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  • Review Article
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Causes of molecular convergence and parallelism in protein evolution

Key Points

  • An important question in evolutionary genetics concerns the extent to which adaptive convergence in protein function is caused by convergent or parallel changes at the amino acid level. Even when there is a many-to-one mapping of genotype to phenotype, particular mutations may be preferentially fixed (substitution bias) owing to among-site variation in the rate of mutation to function-altering alleles and/or variation among mutations in their probability of fixation once they arise.

  • Within the set of mutations that have functionally equivalent effects on a selected phenotype, those that incur a lower magnitude of deleterious pleiotropy will generally have higher fixation probabilities. Mutational pleiotropy may therefore represent an important source of substitution bias.

  • A key finding is that the fitness effects of amino acid mutations are often conditional on the genetic background in which they occur. This context dependence (epistasis) reduces the probability of molecular convergence and parallelism because it reduces the number of possible mutations that have unconditionally acceptable effects in divergent genetic backgrounds.

  • Context-dependent mutational effects often stem from pleiotropic trade-offs, as evidenced by cases where the fitness impact of a given mutation is determined by compensatory (conditionally beneficial) mutations at other sites in the same protein.

  • Even if mutations have identical functional effects on a selected phenotype, they can have different fitness effects owing to a nonlinear mapping of phenotype to fitness. Thus, probabilities of convergence and parallelism are reduced by species differences in the genotype-phenotype map and by differences in the phenotype-fitness map.

  • Although computational analyses of sequence variation play a key part in suggesting hypotheses about the causes of convergent and parallel substitutions and their possible adaptive significance, experimental analyses of specific mutations are necessary to test such hypotheses.

Abstract

To what extent is the convergent evolution of protein function attributable to convergent or parallel changes at the amino acid level? The mutations that contribute to adaptive protein evolution may represent a biased subset of all possible beneficial mutations owing to mutation bias and/or variation in the magnitude of deleterious pleiotropy. A key finding is that the fitness effects of amino acid mutations are often conditional on genetic background. This context dependence (epistasis) can reduce the probability of convergence and parallelism because it reduces the number of possible mutations that are unconditionally acceptable in divergent genetic backgrounds. Here, I review factors that influence the probability of replicated evolution at the molecular level.

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Figure 1: Convergent and parallel substitutions: phylogenetically replicated changes that involve different mutational paths.
Figure 2: Phylogenetic patterns of parallel and unique substitutions in ATPα1 that are associated with resistance to toxic cardenolides in herbivorous insects.
Figure 3: Schematic depiction of how single-position fitness landscapes change through time.
Figure 4: Mutations with additive effects on phenotype can have epistatic effects on fitness.

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References

  1. Wake, D. B. Homoplasy: the result of natural selection, or evidence of design limitations? Am. Naturalist 138, 543–567 (1991).

    Article  Google Scholar 

  2. Losos, J. B. Covergence, adaptation, and constraint. Evolution 65, 1827–1840 (2011).

    Article  PubMed  Google Scholar 

  3. Stern, D. L. & Orgogozo, V. The loci of evolution: how predictable is genetic evolution? Evolution 62, 2155–2177 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stern, D. L. & Orgogozo, V. Is genetic evolution predictable? Science 323, 746–751 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chevin, L.-M., Martin, G. & Lenormand, T. Fisher's model and the genomics of adaptation: restricted pleiotropy, heterogeneous mutation, and parallel evolution. Evolution 64, 3213–3231 (2010).

    Article  PubMed  Google Scholar 

  6. Streisfeld, M. A. & Rausher, M. D. Population genetics, pleiotropy, and the preferential fixation of mutations during adaptive evolution. Evolution 65, 629–642 (2011). This paper describes a statistical framework for testing whether different classes of mutations have made disproportionate contributions to adaptive phenotypic evolution.

    Article  PubMed  Google Scholar 

  7. Stern, D. L. The genetic causes of convergent evolution. Nat. Rev. Genet. 14, 751–764 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. de Visser, J. A. G. M. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Gould, S. J. Wonderful Life: The Burgess Shale and the Nature of History (W. W. Norton and Company, 1989).

    Google Scholar 

  10. Arendt, J. & Reznick, D. Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation? Trends Ecol. Evol. 23, 26–32 (2008).

    Article  PubMed  Google Scholar 

  11. Zhang, J. Z. & Kumar, S. Detection of convergent and parallel evolution at the amino acid sequence level. Mol. Biol. Evol. 14, 527–536 (1997).

    Article  CAS  PubMed  Google Scholar 

  12. Castoe, T. A. et al. Evidence for an ancient adaptive episode of convergent molecular evolution. Proc. Natl Acad. Sci. USA 106, 8986–8991 (2009).

    Article  PubMed  Google Scholar 

  13. Christin, P.-A., Weinreich, D. M. & Besnard, G. Causes and evolutionary significance of genetic convergence. Trends Genet. 26, 400–405 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Goldstein, R. A., Pollard, S. T., Shah, S. D. & Pollock, D. D. Nonadaptive amino acid convergence rates decrease over time. Mol. Biol. Evol. 32, 1373–1381 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yokoyama, S., Tada, T., Zhang, H. & Britt, L. Elucidation of phenotypic adaptations: molecular analyses of dim-light vision proteins in vertebrates. Proc. Natl Acad. Sci. USA 105, 13480–13485 (2008).

    Article  PubMed  Google Scholar 

  16. Yokoyama, S., Yang, H. & Starmert, W. T. Molecular basis of spectral tuning in the red- and green-sensitive (M/LWS) pigments in vertebrates. Genetics 179, 2037–2043 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Projecto-Garcia, J. et al. Repeated elevational transitions in hemoglobin function during the evolution of Andean hummingbirds. Proc. Natl Acad. Sci. USA 110, 20669–20674 (2013). This integrative study documents a striking example of adaptive convergence in protein function, where parallel amino acid substitutions at the same site produced repeated increases in haemoglobin-oxygen affinity in multiple highland lineages of Andean hummingbirds.

    Article  CAS  PubMed  Google Scholar 

  18. Dobler, S., Dalla, S., Wagschal, V. & Agrawal, A. A. Community-wide convergent evolution in insect adaptation to toxic cardenolides by substitutions in the Na, K-ATPase. Proc. Natl Acad. Sci. USA 109, 13040–13045 (2012).

    Article  PubMed  Google Scholar 

  19. Zhen, Y., Aardema, M. L., Medina, E. M., Schumer, M. & Andolfatto, P. Parallel molecular evolution in an herbivore community. Science 337, 1634–1637 (2012). References 18 and 19 document a striking pattern of parallel molecular evolution in the ATPase enzymes of herbivorous insects that have convergently evolved resistance to plant-derived toxins.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ujvari, B. et al. Widespread convergence in toxin resistance by predictable molecular evolution. Proc. Natl Acad. Sci. USA 112, 11911–11916 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Feldman, C. R. et al. Constraint shapes convergence in tetrodotoxin-resistant sodium channels of snakes. Proc. Natl Acad. Sci. USA 109, 4556–4561 (2012).

    Article  PubMed  Google Scholar 

  22. Brodie, E. D. 3rd & Brodie, E. D. Jr Predictably convergent evolution of sodium channels in the arms race between predators and prey. Brain. Behav. Evol. 86, 48–57 (2015).

    Article  PubMed  Google Scholar 

  23. ffrench-Constant, R. H. The molecular and population-genetics of cyclodiene insecticide resistance. Insect Biochem. Mol. Biol. 24, 335–345 (1994).

    Article  CAS  PubMed  Google Scholar 

  24. ffrench-Constant, R. H., Pittendrigh, B., Vaughan, A. & Anthony, N. Why are there so few resistance-associated mutations in insecticide target genes? Phil. Trans. R. Soc. Series B-Biol. Sci. 353, 1685–1693 (1998).

    Article  CAS  Google Scholar 

  25. Broser, M. et al. Structural basis of cyanobacterial photosystem II inhibition by the herbicide terbutryn. J. Biol. Chem. 286, 15964–15972 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).

    Article  CAS  Google Scholar 

  27. Bull, J. J. et al. Exceptional convergent evolution in a virus. Genetics 147, 1497–1507 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Wichman, H. A., Badgett, M. R., Scott, L. A., Boulianne, C. M. & Bull, J. J. Different trajectories of parallel evolution during viral adaptation. Science 285, 422–424 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Rokyta, D. R., Joyce, P., Caudle, S. B. & Wichman, H. A. An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nat. Genet. 37, 441–444 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Rokyta, D. R., Abdo, Z. & Wichman, H. A. The genetics of adaptation for eight microvirid bacteriophages. J. Mol. Evol. 69, 229–239 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. van Ditmarsch, D. et al. Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep. 4, 697–708 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Avise, J. C. & Robinson, T. J. Hemiplasy: a new term in the lexicon of phylogenetics. Syst. Biol. 57, 503–507 (2008).

    Article  PubMed  Google Scholar 

  34. Hahn, M. W. & Nakhleh, L. Irrational exuberance for resolved species trees. Evolution 70, 7–17 (2016). This paper explains how genealogical discordance between gene trees and species trees (due to incomplete lineage sorting or introgressive hybridization) can lead to misleading inferences about trait evolution.

    Article  PubMed  Google Scholar 

  35. Yampolsky, L. Y. & Stoltzfus, A. Bias in the introduction of variation as an orienting factor in evolution. Evol. Dev. 3, 73–83 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Stoltzfus, A. Mutationism and the dual causation of evolutionary change. Evol. Dev. 8, 304–317 (2006).

    Article  PubMed  Google Scholar 

  37. Stoltzfus, A. & Yampolsky, L. Y. Climbing Mount Probable: mutation as a cause of nonrandomness in evolution. J. Hered. 100, 637–647 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Yampolsky, L. Y. & Stoltzfus, A. The exchangeability of amino acids in proteins. Genetics 170, 1459–1472 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. McCandlish, D. M. & Stoltzfus, A. Modeling evolution using the probability of fixation: history and implications. Quarterly Rev. Biol. 89, 225–252 (2014).

    Article  Google Scholar 

  40. Lozovsky, E. R. et al. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proc. Natl Acad. Sci. USA 106, 12025–12030 (2009).

    Article  PubMed  Google Scholar 

  41. Weigand, M. R. & Sundin, G. W. General and inducible hypermutation facilitate parallel adaptation in Pseudomonas aeruginosa despite divergent mutation spectra. Proc. Natl Acad. Sci. USA 109, 13680–13685 (2012).

    Article  PubMed  Google Scholar 

  42. Wong, A., Rodrigue, N. & Kassen, R. Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa. PloS Genet. 8, e1002928 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Couce, A., Rodriguez-Rojas, A. & Blazquez, J. Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. R. Soc. B 282, 20142698 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Galen, S. C. et al. Contribution of a mutational hotspot to adaptive changes in hemoglobin function in high-altitude Andean house wrens. Proc. Natl Acad. Sci. USA 112, 13958–13963 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Wang, X. J., Minasov, G. & Shoichet, B. K. Evolution of an antibiotic resistance enzyme constrained by stability and activity trade-offs. J. Mol. Biol. 320, 85–95 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. DePristo, M. A., Weinreich, D. M. & Hartl, D. L. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat. Rev. Genet. 6, 678–687 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Bloom, J. D., Labthavikul, S. T., Otey, C. R. & Arnold, F. H. Protein stability promotes evolvability. Proc. Natl Acad. Sci. USA 103, 5869–5874 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Tokuriki, N., Stricher, F., Serrano, L. & Tawfik, D. S. How protein stability and new functions trade off. PLoS Comput. Biol. 4, e1000002 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tokuriki, N. & Tawfik, D. S. Stability effects of mutations and protein evolvability. Curr. Opin. Struct. Biol. 19, 596–604 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Harms, M. J. & Thornton, J. W. Evolutionary biochemistry: revealing the historical and physical causes of protein properties. Nat. Rev. Genet. 14, 559–571 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gong, L. I., Suchard, M. A. & Bloom, J. D. Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013). This elegant experimental study documents the context-dependent fitness effects of stabilizing and destabilizing amino acid substitutions during the long-term evolution of an influenza nucleoprotein.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Otto, S. P. Two steps forward, one step back: the pleiotropic effects of favoured alleles. Proc. R. Soc. Lond. B 271, 705–714 (2004).

    Article  CAS  Google Scholar 

  54. Gompel, N. & Prud'homme, B. The causes of repeated genetic evolution. Dev. Biol. 332, 36–47 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Martin, A. & Orgogozo, V. The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation. Evolution 67, 1235–1250 (2013).

    CAS  PubMed  Google Scholar 

  56. Brown, K. M. et al. Compensatory mutations restore fitness during the evolution of dihydrofolate reductase. Mol. Biol. Evol. 27, 2682–2690 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Schenk, M. F. et al. Role of pleiotropy during adaptation of TEM-1 beta-lactamase to two novel antibiotics. Evol. Appl. 8, 248–260 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Tufts, D. M. et al. Epistasis constrains mutational pathways of hemoglobin adaptation in high-altitude pikas. Mol. Biol. Evol. 32, 287–298 (2015). This experimental study shows that the phenotypic effects of amino acid mutations are conditional on the sequential order in which they occur during the course of an adaptive walk; some mutations had opposite phenotypic effects depending on the genetic background in which they occurred.

    Article  CAS  PubMed  Google Scholar 

  59. Bloom, J. D. & Arnold, F. H. In the light of directed evolution: pathways of adaptive protein evolution. Proc. Natl Acad. Sci. USA 106, 9995–10000 (2009).

    Article  PubMed  Google Scholar 

  60. Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell. Biol. 10, 866–876 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Packer, M. S. & Liu, D. R. Methods for the directed evolution of proteins. Nat. Rev. Genet. 16, 379–394 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Bridgham, J. T., Carroll, S. M. & Thornton, J. W. Evolution of hormone-receptor complexity by molecular exploitation. Science 312, 97–101 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Ortlund, E. A., Bridgham, J. T., Redinbo, M. R. & Thornton, J. W. Crystal structure of an ancient protein: evolution by conformational epistasis. Science 317, 1544–1548 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bridgham, J. T., Ortlund, E. A. & Thornton, J. W. An epistatic ratchet constrains the direction of glucocorticoid receptor evolution. Nature 461, 515–519 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dickinson, B. C., Leconte, A. M., Allen, B., Esvelt, K. M. & Liu, D. R. Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution. Proc. Natl Acad. Sci. USA 110, 9007–9012 (2013).

    Article  PubMed  Google Scholar 

  66. Natarajan, C. et al. Epistasis among adaptive mutations in deer mouse hemoglobin. Science 340, 1324–1327 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Reetz, M. T. The importance of additive and non-additive mutational effects in protein engineering. Angew. Chem. Int. Ed. 52, 2658–2666 (2013).

    Article  CAS  Google Scholar 

  68. Harms, M. J. & Thornton, J. W. Historical contingency and its biophysical basis in glucocorticoid receptor evolution. Nature 512, 203–207 (2014). This innovative study integrates a directed evolution approach with reconstructive inference to demonstrate that rare permissive mutations may represent an important source of contingency in the evolution of novel protein functions.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Yokoyama, S. et al. Epistatic adaptive evolution of human color vision. PloS Genet. 10, e1004884 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Bank, C., Hietpas, R. T., Jensen, J. D. & Bolon, D. N. A. A systematic survey of an intragenic epistatic landscape. Mol. Biol. Evol. 32, 229–238 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Kaltenbach, M., Jackson, C. J., Campbell, E. C., Hollfelder, F. & Tokuriki, N. Reverse evolution leads to genotypic incompatibility despite functional and active site convergence. eLife 4, e06492 (2015). This study dissects the mechanistic basis of intramolecular epistatic interactions in an experimentally evolved enzyme, and demonstrates how such interactions can prevent site-specific mutational reversions to ancestral amino acids.

