Prediction of High-Responding Peptides for Targeted Protein Assay by Mass Spectrometry

Broad Institute of Massachusetts Institute of Technology and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.
Nature Biotechnology (Impact Factor: 41.51). 02/2009; 27(2):190-8. DOI: 10.1038/nbt.1524
Source: PubMed


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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    • "cancer/software/genee pattern/modules/ESPPredictor. html) [17], Peptidd eSieve ( [18], PepFly (http://www. "
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    • "Various software packages have been developed for the purpose of selecting peptides for MRM experiments. In our previous paper [4], we have reviewed a few of these, including ESP predictor [15], Peptide Sieve [16], and PepFly [17] [18], PeptideAtlas [8] [19] PABST (Peptide Atlas Best SRM Transition Tool) [7, 13][8] [20], Automated and Targeted Analysis with Quantitative SRM --ATAQS [21], MRMaid,[22], GPMDB MRM Worksheet [23], SRM/MRMAtlas [24] [25], TIQAM [26] [27] (used ATAQS workflow), Skyline [28] [29], and TPP-MaRiMba [30] [31]. "
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    • "Our results with three peptide classes showed that eight out of the top 15 ranked features to discriminate these three classes of peptides are based on peptide sequence as well as the detected peptide's neighboring amino acid sequence regions (Table 2). This result reinforces the concept that the abundance of peptides (not proteins) in a given enzymatically digested sample is influenced not only by their parent protein's abundance, but also by flanking amino acid sequences that [9] b 9 features from Peptide Detectability [8] as shown inTable S3 Citation: Jung S, Danziger SA, Panchaud A, von Haller P, Aitchison JD, et al. (2015) Systematic Analysis of Yeast Proteome Reveals Peptide Detectability Factors for Mass Spectrometry. J Proteomics Bioinform 8: 231-239. "
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