1932

Abstract

The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes.

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2019-05-06
2024-03-28
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Literature Cited

  1. 1.
    Abeydeera ND, Egli M, Cox N, Mercier K, Conde JN et al. 2016. Evoking picomolar binding in RNA by a single phosphorodithioate linkage. Nucleic Acids Res 44:8052–64
    [Google Scholar]
  2. 2.
    Aguilar-Rodríguez J, Payne JL, Wagner A 2017. A thousand empirical adaptive landscapes and their navigability. Nat. Ecol. Evol. 1:0045
    [Google Scholar]
  3. 3.
    Aita T, Husimi Y 1996. Fitness spectrum among random mutants on Mt. Fuji-type fitness landscape. J. Theor. Biol. 182:469–85
    [Google Scholar]
  4. 4.
    Aita T, Uchiyama H, Inaoka T, Nakajima M, Kokubo T, Husimi Y 2000. Analysis of a local fitness landscape with a model of the rough Mt. Fuji-type landscape: application to prolyl endopeptidase and thermolysin. Biopolymers 54:64–79
    [Google Scholar]
  5. 5.
    Araya CL, Fowler DM 2011. Deep mutational scanning: assessing protein function on a massive scale. Trends Biotechnol 29:435–42
    [Google Scholar]
  6. 6.
    Arroyo-Currás N, Dauphin-Ducharme P, Ortega G, Ploense KL, Kippin TE, Plaxco KW 2018. Subsecond-resolved molecular measurements in the living body using chronoamperometrically interrogated aptamer-based sensors. ACS Sens 3:360–66
    [Google Scholar]
  7. 7.
    Athavale SS, Spicer B, Chen IA 2014. Experimental fitness landscapes to understand the molecular evolution of RNA-based life. Curr. Opin. Chem. Biol. 22:35–39
    [Google Scholar]
  8. 8.
    Attwater J, Wochner A, Holliger P 2013. In-ice evolution of RNA polymerase ribozyme activity. Nat. Chem. 5:1011
    [Google Scholar]
  9. 9.
    Baird NJ, Inglese J, Ferré-D'Amaré AR 2015. Rapid RNA–ligand interaction analysis through high-information content conformational and stability landscapes. Nat. Commun. 6:8898–98
    [Google Scholar]
  10. 10.
    Barnett L 1998. Ruggedness and neutrality—the NKp family of fitness landscapes. Proceedings of the Sixth International Conference on Artificial Life18–27 Cambridge, MA: MIT Press
    [Google Scholar]
  11. 11.
    Blount ZD, Borland CZ, Lenski RE 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. . PNAS 105:7899–906
    [Google Scholar]
  12. 12.
    Buenrostro JD, Araya CL, Chircus LM, Layton CJ, Chang HY et al. 2014. Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes. Nat. Biotechnol. 32:562–68
    [Google Scholar]
  13. 13.
    Cervera H, Lalić J, Elena SF 2016. Effect of host species on the topography of fitness landscape for a plant RNA virus. J. Virol. 90:10160–69
    [Google Scholar]
  14. 14.
    Culbertson MC, Temburnikar KW, Sau SP, Liao JY, Bala S, Chaput JC 2016. Evaluating TNA stability under simulated physiological conditions. Bioorg. Med. Chem. Lett. 26:2418–21
    [Google Scholar]
  15. 15.
    Curtis EA, Bartel DP 2005. New catalytic structures from an existing ribozyme. Nat. Struct. Mol. Biol. 12:994–1000
    [Google Scholar]
  16. 16.
    Daher M, Widom JR, Tay W, Walter NG 2018. Soft interactions with model crowders and non-canonical interactions with cellular proteins stabilize RNA folding. J. Mol. Biol. 430:509–23
    [Google Scholar]
  17. 17.
    de Visser JA, Krug J 2014. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15:480–90Accessible review on fitness landscapes and their connection to evolutionary questions.
    [Google Scholar]
  18. 18.
    Derrida B 1981. Random-energy model: an exactly solvable model of disordered systems. Phys. Rev. B 24:2613–26
    [Google Scholar]
  19. 19.
    Dhamodharan V, Kobori S, Yokobayashi Y 2017. Large scale mutational and kinetic analysis of a self-hydrolyzing deoxyribozyme. ACS Chem. Biol. 12:2940–45
    [Google Scholar]
  20. 20.
    Domingo J, Diss G, Lehner B 2018. Pairwise and higher-order genetic interactions during the evolution of a tRNA. Nature 558:117–21Comprehensive work measuring epistatic effects over a local tRNA fitness landscape in multiple genetic backgrounds.
    [Google Scholar]
  21. 21.
    Dunn MR, Jimenez RM, Chaput JC 2017. Analysis of aptamer discovery and technology. Nat. Rev. Chem. 1:0076
    [Google Scholar]
  22. 22.
    Eschenmoser A 1999. Chemical etiology of nucleic acid structure. Science 284:2118–24
    [Google Scholar]
  23. 23.
    Ferretti L, Schmiegelt B, Weinreich D, Yamauchi A, Kobayashi Y et al. 2016. Measuring epistasis in fitness landscapes: the correlation of fitness effects of mutations. J. Theor. Biol. 396:132–43
    [Google Scholar]
  24. 24.
    Filteau M, Hamel V, Pouliot M-C, Gagnon-Arsenault I, Dubé AK, Landry CR 2015. Evolutionary rescue by compensatory mutations is constrained by genomic and environmental backgrounds. Mol. Syst. Biol. 11:832
    [Google Scholar]
  25. 25.
    Fischer NO, Tok JB-H, Tarasow TM 2008. Massively parallel interrogation of aptamer sequence, structure and function. PLOS ONE 3:e2720
    [Google Scholar]
  26. 26.
    Fontana W, Konings DA, Stadler PF, Schuster P 1993. Statistics of RNA secondary structures. Biopolymers 33:1389–404
    [Google Scholar]
  27. 27.
    Fontana W, Schuster P 1998. Continuity in evolution: on the nature of transitions. Science 280:1451–55
    [Google Scholar]
  28. 28.
    Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ et al. 2010. High-resolution mapping of protein sequence-function relationships. Nat. Methods 7:741–46
    [Google Scholar]
  29. 29.
    Fowler DM, Fields S 2014. Deep mutational scanning: a new style of protein science. Nat. Methods 11:801–7Describes DMS, an important new HTS tool for studying local protein fitness landscapes.
    [Google Scholar]
  30. 30.
    Franke J, Klozer A, de Visser JA, Krug J 2011. Evolutionary accessibility of mutational pathways. PLOS Comput. Biol. 7:e1002134
    [Google Scholar]
  31. 31.
    Frommer J, Appel B, Müller S 2015. Ribozymes that can be regulated by external stimuli. Curr. Opin. Biotechnol. 31:35–41
    [Google Scholar]
  32. 32.
    Gao M, Held C, Patra S, Arns L, Sadowski G, Winter R. 2017. Crowders and cosolvents—major contributors to the cellular milieu and efficient means to counteract environmental stresses. Chem. Phys. Chem. 18:2951–72
    [Google Scholar]
  33. 33.
    Gao M, Arns L, Winter R 2017. Modulation of the thermodynamic signatures of an RNA thermometer by osmolytes and salts. Angew. Chem. Int. Ed. 56:2302–6
    [Google Scholar]
  34. 34.
    Gavrilets S 2004. Fitness Landscapes and the Origin of Species (MPB-41) Princeton, NJ: Princeton Univ. Press
  35. 35.
    Gavrilets S 2014. Models of speciation: Where are we now?. J. Hered. 105:Suppl. 1743–55
    [Google Scholar]
  36. 36.
    Gawande BN, Rohloff JC, Carter JD, Von Carlowitz I, Zhang C et al. 2017. Selection of DNA aptamers with two modified bases. PNAS 114:2898–903
    [Google Scholar]
  37. 37.
