skip to main content


Title: 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 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 epigraphmore » tool suite and explain their content and applications.« less
ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Consortium, Los Alamos, NM (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0277-6715
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Statistics in Medicine
Additional Journal Information:
Journal Volume: 37; Journal Issue: 2; Journal ID: ISSN 0277-6715
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
National Institutes of Health (NIH); USDOE; Bill and Melinda Gates Foundation
Country of Publication:
United States
60 APPLIED LIFE SCIENCES; biological science; vaccine; epitope; antigen; algorithm; directed acyclic graph; de Bruijn graph
OSTI Identifier: