Predicting the Interplay of Epitope Recognition and Evolution in RNA Viruses Under Immune Pressure

Abstract

RNA viruses can rapidly mutate, causing therapeutics and vaccines to loose their effectiveness. The long-term goal of this project is to predict such mutations, in order to anticipate their effect and design better therapeutics and vaccines. In the funding period reported here, the specific goal was to build a predictive model of viral escape from immune pressure exerted by monospecific T cells in vitro. This goal was achieved: a general model was developed that integrates selective pressure determined by the phenotype of a virus with random mutations at the genotype level. To make quantitative predictions, the model requires parameters characterizing the selective pressure. These were determined experimentally for the specific case of T cell recognition of the LCM virus epitope NP 396 - 404. The parameterized model was shown to agree with escape mutants reported to arise when co-culturing the virus with these epitope-specific T cells.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 30, 2008
Accession Number
ADA500852

Entities

People

  • Alesandro Sette
  • Bjoern Peters
  • Martin Blythe

Organizations

  • La Jolla Institute for Allergy and Immunology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Agreements
  • Algorithms
  • Allergy And Immunology
  • Cells
  • Department Of Defense
  • Engineering
  • Genetics
  • Lymphocytes
  • Mathematical Models
  • Mathematics
  • Medical Personnel
  • Models
  • Predictive Modeling
  • Recognition
  • Rna Viruses
  • Sequences
  • Therapy

Fields of Study

  • Biology

Readers

  • Immunology
  • Molecular Genetics
  • Oncology (Cancer Research).

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Biotechnology
  • Biotechnology - Cancer Biotech