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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Epitope discovery with phylogenetic hidden Markov models.

Miguel Lacerda1, Konrad Scheffler, Cathal Seoighe

  • 1School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland.

Molecular Biology and Evolution
|January 22, 2010
PubMed
Summary

We developed a new evolutionary model to predict T-cell epitopes by analyzing pathogen protein evolution influenced by host human leukocyte antigen (HLA) genotypes, improving epitope identification.

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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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Area of Science:

  • * Evolutionary biology
  • * Immunology
  • * Bioinformatics

Background:

  • * Current T-cell epitope prediction relies on protein sequence or structure.
  • * Host immune background, specifically human leukocyte antigen (HLA) genotype, influences pathogen evolution.
  • * Integrating evolutionary patterns with host immune data can enhance epitope identification.

Purpose of the Study:

  • * To develop a novel mutation-selection model for T-cell epitope evolution.
  • * To incorporate host HLA genotype as an environmental parameter in phylogenetic modeling.
  • * To improve the prediction of immunologically active T-cell epitopes.

Main Methods:

  • * Developed a mutation-selection model of T-cell epitope evolution.
  • * Incorporated host HLA genotype to influence evolutionary processes.
  • * Combined the evolutionary model with a hidden Markov model (HMM).
  • * Applied a phylogenetic hidden Markov model to HIV-1 sequences and HLA genotypes.

Main Results:

  • * Demonstrated a novel method to model T-cell epitope evolution influenced by host immune alleles.
  • * Identified contiguous amino acid positions evolving under immune pressure.
  • * Showcased the model's potential for enhancing epitope prediction accuracy.

Conclusions:

  • * The developed phylogenetic hidden Markov model offers a rigorous framework for epitope prediction.
  • * Incorporating host immune parameters into evolutionary models is a promising approach.
  • * This method can improve the identification of T-cell epitopes, with applications in infectious diseases like HIV-1.