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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    • "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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    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.
    Journal of proteomics 07/2015; 129. DOI:10.1016/j.jprot.2015.07.025 · 3.89 Impact Factor
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    • "For example , how many times a peptide sequence was detected within a given set of experiments can help to estimate the detectability of this peptide by LC-MS (due to digestion efficacy, good ionization , LC or MS behavior, etc.). Theoretical scores based on various parameters (e.g., amino acid composition and hydrophobicity ) and algorithms can give theoretical estimations of the LC-MS behavior and detectability of the peptides (Mallick et al., 2007; Fusaro et al., 2009; Eyers et al., 2011; Qeli et al., 2014). All this information can be used to further rationalize the peptide "
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    ABSTRACT: The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.
    Frontiers in Genetics 09/2014; 5. DOI:10.3389/fgene.2014.00305
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    • "To generate protein-specific MRM assays, in silico trypsin digestion of proteins of interest was performed on 29 cartilage ECM proteins to identify proteotypic peptides for each protein (Fusaro et al., 2009). "
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    ABSTRACT: The articular cartilage of synovial joints ensures friction-free mobility and attenuates mechanical impact on the joint during movement. These functions are mediated by the complex network of extracellular molecules characteristic for articular cartilage. Zonal differences in the extracellular matrix (ECM)1 are well recognized. However, the knowledge about the precise molecular composition in the different zones remains limited. In the present study, we investigated the distribution of ECM molecules along the surface-to-bone axis, using quantitative non-targeted as well as targeted proteomics. In a discovery approach, iTRAQ mass spectrometry was used to identify all extractable ECM proteins in the different layers of a human lateral tibial plateau full thickness cartilage sample. A targeted MRM mass spectrometry approach was then applied to verify these findings and to extend the analysis to four medial tibial plateau samples. In the lateral tibial plateau sample the unique distribution patterns of 70 ECM proteins were identified, revealing groups of proteins with a preferential distribution to the superficial, intermediate or deep regions of articular cartilage. The detailed analysis of selected 29 proteins confirmed these findings and revealed similar distribution patterns in the four medial tibial plateau samples. The results of this study allow for the first time an overview of the zonal distribution of a broad range of cartilage ECM proteins and open up for further investigations of the functional roles of matrix proteins in the different zones of articular cartilage in health and disease.
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