Graph‐based optimization of epitope coverage for vaccine antigen design

Theiler, James; Korber, Bette

doi:10.1002/sim.7203

Cited by 24 publications

(23 citation statements)

References 44 publications

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“…Another variation is presented by Epigraph, 33 which is a developed algorithm enabling to maximize the potential epitope coverage for a diverse pathogen population and so the design of single or multiantigen vaccines. The problem is formulated again on graphs [177] , but this time the edges of the graphs represent the overlapping between two epitopes. The candidate antigens are represented as walks 34 that traverse this graph (notice that although they call it paths, they allow for repetition of nodes, so it is more correct to call them walks).…”

Section: Vaccinesmentioning

confidence: 99%

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

2020

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Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon become a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches – deterministic, data-driven, stochastic, agent-based, and their combinations – to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts – (multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc. – that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables, but it also plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively the questions: could existing drugs work against SARS CoV-2? and can new vaccines be developed in time? demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergency situation as the current one to help in fighting future pandemics.

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Section: Vaccinesmentioning

confidence: 99%

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

2020

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“…This study aims to evaluate a novel vaccine antigen designer, called the Epigraph vaccine designer tool, for the design of a universal swH3 influenza vaccine 17 . Epigraph is a graph-based algorithm that creates a cocktail of vaccine antigens designed to maximize the potential epitope coverage of a highly diverse population.…”

Section: >460 Reported Swine Influenza Virus (Siv) Variant Infectionsmentioning

confidence: 99%

“…We designed the swine H3 (swH3) epigraph hemagglutinins (HA) using the Epigraph vaccine designer tool, a graph-based algorithm which creates a cocktail of immunogens designed to maximize potential epitope coverage in a population 17,18 . First, the epigraph vaccine designer determines the frequency of each potential epitope of designated length (k-mer) in the target population.…”

Section: Development and Characterization Of The Swh3 Epigraph Ha Vacmentioning

confidence: 99%

Epigraph Hemagglutinin Vaccine Induces Broad Cross-reactive Immunity Against Swine H3 Influenza Virus

Bullard

Corder

Korber

et al. 2020

Preprint

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Swine influenza virus (SIV) is a significant burden on the pork industry and threat to human health due to its zoonotic potential. Here, we utilized epigraph, a computational algorithm, to design a universal swine H3 (swH3) influenza vaccine. The epigraph hemagglutinin proteins were delivered using an Adenovirus type 5 vector and compared to a wild type hemagglutinin and the commercial inactivated vaccine, FluSure. In mice, epigraph vaccination led to significant cross-reactive antibody and T-cell responses against a diverse panel of swH3 isolates. Epigraph vaccination also reduced weight loss and lung viral titers in mice after challenge with three divergent swH3 viruses. Vaccination studies in swine, the target species for this vaccine, showed stronger levels of crossreactive antibodies and T-cell responses after immunization with epigraph vaccine compared to the wild type and FluSure vaccines. In both murine and swine models, epigraph vaccination showed superior cross-reactive immunity that should be further investigated as a universal swH3 vaccine.

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“…Thus, successful CTL-based HIV eradication strategies should target subdominant viral epitopes or epitopes for which escape mutations have not accumulated [121]. Graph-based epitope selection strategies using mathematical algorithm program tools such as Epigraph can be used to assess global epitope diversity and/or conservation when designing DC vaccine antigenic formulations [124]. An alternative to this approach is to base antigen selection according to biochemical network analyses of CD8 + T cell epitope topology based on HIV proteome data, which has revealed an inverse relationship between protective CTL epitopes targeted by HIV controllers and mutational frequency in vivo [125].…”

Section: Mutations In Ctl Epitopesmentioning

confidence: 99%

Role of Dendritic Cells in Exposing Latent HIV-1 for the Kill

Kristoff

Rinaldo

Mailliard

2019

Viruses

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The development of effective yet nontoxic strategies to target the latent human immunodeficiency virus-1 (HIV-1) reservoir in antiretroviral therapy (ART)-suppressed individuals poses a critical barrier to a functional cure. The 'kick and kill' approach to HIV eradication entails proviral reactivation during ART, coupled with generation of cytotoxic T lymphocytes (CTLs) or other immune effectors equipped to eliminate exposed infected cells. Pharmacological latency reversal agents (LRAs) that have produced modest reductions in the latent reservoir ex vivo have not impacted levels of proviral DNA in HIV-infected individuals. An optimal cure strategy incorporates methods that facilitate sufficient antigen exposure on reactivated cells following the induction of proviral gene expression, as well as the elimination of infected targets by either polyfunctional HIV-specific CTLs or other immune-based strategies. Although conventional dendritic cells (DCs) have been used extensively for the purpose of inducing antigen-specific CTL responses in HIV-1 clinical trials, their immunotherapeutic potential as cellular LRAs has been largely ignored. In this review, we discuss the challenges associated with current HIV-1 eradication strategies, as well as the unharnessed potential of ex vivo-programmed DCs for both the 'kick and kill' of latent HIV-1.

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Graph‐based optimization of epitope coverage for vaccine antigen design

Cited by 24 publications

References 44 publications

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

Epigraph Hemagglutinin Vaccine Induces Broad Cross-reactive Immunity Against Swine H3 Influenza Virus

Role of Dendritic Cells in Exposing Latent HIV-1 for the Kill

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