Journal of Molecular Evolution

, Volume 60, Issue 4, pp 499–504 | Cite as

Tertiary Windowing to Detect Positive Diversifying Selection

  • Ann-Charlotte Berglund
  • Björn Wallner
  • Arne Elofsson
  • David A. Liberles


As a protein-encoding gene evolves, different selective pressures act on the gene temporally and spatially. An examination of the ratio of nonsynonymous-to-synonymous nucleotide substitution rate ratios (Ka/Ks) has proven to be a valuable method to examine selective pressures on protein encoding genes, including detecting positive diversifying selection. To gain power over averaging all sites in a gene together, examination of sites in primary sequence windows has frequently been employed. However, selection acts on folded proteins and sites that are close in tertiary space may not be close in primary sequence. A new method for the examination of Ka/Ks ratios based upon windows in tertiary structure is introduced and applied to the leptin gene family in mammals. Tertiary sequence windowing detects new sites under positive diversifying selection and detects positive diversifying selection with a more significant signal along various branches of the leptin gene family tree.


Positive diversifying selection Substitution rate Protein structure Leptin 


  1. Benjamini, Y, Hochberg, Y 1995Controlling the false discovery rate: a practical and powerful approach to multiple testingJ Roy Stat Soc B57289300Google Scholar
  2. Benner, SA, Trabesinger-Rueff, N, Schreiber, DR 1998Exobiology and post-genomic science. Converting primary structure into physiological functionAdv Enzyme Reg38155180CrossRefGoogle Scholar
  3. Benner, SA, Caraco, MD, Thomson, JM, Gaucher, EA 2002Planetary biology: paleontological, geological, and molecular histories of lifeScience296864868CrossRefPubMedGoogle Scholar
  4. Berman, HW, Westbrook, J, Feng, Z, Gilliland, G, Bhat, TN, Weissig, H, Shindyalov, IN, Bourne, PE 2000The Protein Data BankNucleic Acids Res28235242CrossRefPubMedGoogle Scholar
  5. Comeron, JM 1995A method for estimating the numbers of synonymous and nonsynonymous substitutions per siteJ Mol Evol4111521159CrossRefPubMedGoogle Scholar
  6. Endo, T, Ikeo, K, Gojobori, T 1996Large-scale search for genes on which positive selection may operateMol Biol Evol13685690PubMedGoogle Scholar
  7. Fares, MA, Elena, SF, Ortiz, J, Moya, A, Barrio, E 2002A sliding window-based method to detect selective constraints in protein-coding genes and its application to RNA virusesJ Mol Evol55509521CrossRefPubMedGoogle Scholar
  8. Gaucher, EA, Miyamoto, MM, Benner, SA 2003Evolutionary, structural and biochemical evidence for a new interaction site of the leptin obesity proteinGenetics16315491553PubMedGoogle Scholar
  9. Golding, GB, Dean, AM 1998The structural basis of molecular adaptationMol Biol Evol15355369PubMedGoogle Scholar
  10. Goldman, N, Yang, Z 1994A codon-based model of nucleotide substitution for protein-coding DNA sequencesMol Biol Evol11725736PubMedGoogle Scholar
  11. Grasso, P, Leinung, MC, Lee, DW 1999Epitope mapping of secreted mouse leptin utilizing peripherally administered synthetic peptidesRegul Pept8593100CrossRefPubMedGoogle Scholar
  12. Hiroike, T, Higo, J, Jingami, H, Toh, H 2000Homology modeling of human leptin/leptin receptor complexBiochem Biophys Res Commun275154158CrossRefPubMedGoogle Scholar
  13. Huelsenbeck, JP, Ronquist, F 2001MRBAYES: Bayesian inference of phylogenyBioinformatics17754755CrossRefPubMedGoogle Scholar
