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Graph‐based optimization of epitope coverage for vaccine antigen design

Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi‐antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in lengt...

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Detalles Bibliográficos
Autores principales: Theiler, James, Korber, Bette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Ltd 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763320/
https://www.ncbi.nlm.nih.gov/pubmed/28132437
http://dx.doi.org/10.1002/sim.7203
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author Theiler, James
Korber, Bette
author_facet Theiler, James
Korber, Bette
author_sort Theiler, James
collection PubMed
description Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi‐antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T‐cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins. The epigraph tool suite also enables rapid characterization of populations of diverse sequences from an immunological perspective. Fundamental distance measures are based on immunologically relevant shared potential epitope frequencies, rather than simple Hamming or phylogenetic distances. Here, we provide a mathematical description of the epigraph algorithm, include a comparison of different heuristics that can be used when graphs are not acyclic, and we describe an additional tool we have added to the web‐based epigraph tool suite that provides frequency summaries of all distinct potential epitopes in a population. We also show examples of the graphical output and summary tables that can be generated using the epigraph tool suite and explain their content and applications. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Statistics in Medicine published by John Wiley & Sons Ltd.
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spelling pubmed-57633202018-01-17 Graph‐based optimization of epitope coverage for vaccine antigen design Theiler, James Korber, Bette Stat Med Special Issue Papers Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi‐antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T‐cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins. The epigraph tool suite also enables rapid characterization of populations of diverse sequences from an immunological perspective. Fundamental distance measures are based on immunologically relevant shared potential epitope frequencies, rather than simple Hamming or phylogenetic distances. Here, we provide a mathematical description of the epigraph algorithm, include a comparison of different heuristics that can be used when graphs are not acyclic, and we describe an additional tool we have added to the web‐based epigraph tool suite that provides frequency summaries of all distinct potential epitopes in a population. We also show examples of the graphical output and summary tables that can be generated using the epigraph tool suite and explain their content and applications. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Statistics in Medicine published by John Wiley & Sons Ltd. John Wiley & Sons, Ltd 2017-01-29 2018-01-30 /pmc/articles/PMC5763320/ /pubmed/28132437 http://dx.doi.org/10.1002/sim.7203 Text en Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Special Issue Papers
Theiler, James
Korber, Bette
Graph‐based optimization of epitope coverage for vaccine antigen design
title Graph‐based optimization of epitope coverage for vaccine antigen design
title_full Graph‐based optimization of epitope coverage for vaccine antigen design
title_fullStr Graph‐based optimization of epitope coverage for vaccine antigen design
title_full_unstemmed Graph‐based optimization of epitope coverage for vaccine antigen design
title_short Graph‐based optimization of epitope coverage for vaccine antigen design
title_sort graph‐based optimization of epitope coverage for vaccine antigen design
topic Special Issue Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763320/
https://www.ncbi.nlm.nih.gov/pubmed/28132437
http://dx.doi.org/10.1002/sim.7203
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