Epigraph: A Vaccine Design Tool Applied to an HIV Therapeutic Vaccine and a Pan-Filovirus Vaccine.

James Theiler,Karina Yusim,Bette Korber,Hyejin Yoon,Louis J Picker,Klaus Fruh

doi:10.1038/srep33987

Abstract

Epigraph is an efficient graph-based algorithm for designing vaccine antigens to optimize potential T-cell epitope (PTE) coverage. Epigraph vaccine antigens are functionally similar to Mosaic vaccines, which have demonstrated effectiveness in preliminary HIV non-human primate studies. In contrast to the Mosaic algorithm, Epigraph is substantially faster, and in restricted cases, provides a mathematically optimal solution. Epigraph furthermore has new features that enable enhanced vaccine design flexibility. These features include the ability to exclude rare epitopes from a design, to optimize population coverage based on inexact epitope matches, and to apply the code to both aligned and unaligned input sequences. Epigraph was developed to provide practical design solutions for two outstanding vaccine problems. The first of these is a personalized approach to a therapeutic T-cell HIV vaccine that would provide antigens with an excellent match to an individual’s infecting strain, intended to contain or clear a chronic infection. The second is a pan-filovirus vaccine, with the potential to protect against all known viruses in the Filoviradae family, including ebolaviruses. A web-based interface to run the Epigraph tool suite is available (http://www.hiv.lanl.gov/content/sequence/EPIGRAPH/epigraph.html).

Highlights

Mosaics and Epigraphs solve essentially the same optimization problem (PTE coverage), and are expected to behave the same way experimentally
The Epigraph-based Therapeutic Vaccine (TTV) code optimizes the set of vaccine antigens for manufacture, such that the set will sample the diversity of the target population, and enable the best vaccine matches overall for infected individuals in the target population
There are many possible paths to achieve good Filoviridae potential T-cell epitope (PTE) coverage, and we systematically explored the outcomes of different design strategies, including optimization of Epigraph vaccine antigens using the 34 outbreak sequences simultaneously, as well as combinations that used serial optimization, either starting with a natural sequence, or starting with an Epigraph solution based on the 5 representative sequences selected from members of the Ebolavirus genus, or on a set of 8 representative sequences that sampled filovirus diversity

Summary

Introduction

Mosaics and Epigraphs solve essentially the same optimization problem (PTE coverage), and are expected to behave the same way experimentally. We developed Epigraph to enable computational solutions to two pressing T cell vaccine design problems that were intractable using the computationally slower Mosaic algorithm: a pan-filovirus T-cell vaccine and a strategy for matching vaccines to infecting strains in a therapeutic setting. There is keen interest in focusing vaccine-stimulated T-cell responses on conserved regions, to shift immunodominance to epitopes with a limited capacity to escape because they are under fitness constraints[16,23,24,25] Such T-cell vaccination strategies may be beneficial in either a preventive or therapeutic setting. The Epigraph-based TTV code optimizes the set of vaccine antigens for manufacture, such that the set will sample the diversity of the target population, and enable the best vaccine matches overall for infected individuals in the target population. Exploring the combinatorics of the many design options we considered to meet these criteria would have been prohibitive using the slower Mosaic code, but through systematic use of Epigraph, we were able to identify a promising design strategy that met our 3 criteria

Methods

Results

Conclusion