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      Combination of In Silico Methods in the Search for Potential CD4 + and CD8 + T Cell Epitopes in the Proteome of Leishmania braziliensis

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          Abstract

          The leishmaniases are neglected tropical diseases widespread throughout the globe, which are caused by protozoans from the genus Leishmania and are transmitted by infected phlebotomine flies. The development of a safe and effective vaccine against these diseases has been seen as the best alternative to control and reduce the number of cases. To support vaccine development, this work has applied an in silico approach to search for high potential peptide epitopes able to bind to different major histocompatibility complex Class I and Class II (MHC I and MHC II) molecules from different human populations. First, the predicted proteome of Leishmania braziliensis was compared and analyzed by modern linear programs to find epitopes with the capacity to trigger an immune response. This approach resulted in thousands of epitopes derived from 8,000 proteins conserved among different Leishmania species. Epitopes from proteins similar to those found in host species were excluded, and epitopes from proteins conserved between different Leishmania species and belonging to surface proteins were preferentially selected. The resulting epitopes were then clustered, to avoid redundancies, resulting in a total of 230 individual epitopes for MHC I and 2,319 for MHC II. These were used for molecular modeling and docking with MHC structures retrieved from the Protein Data Bank. Molecular docking then ranked epitopes based on their predicted binding affinity to both MHC I and II. Peptides corresponding to the top 10 ranked epitopes were synthesized and evaluated in vitro for their capacity to stimulate peripheral blood mononuclear cells (PBMC) from post-treated cutaneous leishmaniasis patients, with PBMC from healthy donors used as control. From the 10 peptides tested, 50% showed to be immunogenic and capable to stimulate the proliferation of lymphocytes from recovered individuals.

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          ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules.

          We have recently completed a full re-architecturing of the ROSETTA molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy-to-use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as ROSETTA3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This chapter describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform. © 2011 Elsevier Inc. All rights reserved.
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            Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

            In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
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              HLA class I supertypes: a revised and updated classification

              Background Class I major histocompatibility complex (MHC) molecules bind, and present to T cells, short peptides derived from intracellular processing of proteins. The peptide repertoire of a specific molecule is to a large extent determined by the molecular structure accommodating so-called main anchor positions of the presented peptide. These receptors are extremely polymorphic, and much of the polymorphism influences the peptide-binding repertoire. However, despite this polymorphism, class I molecules can be clustered into sets of molecules that bind largely overlapping peptide repertoires. Almost a decade ago we introduced this concept of clustering human leukocyte antigen (HLA) alleles and defined nine different groups, denominated as supertypes, on the basis of their main anchor specificity. The utility of this original supertype classification, as well several other subsequent arrangements derived by others, has been demonstrated in a large number of epitope identification studies. Results Following our original approach, in the present report we provide an updated classification of HLA-A and -B class I alleles into supertypes. The present analysis incorporates the large amount of class I MHC binding data and sequence information that has become available in the last decade. As a result, over 80% of the 945 different HLA-A and -B alleles examined to date can be assigned to one of the original nine supertypes. A few alleles are expected to be associated with repertoires that overlap multiple supertypes. Interestingly, the current analysis did not identify any additional supertype specificities. Conclusion As a result of this updated analysis, HLA supertype associations have been defined for over 750 different HLA-A and -B alleles. This information is expected to facilitate epitope identification and vaccine design studies, as well as investigations into disease association and correlates of immunity. In addition, the approach utilized has been made more transparent, allowing others to utilize the classification approach going forward.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/116904
                URI : http://frontiersin.org/people/u/357419
                URI : http://frontiersin.org/people/u/357351
                URI : http://frontiersin.org/people/u/358025
                URI : http://frontiersin.org/people/u/357437
                URI : http://frontiersin.org/people/u/357375
                URI : http://frontiersin.org/people/u/357747
                URI : http://frontiersin.org/people/u/194859
                URI : http://frontiersin.org/people/u/139064
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                29 August 2016
                2016
                : 7
                : 327
                Affiliations
                [1] 1Department of Natural Sciences, Universidade de Pernambuco , Garanhuns, Pernambuco, Brazil
                [2] 2Department of Immunology, Fundação Oswaldo Cruz , Recife, Pernambuco, Brazil
                [3] 3Department of Pharmaceutical Sciences, Universidade Federal de Pernambuco , Recife, Pernambuco, Brazil
                [4] 4Department of Microbiology, Fundação Oswaldo Cruz , Recife, Pernambuco, Brazil
                Author notes

                Edited by: José Mordoh, Fundación Instituto Leloir, Argentina

                Reviewed by: Yvonne Paterson, University of Pennsylvania, USA; Vijay Panchanathan, Perdana University, Malaysia

                *Correspondence: Antônio Mauro Rezende, antonio.rezende@ 123456cpqam.fiocruz.br ; Valéria Rêgo Alves Pereira, valeria@ 123456cpqam.fiocruz.br

                Antônio Mauro Rezende and Valéria Rêgo Alves Pereira contributed equally to this work.

                Specialty section: This article was submitted to Immunotherapies and Vaccines, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2016.00327
                5002431
                27621732
                04452192-700b-416a-8ae5-d5e3e4d25961
                Copyright © 2016 e Silva, Ferreira, Hernandes, de Brito, de Oliveira, da Silva, de-Melo-Neto, Rezende and Pereira.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 June 2016
                : 16 August 2016
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 54, Pages: 14, Words: 8364
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico 10.13039/501100003593
                Award ID: 404259/2012
                Categories
                Immunology
                Original Research

                Immunology
                neglected tropical diseases,cutaneous leishmaniasis,leishmania braziliensis,vaccine development,cd4+ cd8+ t cell epitopes

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