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Detecting Site-Specific Physicochemical Selective Pressures: Applications to the Class I HLA of the Human Major Histocompatibility Complex and the SRK of the Plant Sporophytic Self-Incompatibility System
Authors:Raazesh Sainudiin  Wendy Shuk Wan Wong  Krithika Yogeeswaran  June B Nasrallah  Ziheng Yang  Rasmus Nielsen
Institution:(1) Department of Statistical Science, 301 Malott Hall, Cornell University, Ithaca, NY 14853, USA;(2) Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA;(3) Department of Plant Biology, Cornell University, Ithaca, NY 14853, USA;(4) Department of Biology, University College London, London, UK
Abstract:Models of codon substitution are developed that incorporate physicochemical properties of amino acids. When amino acid sites are inferred to be under positive selection, these models suggest the nature and extent of the physicochemical properties under selection. This is accomplished by first partitioning the codons on the basis of some property of the encoded amino acids. This partition is used to parametrize the rates of property-conserving and property-altering base substitutions at the codon level by means of finite mixtures of Markov models that also account for codon and transition:transversion biases. Here, we apply this method to two positively selected receptors involved in ligand-recognition: the class I alleles of the human major histocompatibility complex (MHC) of known structure and the S-locus receptor kinase (SRK) of the sporophytic self-incompatibility system (SSI) in cruciferous plants (Brassicaceae), whose structure is unknown. Through likelihood ratio tests we demonstrate that at some sites, the positively selected MHC and SRK proteins are under physicochemical selective pressures to alter polarity, volume, polarity and/or volume, and charge to various extents. An empirical Bayes approach is used to identify sites that may be important for ligand recognition in these proteins.Reviewing Editor : Dr. Willie Swanson
Keywords:Codon-based Markov models  Likelihood ratio tests  MHC  Physicochemical selective pressures  SRK
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