    Article  PubMed Central  Google Scholar 

  72. Lunzer, M., Milter, S. P., Felsheim, R. & Dean, A. M. The biochemical architecture of an ancient adaptive landscape. Science 310, 499–501 (2005).

    Article  CAS  PubMed  Google Scholar 

  73. Bershtein, S., Segal, M., Bekerman, R., Tokuriki, N. & Tawfik, D. S. Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature 444, 929–932 (2006).

    Article  CAS  PubMed  Google Scholar 

  74. Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

    Article  CAS  PubMed  Google Scholar 

  75. da Silva, J., Coetzer, M., Nedellec, R., Pastore, C. & Mosier, D. E. Fitness epistasis and constraints on adaptation in a human immunodeficiency virus type 1 protein region. Genetics 185, 293–303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lunzer, M., Golding, G. B. & Dean, A. M. Pervasive cryptic epistasis in molecular evolution. Plos Genet. 6, e1001162 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Kvitek, D. J. & Sherlock, G. Reciprocal sign epistasis between frequently experimentally evolved adaptive mutations causes a rugged fitness landscape. PLoS Genet. 7, e1002056 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Rokyta, D. R. et al. Epistasis between beneficial mutations and the phenotype-to-fitness map for a ssDNA virus. Plos Genet. 7, e1002075 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Salverda, M. L. M. et al. Initial mutations direct alternative pathways of protein evolution. PLoS Genet. 7, e1001321 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Schenk, M. F., Szendro, I. G., Salverda, M. L. M., Krug, J. & de Visser, J. A. G. M. Patterns of epistasis between beneficial mutations in an antibiotic resistance gene. Mol. Biol. Evol. 30, 1779–1787 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Parera, M. & Angel Martinez, M. Strong epistatic interactions within a single protein. Mol. Biol. Evol. 31, 1546–1553 (2014).

    Article  CAS  PubMed  Google Scholar 

  82. Weinreich, D. M., Lan, Y., Wylie, C. S. & Heckendorn, R. B. Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 23, 700–707 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Weinreich, D. M., Watson, R. A. & Chao, L. Sign epistasis and genetic constraint on evolutionary trajectories. Evolution 59, 1165–1174 (2005).

    CAS  PubMed  Google Scholar 

  84. Rosenblum, E. B., Parent, C. E. & Brandt, E. E. The molecular basis of phenotypic convergence. Annu. Rev. Ecol. Evol. Syst. 45, 203–226 (2014).

    Article  Google Scholar 

  85. Pollock, D. D., Thiltgen, G. & Goldstein, R. A. Amino acid coevolution induces an evolutionary Stokes shift. Proc. Natl Acad. Sci. USA 109, E1352–E1359 (2012). Similar to findings reported in reference 87, this computational study demonstrates that individual amino acid substitutions at a given site can alter the amino acid propensities of other sites in the same protein; consequently, once a given substitution has occurred, the protein will tend to equilibrate to the newly altered structural context via substitutions at other sites.

    Article  PubMed  Google Scholar 

  86. Bazykin, G. A. Changing preferences: deformation of single position amino acid fitness landscapes and evolution of proteins. Biol. Lett. 11, 20150315 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Shah, P., McCandlish, D. M. & Plotkin, J. B. Contingency and entrenchment in protein evolution under purifying selection. Proc. Natl Acad. Sci. USA 112, E3226–E3235 (2015). This study demonstrates that the set of acceptable amino acid substitutions at a given site is highly contingent on prior substitutions and — likewise — once a substitution has occurred at a site, mutational reversions to the ancestral state become increasingly deleterious owing to changes in structural context caused by substitutions at other sites.

    Article  CAS  PubMed  Google Scholar 

  88. Rogozin, I. B., Thomson, K., Csueroes, M., Carmel, L. & Koonin, E. V. Homoplasy in genome-wide analysis of rare amino acid replacements: the molecular-evolutionary basis for Vavilov's law of homologous series. Biol. Direct 3, 7 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Povolotskaya, I. S. & Kondrashov, F. A. Sequence space and the ongoing expansion of the protein universe. Nature 465, 922–926 (2010).