    Geard N, Wiles J, Hallinan J, Tonkes B, Skellett B 2002. A comparison of neutral landscapes—NK, NKp and NKq. Proceedings of the 2002 Congress on Evolutionary Computation Piscataway, NJ: IEEE
    [Google Scholar]
  38. 38.
    Gillespie JH 1983. Some properties of finite populations experiencing strong selection and weak mutation. Am. Nat. 121:691–708
    [Google Scholar]
  39. 39.
    Hayashi Y, Aita T, Toyota H, Husimi Y, Urabe I, Yomo T 2006. Experimental rugged fitness landscape in protein sequence space. PLOS ONE 1:e96
    [Google Scholar]
  40. 40.
    Hayashi Y, Sakata H, Makino Y, Urabe I, Yomo T 2003. Can an arbitrary sequence evolve towards acquiring a biological function?. J. Mol. Evol. 56:162–68
    [Google Scholar]
  41. 41.
    Held DM, Greathouse ST, Agrawal A, Burke DH 2003. Evolutionary landscapes for the acquisition of new ligand recognition by RNA aptamers. J. Mol. Evol. 57:299–308
    [Google Scholar]
  42. 42.
    Hietpas RT, Bank C, Jensen JD, Bolon DNA 2013. Shifting fitness landscapes in response to altered environments. Evolution 67:3512–22
    [Google Scholar]
  43. 43.
    Hietpas RT, Jensen JD, Bolon DN 2011. Experimental illumination of a fitness landscape. PNAS 108:7896–901
    [Google Scholar]
  44. 44.
    Ho W-C, Zhang J 2018. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. Nat. Commun. 9:350
    [Google Scholar]
  45. 45.
    Humphrey W, Dalke A, Schulten K 1996. VMD: visual molecular dynamics. J. Mol. Graph. 14:33–38
    [Google Scholar]
  46. 46.
    Huynen MA 1996. Exploring phenotype space through neutral evolution. J. Mol. Evol. 43:165–69
    [Google Scholar]
  47. 47.
    Jalali-Yazdi F, Lai LH, Takahashi TT, Roberts RW 2016. High-throughput measurement of binding kinetics by mRNA display and next-generation sequencing. Angew. Chem. Int. Ed. 55:4007–10
    [Google Scholar]
  48. 48.
    Jiménez JI, Xulvi-Brunet R, Campbell GW, Turk-MacLeod R, Chen IA 2013. Comprehensive experimental fitness landscape and evolutionary network for small RNA. PNAS 110:14984–89Complete mapping of functional RNA fitness landscape through HTS and in vitro selection (421 sequences).
    [Google Scholar]
  49. 49.
    Katilius E, Flores C, Woodbury NW 2007. Exploring the sequence space of a DNA aptamer using microarrays. Nucleic Acids Res 35:7626–35
    [Google Scholar]
  50. 50.
    Kauffman S, Levin S 1987. Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128:11–45
    [Google Scholar]
  51. 51.
    Kauffman SA, Weinberger ED 1989. The NK model of rugged fitness landscapes and its application to maturation of the immune response. J. Theor. Biol. 141:211–45
    [Google Scholar]
  52. 52.
    Kimoto M, Yamashige R, Matsunaga K-i, Yokoyama S, Hirao I 2013. Generation of high-affinity DNA aptamers using an expanded genetic alphabet. Nat. Biotechnol. 31:453–57
    [Google Scholar]
  53. 53.
    Kingman JFC 1978. A simple model for the balance between selection and mutation. J. Appl. Probability 15:1–12
    [Google Scholar]
  54. 54.
    Knight CG, Platt M, Rowe W, Wedge DC, Khan F et al. 2009. Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape. Nucleic Acids Res 37:e6
    [Google Scholar]
  55. 55.
    Kobori S, Yokobayashi Y 2016. High‐throughput mutational analysis of a twister ribozyme. Angew. Chem. 55:10354–57
    [Google Scholar]
  56. 56.
    Kun Á, Szathmáry E 2015. Fitness landscapes of functional RNAs. Life 5:1497–517
    [Google Scholar]
  57. 57.
