Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response

Front Immunol. 2021 Feb 18:12:609884. doi: 10.3389/fimmu.2021.609884. eCollection 2021.


Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.

Keywords: CD8 T-cells; HIV; T-cell epitopes; machine learning; vaccines.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Antigens, Viral / genetics
  • Antigens, Viral / immunology
  • CD8-Positive T-Lymphocytes / immunology*
  • Epitopes, T-Lymphocyte / immunology
  • Genetic Variation
  • Genome, Viral
  • HIV / immunology*
  • HIV Infections / immunology*
  • HIV Infections / virology*
  • HLA Antigens / immunology
  • Host-Pathogen Interactions / immunology*
  • Humans
  • Machine Learning*
  • Peptides / immunology
  • Proteome
  • Viral Proteins


  • Antigens, Viral
  • Epitopes, T-Lymphocyte
  • HLA Antigens
  • Peptides
  • Proteome
  • Viral Proteins