    Article  CAS  PubMed  Google Scholar 

  90. Naumenko, S. A., Kondrashov, A. S. & Bazykin, G. A. Fitness conferred by replaced amino acids declines with time. Biol. Lett. 8, 825–828 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Soylemez, O. & Kondrashov, F. A. Estimating the rate of irreversibility in protein evolution. Genome Biol. Evol. 4, 1213–1222 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Poon, A. & Chao, L. The rate of compensatory mutation in the DNA bacteriophage phi X174. Genetics 170, 989–999 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Poon, A. F. Y. & Chao, L. Functional origins of fitness effect-sizes of compensatory mutations in the DNA bacteriophage phi X174. Evolution 60, 2032–2043 (2006).

    CAS  PubMed  Google Scholar 

  94. Bloom, J. D. et al. Thermodynamic prediction of protein neutrality. Proc. Natl Acad. Sci. USA 102, 606–611 (2005).

    Article  CAS  PubMed  Google Scholar 

  95. Bloom, J. D., Romero, P. A., Lu, Z. & Arnold, F. H. Neutral genetic drift can alter promiscuous protein functions, potentially aiding functional evolution. Biol. Direct 2, 17 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Bloom, J. D., Gong, L. I. & Baltimore, D. Permissive secondary mutations enable the evolution of influenza oseltamivir resistance. Science 328, 1272–1275 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Kondrashov, A. S., Sunyaev, S. & Kondrashov, F. A. Dobzhansky-Muller incompatibilities in protein evolution. Proc. Natl Acad. Sci. USA 99, 14878–14883 (2002). This influential study documents cases where pathogenic amino acid variants in human proteins are present as wild-type residues in the orthologues of other mammals, suggesting that the fitness effects of mutations are highly dependent on the genetic background in which they occur.

    Article  CAS  PubMed  Google Scholar 

  98. Gao, L. Z. & Zhang, J. Z. Why are some human disease-associated mutations fixed in mice? Trends Genet. 19, 678–681 (2003).

    Article  CAS  PubMed  Google Scholar 

  99. Kulathinal, R. J., Bettencourt, B. R. & Hartl, D. L. Compensated deleterious mutations in insect genomes. Science 306, 1553–1554 (2004).

    Article  CAS  PubMed  Google Scholar 

  100. Azevedo, L., Suriano, G., van Asch, B., Harding, R. M. & Amorim, A. Epistatic interactions: how strong in disease and evolution? Trends Genet. 22, 581–585 (2006).

    Article  CAS  PubMed  Google Scholar 

  101. Ferrer-Costa, C., Orozco, M. & de la Cruz, X. Characterization of compensated mutations in terms of structural and physico-chemical properties. J. Mol. Biol. 365, 249–256 (2007).

    Article  CAS  PubMed  Google Scholar 

  102. Baresic, A., Hopcroft, L. E. M., Rogers, H. H., Hurst, J. M. & Martin, A. C. R. Compensated pathogenic deviations: analysis of structural effects. J. Mol. Biol. 396, 19–30 (2010).

    Article  CAS  PubMed  Google Scholar 

  103. Ivankov, D. N., Finkelstein, A. V. & Kondrashov, F. A. A structural perspective of compensatory evolution. Curr. Opin. Struct. Biol. 26, 104–112 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Xu, J. & Zhang, J. Why human disease-associated residues appear as the wild-type in other species: genome-scale structural evidence for the compensation hypothesis. Mol. Biol. Evol. 31, 1787–1792 (2014). Results of this bioinformatic study indicate that conditionally deleterious amino acid mutations are often compensated by second-site mutations in close proximity in the same protein.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Jordan, D. M. et al. Identification of cis-suppression of human disease mutations by comparative genomics. Nature 524, 225–229 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zhang, J. Z. Parallel adaptive origins of digestive RNases in Asian and African leaf monkeys. Nat. Genet. 38, 819–823 (2006). This study integrates statistical analyses of sequence divergence with manipulative in vitro experiments to make inferences about the adaptive significance of parallel amino acid substitutions in the RNase isozymes of leaf-eating monkeys.