    Le DD, Shimko TC, Aditham AK, Keys AM, Longwell SA et al. 2018. Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. PNAS 15:E3702–11
    [Google Scholar]
  58. 58.
    Lee H-T, Kilburn D, Behrouzi R, Briber RM, Woodson SA 2015. Molecular crowding overcomes the destabilizing effects of mutations in a bacterial ribozyme. Nucleic Acids Res 43:1170–76
    [Google Scholar]
  59. 59.
    Li C, Qian W, Maclean CJ, Zhang J 2016. The fitness landscape of a tRNA gene. Science 352:837–40
    [Google Scholar]
  60. 60.
    Li C, Zhang J 2018. Multi-environment fitness landscapes of a tRNA gene. Nat. Ecol. Evol. 2:1025–32
    [Google Scholar]
  61. 61.
    Luksza M, Lassig M 2014. A predictive fitness model for influenza. Nature 507:57–61
    [Google Scholar]
  62. 62.
    Malyshev DA, Dhami K, Lavergne T, Chen T, Dai N et al. 2014. A semi-synthetic organism with an expanded genetic alphabet. Nature 509:385–88
    [Google Scholar]
  63. 63.
    Melnikov A, Rogov P, Wang L, Gnirke A, Mikkelsen TS 2014. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res 42:e112
    [Google Scholar]
  64. 64.
    Neidhart J, Szendro IG, Krug J 2014. Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model. Genetics 198:699–721
    [Google Scholar]
  65. 65.
    Ni S, Yao H, Wang L, Lu J, Jiang F et al. 2017. Chemical modifications of nucleic acid aptamers for therapeutic purposes. Int. J. Mol. Sci. 18:1683
    [Google Scholar]
  66. 66.
    Obolski U, Ram Y, Hadany L 2018. Key issues review: evolution on rugged adaptive landscapes. Rep. Prog. Phys. 81:012602
    [Google Scholar]
  67. 67.
    Ota N, Kurahashi R, Sano S, Takano K 2018. The direction of protein evolution is destined by the stability. Biochimie 150:100–9
    [Google Scholar]
  68. 68.
    Otwinowski J, Plotkin JB 2014. Inferring fitness landscapes by regression produces biased estimates of epistasis. PNAS 111:E2301–9
    [Google Scholar]
  69. 69.
    Paul N, Springsteen G, Joyce GF 2006. Conversion of a ribozyme to a deoxyribozyme through in vitro evolution. Chem. Biol. 13:329–38
    [Google Scholar]
  70. 70.
    Perelson AS, Macken CA 1995. Protein evolution on partially correlated landscapes. PNAS 92:9657–61
    [Google Scholar]
  71. 71.
    Petrie KL, Joyce GF 2014. Limits of neutral drift: lessons from the in vitro evolution of two ribozymes. J. Mol. Evol. 79:75–90
    [Google Scholar]
  72. 72.
    Phillips AM, Gonzalez LO, Nekongo EE, Ponomarenko AI, McHugh SM et al. 2017. Host proteostasis modulates influenza evolution. eLife 6:e28652
    [Google Scholar]
  73. 73.
    Pieken WA, Olsen DB, Benseler F, Aurup H, Eckstein F 1991. Kinetic characterization of ribonuclease-resistant 2′-modified hammerhead ribozymes. Science 253:314–17
    [Google Scholar]
  74. 74.
    Pinheiro VB, Holliger P 2014. Towards XNA nanotechnology: new materials from synthetic genetic polymers. Trends Biotechnol 32:321–28
    [Google Scholar]
  75. 75.
    Pinheiro VB, Taylor AI, Cozens C, Abramov M, Renders M et al. 2012. Synthetic genetic polymers capable of heredity and evolution. Science 336:341–44
    [Google Scholar]
  76. 76.
    Pitt JN, Ferré-D'Amaré AR 2009. Structure-guided engineering of the regioselectivity of RNA ligase ribozymes. J. Am. Chem. Soc. 131:3532–40
    [Google Scholar]
  77. 77.
