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      Integrated computational prediction and experimental validation identifies promiscuous T cell epitopes in the proteome of Mycobacterium bovis

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          Abstract

          The discovery of novel antigens is an essential requirement in devising new diagnostics or vaccines for use in control programmes against human tuberculosis (TB) and bovine tuberculosis (bTB). Identification of potential epitopes recognised by CD4 + T cells requires prediction of peptide binding to MHC class-II, an obligatory prerequisite for T cell recognition. To comprehensively prioritise potential MHC-II-binding epitopes from Mycobacterium bovis, the agent of bTB and zoonotic TB in humans, we integrated three binding prediction methods with the M. bovisproteome using a subset of human HLA alleles to approximate the binding of epitope-containing peptides to the bovine MHC class II molecule BoLA-DRB3. Two parallel strategies were then applied to filter the resulting set of binders: identification of the top-scoring binders or clusters of binders. Our approach was tested experimentally by assessing the capacity of predicted promiscuous peptides to drive interferon-γ secretion from T cells of M. bovis infected cattle. Thus, 376 20-mer peptides, were synthesised (270 predicted epitopes, 94 random peptides with low predictive scores and 12 positive controls of known epitopes). The results of this validation demonstrated significant enrichment (>24 %) of promiscuously recognised peptides predicted in our selection strategies, compared with randomly selected peptides with low prediction scores. Our strategy offers a general approach to the identification of promiscuous epitopes tailored to target populations where there is limited knowledge of MHC allelic diversity.

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          Most cited references55

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          A generic method for assignment of reliability scores applied to solvent accessibility predictions

          Background Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score. Results An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output. Conclusion The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively. This tendency is true for any selected subset.
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            Immune epitope database analysis resource

            The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.
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              HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity.

              Prediction of HLA binding affinity is widely used to identify candidate T cell epitopes, and an affinity of 500 nM is routinely used as a threshold for peptide selection. However, the fraction (percentage) of peptides predicted to bind with affinities of 500 nM varies by allele. For example, of a large collection of ~30,000 dengue virus-derived peptides only 0.3% were predicted to bind HLA A*0101, whereas nearly 5% were predicted for A*0201. This striking difference could not be ascribed to variation in accuracy of the algorithms used, as predicted values closely correlated with affinity measured in vitro with purified HLA molecules. These data raised the question whether different alleles would also vary in terms of epitope repertoire size, defined as the number of associated epitopes or, alternatively, whether alleles vary drastically in terms of the affinity threshold associated with immunogenicity. To address this issue, strains of HLA transgenic mice with wide (A*0201), intermediate (B*0702), or narrow (A*0101) repertoires were immunized with peptides of varying binding affinity and relative percentile ranking. The results show that absolute binding capacity is a better predictor of immunogenicity, and analysis of epitopes from the Immune Epitope Database revealed that predictive efficacy is increased using allele-specific affinity thresholds. Finally, we investigated the genetic and structural basis of the phenomenon. Although no stringent correlate was defined, on average HLA B alleles are associated with significantly narrower repertoires than are HLA A alleles.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                MGen
                Microbial Genomics
                Microbiology Society
                2057-5858
                August 2016
                25 August 2016
                : 2
                : 8
                : e000071
                Affiliations
                [ 1]School of Veterinary Medicine, University College Dublin , Dublin D4, Ireland
                [ 2]Department of Bacteriology, Animal and Plant Health Agency , New Haw, Surrey KT15 3NB, UK
                [ 3]School of Agriculture and Food Science, University College Dublin , Dublin 4, Ireland
                [ 4]School of Medicine, University College Dublin , Dublin D4, Ireland
                [ 5]Conway Institute of Biomolecular and Biomedical Science, University College Dublin , Dublin D4, Ireland
                [ 6]School of Biomolecular and Biomedical Science, University College Dublin , Dublin 4, Ireland
                Author notes
                Correspondence Stephen V. Gordon ( stephen.gordon@ 123456ucd.ie )
                [†]

                Division of Experimental Medicine, Imperial College London, UK.

                [‡]

                Faculty of Medicine and Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland.

                All supporting data, code and protocols have been provided within the article or through supplementary data files.

                Article
                mgen000071
                10.1099/mgen.0.000071
                5320590
                28348866
                7094c909-3c1e-436f-819c-d800a47f634f
                © 2016 The Authors

                This is an open access article under the terms of the Creative Commons Attribution 4.04.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 April 2016
                : 26 May 2016
                Funding
                Funded by: Department of Agriculture, Food and the Marine
                Award ID: ERA_NET 11/RD/EMIDA/1
                Funded by: Irish Research Council
                Award ID: GOIPD/2015/475
                Funded by: Department for Environment, Food and Rural Affairs
                Award ID: SE3270
                Funded by: Wellcome Trust
                Award ID: 097429/Z/11/Z
                Categories
                Research Paper
                Microbe-Niche Interactions: Pathogenesis
                Custom metadata
                free

                mycobacterium bovis,tuberculosis,epitope,mhc,bovine
                mycobacterium bovis, tuberculosis, epitope, mhc, bovine

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