    Article  CAS  PubMed  Google Scholar 

  107. Weber, R. E., Fago, A., Malte, H., Storz, J. F. & Gorr, T. A. Lack of conventional oxygen-linked proton and anion binding sites does not impair allosteric regulation of oxygen binding in dwarf caiman hemoglobin. Am. J. Physiol. Regul. Integ. Comp. Physiol. 305, R300–R312 (2013).

    Article  CAS  Google Scholar 

  108. Natarajan, C. et al. Convergent evolution of hemoglobin function in high-altitude Andean waterfowl involves limited parallelism at the molecular sequence level. PloS Genet. 11, e1005681 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Revsbech, I. G. et al. Hemoglobin function and allosteric regulation in semi-fossorial rodents (family Sciuridae) with different altitudinal ranges. J. Exp. Biol. 216, 4264–4271 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Martin, G., Elena, S. F. & Lenormand, T. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nat. Genet. 39, 555–560 (2007).

    Article  CAS  PubMed  Google Scholar 

  111. Pearson, V. M., Miller, C. R. & Rokyta, D. R. The consistency of beneficial fitness effects of mutations across diverse genetic backgrounds. PLoS ONE 7, e43864 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Bazykin, G. A. et al. Extensive parallelism in protein evolution. Biol. Direct 2, 20 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Rokas, A. & Carroll, S. B. Frequent and widespread parallel evolution of protein sequences. Mol. Biol. Evol. 25, 1943–1953 (2008). Results of this comparative sequence analysis suggest that a large fraction of parallel amino acid substitutions may be attributable to purifying selection that constrains substitutions to a restricted set of physicochemically similar amino acids.

    Article  CAS  PubMed  Google Scholar 

  114. Breen, M. S., Kemena, C., Vlasov, P. K., Notredame, C. & Kondrashov, F. A. Epistasis as the primary factor in molecular evolution. Nature 490, 535–538 (2012).

    Article  CAS  PubMed  Google Scholar 

  115. Usmanova, D. R., Ferretti, L., Povolotskaya, I. S., Vlasov, P. K. & Kondrashov, F. A. A model of substitution trajectories in sequence space and long-term protein evolution. Mol. Biol. Evol. 32, 542–554 (2015).

    Article  PubMed  Google Scholar 

  116. Zou, Z. & Zhang, J. Are convergent and parallel amino acid substitutions in protein evolution more prevalent than neutral expectations? Mol. Biol. Evol. 32, 2085–2096 (2015). This comparative genomic analysis demonstrates that inferred levels of molecular convergence and parallelism in eukaryotic proteins are largely consistent with neutral expectations, provided that among-site variation in functional constraint is taken into account.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Zhang, J. Parallel functional changes in the digestive RNases of ruminants and colobines by divergent amino acid substitutions. Mol. Biol. Evol. 20, 1310–1317 (2003).

    Article  CAS  PubMed  Google Scholar 

  118. McCracken, K. G. et al. Parallel evolution in the major haemoglobin genes of eight species of Andean waterfowl. Mol. Ecol. 18, 3992–4005 (2009).

    Article  CAS  PubMed  Google Scholar 

  119. Parker, J. et al. Genome-wide signatures of convergent evolution in echolocating mammals. Nature 502, 228–231 (2013).

    Article  CAS  PubMed  Google Scholar 

  120. Thomas, G. W. C. & Hahn, M. W. Determining the null model for detecting adaptive convergence from genomic data: a case study using echolocating mammals. Mol. Biol. Evol. 32, 1232–1236 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Zou, Z. & Zhang, J. No genome-wide protein sequence convergence for echolocation. Mol. Biol. Evol. 32, 1237–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Mendes, F. K. & Hahn, M. W. Gene tree discordance causes apparent substitution rate variation. bioRxiv http://dx.doi.org/10.1101/029371 (2016).

  123. Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).

