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Random glycopeptide bead libraries for seromic biomarker discovery.

ABSTRACT: Identification of disease-specific biomarkers is important to address early diagnosis and management of disease. Aberrant post-translational modifications (PTM) of proteins such as O-glycosylations (O-PTMs) are emerging as triggers of autoantibodies that can serve as sensitive biomarkers. Here we have developed a random glycopeptide bead library screening platform for detection of autoantibodies and other binding proteins. Libraries were build on biocompatible PEGA beads including a safety-catch C-terminal amide linker (SCAL) that allowed mild cleavage conditions (I(2)/NaBH(4) and TFA) for release of glycopeptides and sequence determination by ESI-Orbitrap-MS(n). As proof-of-principle, tumor -specific glycopeptide reporter epitopes were built-in into the libraries and were detected by tumor-specific monoclonal antibodies and autoantibodies from cancer patients. Sequenced and identified glycopeptides were resynthesized at the preparative scale by automated parallel peptide synthesis and printed on microarrays for validation and broader analysis with larger sets of sera. We further showed that chemical synthesis of the monosaccharide O-glycopeptide library (Tn-glycoform) could be diversified to other tumor glycoforms by on-bead enzymatic glycosylation reactions with recombinant glycosyltransferases. Hence, we have developed a high-throughput flexible platform for rapid discovery of O-glycopeptide biomarkers and the method has applicability in other types of assays such as lectin/antibody/enzyme specificity studies as well as investigation of other PTMs.

SUBMITTER: Kracun SK

PROVIDER: S-EPMC3001164 | biostudies-other | 2010 Dec

REPOSITORIES: biostudies-other

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Random glycopeptide bead libraries for seromic biomarker discovery.

Kracun Stjepan K SK Cló Emiliano E Clausen Henrik H Levery Steven B SB Jensen Knud J KJ Blixt Ola O

Journal of proteome research 20101102 12

Identification of disease-specific biomarkers is important to address early diagnosis and management of disease. Aberrant post-translational modifications (PTM) of proteins such as O-glycosylations (O-PTMs) are emerging as triggers of autoantibodies that can serve as sensitive biomarkers. Here we have developed a random glycopeptide bead library screening platform for detection of autoantibodies and other binding proteins. Libraries were build on biocompatible PEGA beads including a safety-catch ...[more]

PMID: 20886906

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Project description:BackgroundBiomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.MethodsIn this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.ResultsWe present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface ( https://joelarkman.shinyapps.io/PowerTools/ ) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.ConclusionsWe developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.

Dataset Information

Random glycopeptide bead libraries for seromic biomarker discovery.

Publications

Random glycopeptide bead libraries for seromic biomarker discovery.

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