  14. Hughes, AL, Nei, M 1989Nucleotide substitutions major histocompatibility complex class II loci: evidence for overdominant selectionProc Natl Acad Sci USA86958962PubMedGoogle Scholar
  15. Ina, Y 1995New methods for estimating the numbers of synonymous and nonsynonymous substitutionsJ Mol Evol40190226CrossRefPubMedGoogle Scholar
  16. Li, WH 1993Unbiased estimation of the rates of synonymous and nonsynonymous substitutionJ Mol Evol369699PubMedGoogle Scholar
  17. Li, WH, Wu, CI, Luo, CC 1985A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changesMol Biol Evol2150174PubMedGoogle Scholar
  18. Liberles, DA 2001Evaluation of methods for extermination of a reconstructed history of gene sequence evolutionMol Biol Evol1820402047PubMedGoogle Scholar
  19. Liberles, DA, Schreiber, DR, Govindarajan, S, Chamberlin, SG, Benner, SA 2001The Adaptive Evolution Database (TAED)Genome Biol216research0028.Google Scholar
  20. Messier, W, Stewart, CB 1997Episodic adaptive evolution of primate lysozymesNature385151154CrossRefPubMedGoogle Scholar
  21. Nei, M, Gojobori, T 1986Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutionsMol Biol Evol3418426PubMedGoogle Scholar
  22. Nielsen, R, Yang, Z 1998Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope geneGenetics148929936PubMedGoogle Scholar
  23. Pamilo, P, Bianchi, NO 1993Evolution of the Zfx and Zfy genes: rates and interdependence between the genesMol Biol Evol19271281Google Scholar
  24. Samson, WK, Murphy, TC, Robison, D, Vargas, T, Tau, E, Chang, JK 1996A 35 amino acid fragment of leptin inhibits feeding in the ratEndocrinology13751825185CrossRefPubMedGoogle Scholar
  25. Siltberg, J, Liberles, DA 2002A simple covarion-based approach to analyse nucleotide substitution ratesJ Evol Biol15588594Google Scholar
  26. Stamatiadis, DN, Chan, JL, Cogswell, R, Stefanopoulou, HC, Bullen, J, Katsilambros, N, Stathakis, CP, Mantzoros, CS 2003Elevated leptin fragments in renal failure correlate with BMI and haematopoiesis and are normalized by haemodialysisClin Endocrinol60434441CrossRefGoogle Scholar
  27. Suzuki, Y, Gojobori, T 1999A method for detecting positive selection at single amino acid sitesMol Biol Evol1613151328PubMedGoogle Scholar
  28. Yang, Z 1998Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolutionMol Biol Evol15568573PubMedGoogle Scholar
  29. Yang, Z 2002Phylogenetic analysis by maximum likelihood (PAML). Version 3.13University Collage LondonLondonGoogle Scholar
  30. Yang, Z, Nielsen, R 2002Codon-substitution models for detecting molecular adaption at individual sites along specific lineagesMol Biol Evol19908917PubMedGoogle Scholar
  31. Zhang, J, Nei, M 1997Accuracies of ancestral amino acid sequences inferred by the parsimony, likelihood, and distance methodsJ Mol Evol44S139S146PubMedGoogle Scholar
  32. Zhang, F, Basinski, MB, Beals, JM, Briggs, SL, Churgay, LM, Clawson, DK, DiMarchi, RD, Furman, TC, Hale, JE, Hsiung, HM, Schoner, BE, Smith, DP, Zhang, XY, Wery, JP, Schevitz, RW 1997Crystal structure of the obese protein leptin-E100Nature387206209CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Ann-Charlotte Berglund
    • 1
    • 2
  • Björn Wallner
    • 2
  • Arne Elofsson
    • 2
  • David A. Liberles
    • 1
    • 2
  1. 1.Computational Biology Unit, BCCSUniversity of BergenBergenNorway
  2. 2.Stockholm Bioinformatics Center, SCFABStockholm UniversityStockholmSweden

Personalised recommendations