Graph-based optimization of epitope coverage for vaccine antigen design

Theiler, James Patrick; Korber, Bette Tina Marie

doi:10.1002/sim.7203

Title: Graph-based optimization of epitope coverage for vaccine antigen design

Journal Article · Sun Jan 29 00:00:00 EST 2017 · Statistics in Medicine

DOI:https://doi.org/10.1002/sim.7203· OSTI ID:1343714

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Consortium, Los Alamos, NM (United States)

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. Lastly, 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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE; Bill and Melinda Gates Foundation

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1343714

Report Number(s):: LA-UR-16-26417

Journal Information:: Statistics in Medicine, Vol. 37, Issue 2; ISSN 0277-6715

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 12 works

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60 APPLIED LIFE SCIENCES
biological science
vaccine
epitope
antigen
algorithm
directed acyclic graph
de Bruijn graph

Title: Graph-based optimization of epitope coverage for vaccine antigen design

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References (45)

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