    Pitt JN, Ferré-D'Amaré AR 2010. Rapid construction of empirical RNA fitness landscapes. Science 330:376–79First work utilizing HTS to obtain local fitness landscape information about a ribozyme.
    [Google Scholar]
  78. 78.
    Poelwijk FJ, Tanase-Nicola S, Kiviet DJ, Tans SJ 2011. Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes. J. Theor. Biol. 272:141–44
    [Google Scholar]
  79. 79.
    Pressman A, Moretti JE, Campbell GW, Muller UF, Chen IA 2017. Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences. Nucleic Acids Res 45:10922
    [Google Scholar]
  80. 80.
    Puchta O, Cseke B, Czaja H, Tollervey D, Sanguinetti G, Kudla G 2016. Network of epistatic interactions within a yeast snoRNA. Science 352:840–44
    [Google Scholar]
  81. 81.
    Qian W, Ma D, Xiao C, Wang Z, Zhang J 2012. The genomic landscape and evolutionary resolution of antagonistic pleiotropy in yeast. Cell Rep 2:1399–410
    [Google Scholar]
  82. 82.
    Reader JS, Joyce GF 2002. A ribozyme composed of only two different nucleotides. Nature 420:841–44
    [Google Scholar]
  83. 83.
    Rivas G, Minton AP 2016. Macromolecular crowding in vitro, in vivo, and in between. Trends Biochem. Sci. 41:970–81
    [Google Scholar]
  84. 84.
    Rode AB, Endoh T, Sugimoto N 2018. Crowding shifts the FMN recognition mechanism of riboswitch aptamer from conformational selection to induced fit. Angew. Chem. Int. Ed. 57:6868–72
    [Google Scholar]
  85. 85.
    Rogers J, Joyce GF 1999. A ribozyme that lacks cytidine. Nature 402:323–25
    [Google Scholar]
  86. 86.
    Rogers J, Joyce GF 2001. The effect of cytidine on the structure and function of an RNA ligase ribozyme. RNA 7:395–404
    [Google Scholar]
  87. 87.
    Röthlisberger P, Hollenstein M 2018. Aptamer chemistry. Adv. Drug Deliv. Rev. 134:3–21
    [Google Scholar]
  88. 88.
    Rowe W, Platt M, Wedge DC, Day PJ, Kell DB, Knowles J 2010. Analysis of a complete DNA—protein affinity landscape. J. R. Soc. Interface 7:397–408Early microarray work measuring the protein binding landscape for all 10-mer DNA variants.
    [Google Scholar]
  89. 89.
    Saha R, Pohorille A, Chen IA 2015. Molecular crowding and early evolution. Orig. Life Evol. Biosph. 44:319–24
    [Google Scholar]
  90. 90.
    Saha R, Verbanic S, Chen IA 2018. Lipid vesicles chaperone an encapsulated RNA aptamer. Nat. Commun. 9:2313
    [Google Scholar]
  91. 91.
    Sanchez-Luque FJ, Stich M, Manrubia S, Briones C, Berzal-Herranz A 2014. Efficient HIV-1 inhibition by a 16 nt-long RNA aptamer designed by combining in vitro selection and in silico optimisation strategies. Sci. Rep. 4:6242
    [Google Scholar]
  92. 92.
    Sarkisyan KS, Bolotin DA, Meer MV, Usmanova DR, Mishin AS et al. 2016. Local fitness landscape of the green fluorescent protein. Nature 533:397–401
    [Google Scholar]
  93. 93.
    Schuabb C, Kumar N, Pataraia S, Marx D, Winter R 2017. Pressure modulates the self-cleavage step of the hairpin ribozyme. Nat. Commun. 8:14661
    [Google Scholar]
  94. 94.
    Schultes E, Bartel DP 2000. One sequence, two ribozymes: implications for the emergence of new ribozyme folds. Science 289:448–452
    [Google Scholar]
  95. 95.
