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
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) , Peptidd eSieve (https://www.systemsbiology.org/peptidesieve) , PepFly (http://www. "
[Show abstract] [Hide abstract] ABSTRACT: Selecting the most appropriate surrogate peptides to represent a target protein is a major component of experimental design in Multiple Reaction Monitoring (MRM). Our software PeptidePicker with its v-score remains distinctive in its approach of integrating information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM that is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our "best knowledge" for selecting candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it has previously been observed. Here we present an updated approach where we have already compiled a list of all possible surrogate peptides of the human proteome. Using our stringent selection criteria, the list includes 165k suitable MRM peptides covering 17k proteins of the human reviewed proteins in UniProtKB. Compared to average of 2-4min per protein for retrieving and integrating the information, the precompiled list includes all peptides available instantly. This allows a more cohesive and faster design of a multiplexed MRM experiment and provides insights into evidence for a protein's existence. We will keep this list up-to-date as proteomics data repositories continue to grow.
- "Various software packages have been developed for the purpose of selecting peptides for MRM experiments. In our previous paper , we have reviewed a few of these, including ESP predictor , Peptide Sieve , and PepFly  , PeptideAtlas   PABST (Peptide Atlas Best SRM Transition Tool) [7, 13] , Automated and Targeted Analysis with Quantitative SRM --ATAQS , MRMaid,, GPMDB MRM Worksheet , SRM/MRMAtlas  , TIQAM   (used ATAQS workflow), Skyline  , and TPP-MaRiMba  . "
- "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  b 9 features from Peptide Detectability  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. "