Literature DB >> 21693021

Determinants of antigenicity and specificity in immune response for protein sequences.

Yulong Wang1, Wenjun Wu, Nicolas N Negre, Kevin P White, Cheng Li, Parantu K Shah.   

Abstract

BACKGROUND: Target specific antibodies are pivotal for the design of vaccines, immunodiagnostic tests, studies on proteomics for cancer biomarker discovery, identification of protein-DNA and other interactions, and small and large biochemical assays. Therefore, it is important to understand the properties of protein sequences that are important for antigenicity and to identify small peptide epitopes and large regions in the linear sequence of the proteins whose utilization result in specific antibodies.
RESULTS: Our analysis using protein properties suggested that sequence composition combined with evolutionary information and predicted secondary structure, as well as solvent accessibility is sufficient to predict successful peptide epitopes. The antigenicity and the specificity in immune response were also found to depend on the epitope length. We trained the B-Cell Epitope Oracle (BEOracle), a support vector machine (SVM) classifier, for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle achieved an F1-measure of 81.37% on a large validation set. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Finally, a second SVM classifier, the B-Cell Region Oracle (BROracle) was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.26-63.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database.
CONCLUSIONS: Together our results suggest that antigenicity is a local property of the protein sequences and that protein sequence properties of composition, secondary structure, solvent accessibility and evolutionary conservation are the determinants of antigenicity and specificity in immune response. Moreover, specificity in immune response could also be accurately predicted for large protein regions without the knowledge of the protein tertiary structure or the presence of discontinuous epitopes. The dataset prepared in this work and the classifier models are available for download at https://sites.google.com/site/oracleclassifiers/.

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Year:  2011        PMID: 21693021      PMCID: PMC3133554          DOI: 10.1186/1471-2105-12-251

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


  39 in total

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8.  Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide.

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1.  Determining Zebrafish Epitope Reactivity to Commercially Available Antibodies.

Authors:  Michael A Villarreal; Nicole M Biediger; Natalie A Bonner; Jennifer N Miller; Samantha K Zepeda; Benjamin J Ricard; Dana M García; Karen A Lewis
Journal:  Zebrafish       Date:  2017-03-20       Impact factor: 1.985

2.  In Silico Analysis of HA2/Mx Chimera Peptide for Developing an Adjuvanted Vaccine to Induce Immune Responses Against Influenza Viruses.

Authors:  Sina Soleimani; Omid Madadgar; Shahla Shahsavandi; Homayoon Mahravani; Mohsen Lotfi
Journal:  Adv Pharm Bull       Date:  2015-12-31

3.  B-Pred, a structure based B-cell epitopes prediction server.

Authors:  Luciano Giacò; Massimo Amicosante; Maurizio Fraziano; Pier Federico Gherardini; Gabriele Ausiello; Manuela Helmer-Citterich; Vittorio Colizzi; Andrea Cabibbo
Journal:  Adv Appl Bioinform Chem       Date:  2012-07-25

4.  EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.

Authors:  Yao Lian; Meng Ge; Xian-Ming Pan
Journal:  BMC Bioinformatics       Date:  2014-12-19       Impact factor: 3.169

5.  A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin.

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Journal:  BMC Bioinformatics       Date:  2016-04-08       Impact factor: 3.169

6.  SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity.

Authors:  Bo Yao; Lin Zhang; Shide Liang; Chi Zhang
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7.  Virtual interactomics of proteins from biochemical standpoint.

Authors:  Jaroslav Kubrycht; Karel Sigler; Pavel Souček
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8.  Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull.

Authors:  Wei Zheng; Jishou Ruan; Gang Hu; Kui Wang; Michelle Hanlon; Jianzhao Gao
Journal:  PLoS One       Date:  2015-08-05       Impact factor: 3.240

9.  Prediction of B-Cell Epitopes in Listeriolysin O, a Cholesterol Dependent Cytolysin Secreted by Listeria monocytogenes.

Authors:  Morris S Jones; J Mark Carter
Journal:  Adv Bioinformatics       Date:  2014-01-02

10.  Production and Purification of Mycolyl Transferase B of Mycobacterium tuberculosis.

Authors:  Haniyeh Aghababa; Ashraf Mohabati Mobarez; Mehrdad Behmanesh; Nima Khoramabadi; Mandana Mobarhan
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