowledgements

We thank the National Cancer Institute (NCI) Clinical Proteomic Technology Assessment in Cancer Program (NCI-CPTAC, http://proteomics.cancer.gov/programs/CPTAC/) for providing samples of yeast lysate and raw MS data generated by the CPTAC centers. We thank Rushdy Ahmad, Kathy Do, Amy Ham, Emily Rudomin, and Shao-En Ong for MS data generation, and Hasmik Keshishian and Terri Addona for generating the lists of validated MRM peptides. We also thank Shao-En Ong, Jacob Jaffe, Karl Clauser, Eric Kuhn, Pablo Tamayo, and Nick Patterson for helpful discussions. We would like to thank the reviewers for their insightful comments. This work was supported in part by grants to S.A.C. from the National Institutes of Health Grants 1U24 CA126476 as part of the NCI's Clinical Proteomic Technologies Assessment in Cancer Program, the National Heart, Lung, and Blood Institute, U01-HL081341 and The Women's Cancer Research Fund; to J.P.M. from the National Science Foundation and NIGMS the National Institutes of Health (NIGMS and NCI); to D.R.M. from the National Institutes of Health grant R01 CA126219, as part of NCI's Clinical Proteomic Technologies for Cancer Program.

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Authors and Affiliations

  1. Broad Institute of Massachusetts Institute of Technology and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Vincent A Fusaro, D R Mani, Jill P Mesirov & Steven A Carr

  2. Bioinformatics Program, Boston University, 24 Cummington Street, Boston, 02215, Massachusetts, USA

    Vincent A Fusaro

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  1. Vincent A Fusaro
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Correspondence to Jill P Mesirov or Steven A Carr.

Supplementary information

Supplementary Figures 1–7, Methods, Data

(PDF 483 kb)

Supplementary Table 1

Ranked list of 550 physicochemical properties (XLS 77 kb)

Supplementary Table 2

Validated MRM peptides (XLS 49 kb)

Supplementary Source Code (ZIP 84009 kb)

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Fusaro, V., Mani, D., Mesirov, J. et al. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol 27, 190–198 (2009). https://doi.org/10.1038/nbt.1524

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