Predicting population coverage of T-cell epitope-based diagnostics and vaccines

By H. Bui, John Sidney, K. Dinh, S. Southwood, M. Newman, Alessandro Sette. BMC Bioinformatics, 2006

930 citations recorded by the source corpus.

BackgroundT cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a particular pathogen-derived epitope. A given epitope will elicit a response only in individuals that express an MHC molecule capable of binding that particular epitope. MHC molecules are extremely polymorphic and over a thousand different human MHC (HLA) alleles are known. A disproportionate amount of MHC polymorphism occurs in positions constituting the peptide-binding region, and as a result, MHC molecules exhibit a widely varying binding specificity. In the design of peptide-based vaccines and diagnostics, the issue of population coverage in relation to MHC polymorphism is further complicated by the fact that different HLA types are expressed at dramatically different frequencies in different ethnicities. Thus, without careful consideration, a vaccine or diagnostic with ethnically biased population coverage could result.ResultsTo address this issue, an algorithm was developed to calculate, on the basis of HLA genotypic frequencies, the fraction of individuals expected to respond to a given epitope set, diagnostic or vaccine. The population coverage estimates are based on MHC binding and/or T cell restriction data, although the tool can be utilized in a more general fashion. The algorithm was implemented as a web-application available at http://epitope.liai.org:8080/tools/population.ConclusionWe have developed a web-based tool to predict population coverage of T-cell epitope-based diagnostics and vaccines based on MHC binding and/or T cell restriction data. Accordingly, epitope-based vaccines or diagnostics can be designed to maximize population coverage, while minimizing complexity (that is, the number of different epitopes included in the diagnostic or vaccine), and also minimizing the variability of coverage obtained or projected in different ethnic groups.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ee970e135a7e0aa5bd091d70171490079f965d67

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In Immunology

  1. Bjoern Peters
  2. Alessandro Sette
  3. Sinu Paul
  4. Swapnil Mahajan
  5. Sandeep Kumar Dhanda
  6. Ward Fleri
  7. Xiaojun Xu
  8. Morten Nielsen

In Molecular Biology

  1. Alessandro Sette
  2. Bjoern Peters
  3. Sinu Paul
  4. Swapnil Mahajan
  5. Sandeep Kumar Dhanda
  6. Ward Fleri
  7. Xiaojun Xu
  8. Morten Nielsen

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