Epitope discovery with phylogenetic hidden Markov models.

Hdl Handle:
http://hdl.handle.net/10147/108015
Title:
Epitope discovery with phylogenetic hidden Markov models.
Authors:
Lacerda, Miguel; Scheffler, Konrad; Seoighe, Cathal
Affiliation:
School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland.
Citation:
Epitope discovery with phylogenetic hidden Markov models. 2010, 27 (5):1212-20 Mol. Biol. Evol.
Journal:
Molecular biology and evolution
Issue Date:
May-2010
URI:
http://hdl.handle.net/10147/108015
DOI:
10.1093/molbev/msq008
PubMed ID:
20089717
Abstract:
Existing methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.
Language:
en
MeSH:
Alleles; Bayes Theorem; Epitopes, T-Lymphocyte; HIV Core Protein p24; HLA-B Antigens; Humans; Markov Chains; Models, Genetic; Models, Immunological; Phylogeny; Probability
ISSN:
1537-1719

Full metadata record

DC FieldValue Language
dc.contributor.authorLacerda, Miguelen
dc.contributor.authorScheffler, Konraden
dc.contributor.authorSeoighe, Cathalen
dc.date.accessioned2010-07-21T08:07:19Z-
dc.date.available2010-07-21T08:07:19Z-
dc.date.issued2010-05-
dc.identifier.citationEpitope discovery with phylogenetic hidden Markov models. 2010, 27 (5):1212-20 Mol. Biol. Evol.en
dc.identifier.issn1537-1719-
dc.identifier.pmid20089717-
dc.identifier.doi10.1093/molbev/msq008-
dc.identifier.urihttp://hdl.handle.net/10147/108015-
dc.description.abstractExisting methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.-
dc.language.isoenen
dc.subject.meshAlleles-
dc.subject.meshBayes Theorem-
dc.subject.meshEpitopes, T-Lymphocyte-
dc.subject.meshHIV Core Protein p24-
dc.subject.meshHLA-B Antigens-
dc.subject.meshHumans-
dc.subject.meshMarkov Chains-
dc.subject.meshModels, Genetic-
dc.subject.meshModels, Immunological-
dc.subject.meshPhylogeny-
dc.subject.meshProbability-
dc.titleEpitope discovery with phylogenetic hidden Markov models.en
dc.contributor.departmentSchool of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland.en
dc.identifier.journalMolecular biology and evolutionen

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