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PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

BACKGROUND: Construction of artificial polyepitope antigens is one of the most promising strategies for developing more efficient and safer vaccines evoking T-cell immune responses. Epitope rearrangements and utilization of certain spacer sequences have been proven to greatly influence the immunogen...

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Autores principales: Antonets, Denis V, Bazhan, Sergei I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853014/
https://www.ncbi.nlm.nih.gov/pubmed/24107711
http://dx.doi.org/10.1186/1756-0500-6-407
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author Antonets, Denis V
Bazhan, Sergei I
author_facet Antonets, Denis V
Bazhan, Sergei I
author_sort Antonets, Denis V
collection PubMed
description BACKGROUND: Construction of artificial polyepitope antigens is one of the most promising strategies for developing more efficient and safer vaccines evoking T-cell immune responses. Epitope rearrangements and utilization of certain spacer sequences have been proven to greatly influence the immunogenicity of polyepitope constructs. However, despite numerous efforts towards constructing and evaluating artificial polyepitope immunogens as well as despite numerous computational methods elaborated to date for predicting T-cell epitopes, peptides binding to TAP and for antigen processing prediction, only a few computational tools were currently developed for rational design of polyepitope antigens. FINDINGS: Here we present a PolyCTLDesigner program that is intended for constructing polyepitope immunogens. Given a set of either known or predicted T-cell epitopes the program selects N-terminal flanking sequences for each epitope to optimize its binding to TAP (if necessary) and joins resulting oligopeptides into a polyepitope in a way providing efficient liberation of potential epitopes by proteasomal and/or immunoproteasomal processing. And it also tries to minimize the number of non-target junctional epitopes resulting from artificial juxtaposition of target epitopes within the polyepitope. For constructing polyepitopes, PolyCTLDesigner utilizes known amino acid patterns of TAP-binding and proteasomal/immunoproteasomal cleavage specificity together with genetic algorithm and graph theory approaches. The program was implemented using Python programming language and it can be used either interactively or through scripting, which allows users familiar with Python to create custom pipelines. CONCLUSIONS: The developed software realizes a rational approach to designing poly-CTL-epitope antigens and can be used to develop new candidate polyepitope vaccines. The current version of PolyCTLDesigner is integrated with our TEpredict program for predicting T-cell epitopes, and thus it can be used not only for constructing the polyepitope antigens based on preselected sets of T-cell epitopes, but also for predicting cytotoxic and helper T-cell epitopes within selected protein antigens. PolyCTLDesigner is freely available from the project’s web site: http://tepredict.sourceforge.net/PolyCTLDesigner.html.
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spelling pubmed-38530142013-12-16 PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens Antonets, Denis V Bazhan, Sergei I BMC Res Notes Technical Note BACKGROUND: Construction of artificial polyepitope antigens is one of the most promising strategies for developing more efficient and safer vaccines evoking T-cell immune responses. Epitope rearrangements and utilization of certain spacer sequences have been proven to greatly influence the immunogenicity of polyepitope constructs. However, despite numerous efforts towards constructing and evaluating artificial polyepitope immunogens as well as despite numerous computational methods elaborated to date for predicting T-cell epitopes, peptides binding to TAP and for antigen processing prediction, only a few computational tools were currently developed for rational design of polyepitope antigens. FINDINGS: Here we present a PolyCTLDesigner program that is intended for constructing polyepitope immunogens. Given a set of either known or predicted T-cell epitopes the program selects N-terminal flanking sequences for each epitope to optimize its binding to TAP (if necessary) and joins resulting oligopeptides into a polyepitope in a way providing efficient liberation of potential epitopes by proteasomal and/or immunoproteasomal processing. And it also tries to minimize the number of non-target junctional epitopes resulting from artificial juxtaposition of target epitopes within the polyepitope. For constructing polyepitopes, PolyCTLDesigner utilizes known amino acid patterns of TAP-binding and proteasomal/immunoproteasomal cleavage specificity together with genetic algorithm and graph theory approaches. The program was implemented using Python programming language and it can be used either interactively or through scripting, which allows users familiar with Python to create custom pipelines. CONCLUSIONS: The developed software realizes a rational approach to designing poly-CTL-epitope antigens and can be used to develop new candidate polyepitope vaccines. The current version of PolyCTLDesigner is integrated with our TEpredict program for predicting T-cell epitopes, and thus it can be used not only for constructing the polyepitope antigens based on preselected sets of T-cell epitopes, but also for predicting cytotoxic and helper T-cell epitopes within selected protein antigens. PolyCTLDesigner is freely available from the project’s web site: http://tepredict.sourceforge.net/PolyCTLDesigner.html. BioMed Central 2013-10-10 /pmc/articles/PMC3853014/ /pubmed/24107711 http://dx.doi.org/10.1186/1756-0500-6-407 Text en Copyright © 2013 Antonets and Bazhan; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Antonets, Denis V
Bazhan, Sergei I
PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title_full PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title_fullStr PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title_full_unstemmed PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title_short PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
title_sort polyctldesigner: a computational tool for constructing polyepitope t-cell antigens
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853014/
https://www.ncbi.nlm.nih.gov/pubmed/24107711
http://dx.doi.org/10.1186/1756-0500-6-407
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