tetano
Editor, Senior Moderator
Mol Biol Evol. 2012 Mar 16. [Epub ahead of print]
Detecting patches of protein sites of influenza A viruses under positive selection.
Tusche C, Steinbr?ck L, McHardy AC.
Source
Max Planck Research Group for Computational Genomics & Epidemiology, Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbr?cken, Germany.
Abstract
Influenza A viruses are single-stranded RNA viruses capable of evolving rapidly to adapt to environmental conditions. Examples include the establishment of a virus in a novel host or an adaptation to increasing immunity within the host population due to prior infection or vaccination against a circulating strain. Knowledge of the viral protein regions under positive selection is therefore crucial for surveillance. We have developed a method for detecting positively selected patches of sites on the surface of viral proteins, which we assume to be relevant for adaptive evolution. We measure positive selection based on dN/dS ratios of genetic changes inferred by considering the phylogenetic structure of the data and suggest a graph cut algorithm to identify such regions. Our algorithm searches for dense and spatially distinct clusters of sites under positive selection on the protein surface. For the hemagglutinin protein of human influenza A viruses of the subtypes H3N2 and H1N1, our predicted sites significantly overlap with known antigenic and receptor-binding sites. From the structure and sequence data of the 2009 swine-origin influenza A/H1N1 hemagglutinin and PB2 protein, we identified regions that provide evidence of evolution under positive selection since introduction of the virus into the human population. The changes in PB2 overlap with sites reported to be associated with mammalian adaptation of the influenza A viruses. Application of our technique to the protein structures of viruses of yet unknown adaptive behavior could identify further candidate regions that are important for hostvirus interaction.
PMID:
22427709
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22427709
Detecting patches of protein sites of influenza A viruses under positive selection.
Tusche C, Steinbr?ck L, McHardy AC.
Source
Max Planck Research Group for Computational Genomics & Epidemiology, Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbr?cken, Germany.
Abstract
Influenza A viruses are single-stranded RNA viruses capable of evolving rapidly to adapt to environmental conditions. Examples include the establishment of a virus in a novel host or an adaptation to increasing immunity within the host population due to prior infection or vaccination against a circulating strain. Knowledge of the viral protein regions under positive selection is therefore crucial for surveillance. We have developed a method for detecting positively selected patches of sites on the surface of viral proteins, which we assume to be relevant for adaptive evolution. We measure positive selection based on dN/dS ratios of genetic changes inferred by considering the phylogenetic structure of the data and suggest a graph cut algorithm to identify such regions. Our algorithm searches for dense and spatially distinct clusters of sites under positive selection on the protein surface. For the hemagglutinin protein of human influenza A viruses of the subtypes H3N2 and H1N1, our predicted sites significantly overlap with known antigenic and receptor-binding sites. From the structure and sequence data of the 2009 swine-origin influenza A/H1N1 hemagglutinin and PB2 protein, we identified regions that provide evidence of evolution under positive selection since introduction of the virus into the human population. The changes in PB2 overlap with sites reported to be associated with mammalian adaptation of the influenza A viruses. Application of our technique to the protein structures of viruses of yet unknown adaptive behavior could identify further candidate regions that are important for hostvirus interaction.
PMID:
22427709
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22427709