    Article  CAS  PubMed  Google Scholar 

  124. Pamilo, P. & Nei, M. Relationships between gene trees and species trees. Mol. Biol. Evol. 5, 568–583 (1988).

    CAS  PubMed  Google Scholar 

  125. Maddison, W. P. Gene trees in species trees. Syst. Biol. 46, 523–536 (1997).

    Article  Google Scholar 

  126. Orr, H. A. The population genetics of adaptation: The distribution of factors fixed during adaptive evolution. Evolution 52, 935–949 (1998).

    Article  PubMed  Google Scholar 

  127. Kimura, M. Number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics 61, 893–903 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The author thanks M. W. Hahn, M. J. Harms, D. McCandlish, M. D. Rausher, A. Stoltzfus and anonymous reviewers for helpful suggestions. This work was supported by grants from the US National Institutes of Health (HL087216) and the US National Science Foundation (MCB-1517636 and IOS-0949931).

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Glossary

Orthologous

A form of homologous relationship between genes from different species, indicating that they trace their common ancestry back to speciation events (represented by internal nodes of the species tree) rather than gene duplication events.

Fixation

The process by which the frequency of a mutant allele increases to 100% in a population, thereby supplanting the ancestral allele.

Identical-by-state

The identity of allelic gene copies that is attributable to independent mutational changes to a shared character state. The alleles in question have independent mutational origins.

Cardenolides

Plant-derived steroidal toxins that have an important role in defence against insect herbivores by inhibiting the Na+/K+-ATPase enzyme.

Identical-by-descent

The identity of allelic gene copies that is attributable to direct descent from a single-copy ancestral allele. The alleles in question have a single mutational origin.

Incomplete lineage sorting

The retention of ancestral polymorphism from one population-splitting event to the next, followed by stochastic sorting of allelic lineages among descendant species. A common cause of genealogical discordance between gene trees and species trees.

Introgressive hybridization

The incorporation of allelic variants from one species into the gene pool of another species via hybridization and repeated backcrossing.

Genealogical discordance

Topological discrepancies among the allelic genealogies (gene trees) of different loci in the same organismal phylogeny.

Homoplasy

Sharing of character states between species that is attributable to convergence, parallelism, or evolutionary reversal rather than direct inheritance from a common ancestor.

Pleiotropy

The phenomenon where the same mutation (or genetic locus) affects multiple phenotypes.

Selection coefficients

Measures of the relative fitnesses of particular genotypes in comparison with a reference genotype in a defined environment.

Transition:transversion bias

The commonly observed excess of transition mutations (exchanges between purine DNA bases [A↔G] or between pyrimidine bases [C↔T]) relative to transversion mutations (exchanges between purines and pyrimidines).

CpG bias

If the DNA nucleotide cytosine (C) is immediately 5′ to guanine (G) on the same coding strand (a so-called 'CpG' dinucleotide), and if the C is methylated to form 5′-methylcytosine, then C→T and G→A transition mutations occur at an elevated rate relative to mutations at non-CpG sites.

Adaptive walks

Adaptive evolution that occurs via the sequential fixation of beneficial mutations. The process can be conceptualized as the movement of a population through genotype space via discrete mutational steps, following a trajectory of progressively increasing fitness.

Nonsynonymous mutation

A point mutation in coding sequence that causes an amino acid replacement in the encoded protein.

Standing variation

Allelic variation that is currently segregating in a population, as opposed to allelic variants produced by de novo mutation.

Epistasis

Non-additive interactions between alleles at two or more loci, such that the phenotypic effect of the different alleles in combination cannot be predicted by the sum of the individual effects of each allele by itself.

Stabilizing selection

Selection on phenotypic variation that favours intermediate trait values.

Purifying selection

Selection that removes deleterious allelic variants.

Equilibrium frequencies

Expected frequencies of amino acids in a given sequence or at each site within a sequence. In most models of amino acid sequence evolution, the frequencies are assumed to remain constant over the time period under consideration.

Markov process

A 'memoryless' process of stochastic change with the property that future states depend only on the present state, not on the sequence of antecedent states.

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Storz, J. Causes of molecular convergence and parallelism in protein evolution. Nat Rev Genet 17, 239–250 (2016). https://doi.org/10.1038/nrg.2016.11

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