Journal of Molecular Evolution

, Volume 60, Issue 3, pp 315–326 | Cite as

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

  • Raazesh Sainudiin
  • Wendy Shuk Wan Wong
  • Krithika Yogeeswaran
  • June B. Nasrallah
  • Ziheng Yang
  • Rasmus Nielsen
Article

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.

Keywords

Codon-based Markov models Likelihood ratio tests MHC Physicochemical selective pressures SRK 

Notes

Acknowledgments

R.S. is supported by the Integrative Graduate Education and Research Traineeship from National Science Foundation Grant DGE-9870631, National Science Foundation Grant DEB-9602229 to Carlos Castillo-Chavez, and National Science Foundation grant DEB-0089487 to Rasmus Nielsen. K.Y. is supported in part by National Institutes of Health grant GM57527 to June Nasrallah. Z.Y. is supported by grants from the Biotechnology and Biological Sciences Research Council (UK) and the Human Frontier Science Program (EU). R.S. thanks Willie Swanson for guiding him through various phylogenetic tools, Wa Yang and Joel Bielewski for the most insightful discussions on physicochemical selection, Stephane Aris-Brosou for discussions on Bayesian confidence sets on tree spaces, Aardra Pontis for discussions on the SSI system, and Akendra De, Matt Dimmic, Richard Durrett, and two anonymous reviewers for comments.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Raazesh Sainudiin
    • 1
  • Wendy Shuk Wan Wong
    • 2
  • Krithika Yogeeswaran
    • 3
  • June B. Nasrallah
    • 3
  • Ziheng Yang
    • 4
  • Rasmus Nielsen
    • 2
  1. 1.Department of Statistical Science, 301 Malott HallCornell UniversityIthacaUSA
  2. 2.Department of Biological Statistics and Computational BiologyCornell UniversityIthacaUSA
  3. 3.Department of Plant BiologyCornell UniversityIthacaUSA
  4. 4.Department of BiologyUniversity College LondonLondonUK

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