    Schuster P, Fontana W, Stadler PF, Hofacker IL 1994. From sequences to shapes and back: a case study in RNA secondary structures. Proc. Biol. Sci. 255:279–84In silico prediction of a neutral network for RNA secondary structure.
    [Google Scholar]
  96. 96.
    Sefah K, Yang Z, Bradley KM, Hoshika S, Jiménez E et al. 2014. In vitro selection with artificial expanded genetic information systems. PNAS 111:1449–54
    [Google Scholar]
  97. 97.
    Silverman SK 2016. Catalytic DNA: scope, applications, and biochemistry of deoxyribozymes. Trends Biochem. Sci. 41:595–609
    [Google Scholar]
  98. 98.
    Smith JM 1970. Natural selection and the concept of a protein space. Nature 225:563–64Classic paper setting forth the modern understanding of a fitness landscape.
    [Google Scholar]
  99. 99.
    Stadler PF, Happel R 1999. Random field models for fitness landscapes. J. Math. Biol. 38:435–78
    [Google Scholar]
  100. 100.
    Starita LM, Fields S 2015. Deep mutational scanning: a highly parallel method to measure the effects of mutation on protein function. Cold Spring Harb. Protoc. 2015:711–14
    [Google Scholar]
  101. 101.
    Szendro IG, Schenk MF, Franke J, Krug J, de Visser JAGM 2013. Quantitative analyses of empirical fitness landscapes. J. Stat. Mech. Theory Exp. 2013:P01005
    [Google Scholar]
  102. 102.
    Tacker M, Fontana W, Stadler P, Schuster P 1994. Statistics of RNA melting kinetics. Eur. Biophys. J. 23:29–38
    [Google Scholar]
  103. 103.
    Taylor AI, Pinheiro VB, Smola MJ, Morgunov AS, Peak-Chew S et al. 2015. Catalysts from synthetic genetic polymers. Nature 69:208–15
    [Google Scholar]
  104. 104.
    Thirunavukarasu D, Chen T, Liu Z, Hongdilokkul N, Romesberg FE 2017. Selection of 2′-fluoro-modified aptamers with optimized properties. J. Am. Chem. Soc. 139:2892–95
    [Google Scholar]
  105. 105.
    Tolle F, Brändle GM, Matzner D, Mayer G 2015. A versatile approach towards nucleobase-modified aptamers. Angew. Chem. Int. Ed. 54:10971–74
    [Google Scholar]
  106. 106.
    Travascio P, Bennet AJ, Wang DY, Sen D 1999. A ribozyme and a catalytic DNA with peroxidase activity: active sites versus cofactor-binding sites. Chem. Biol. 6:779–87
    [Google Scholar]
  107. 107.
    Vaidya N, Manapat ML, Chen IA, Xulvi-Brunet R, Hayden EJ, Lehman N 2012. Spontaneous network formation among cooperative RNA replicators. Nature 491:72
    [Google Scholar]
  108. 108.
    Volk DE, Yang X, Fennewald SM, King DJ, Bassett SE et al. 2002. Solution structure and design of dithiophosphate backbone aptamers targeting transcription factor NF-κB. Bioorg. Chem. 30:396–419
    [Google Scholar]
  109. 109.
    Walsh R, DeRosa MC 2009. Retention of function in the DNA homolog of the RNA dopamine aptamer. Biochem. Biophys. Res. Commun. 388:732–35
    [Google Scholar]
  110. 110.
    Weinreich DM, Lan Y, Jaffe J, Heckendorn RB 2018. The influence of higher-order epistasis on biological fitness landscape topography. J. Stat. Phys. 172:208–25
    [Google Scholar]
  111. 111.
    Weinreich DM, Watson RA, Chao L 2005. Perspective: sign epistasis and genetic constraint on evolutionary trajectories. Evolution 59:1165–74Explains the importance of sign and reciprocal sign epistasis for landscape topography and viable evolutionary pathways.
    [Google Scholar]
  112. 112.
    Whitehead TA, Chevalier A, Song Y, Dreyfus C, Fleishman SJ et al. 2012. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat. Biotechnol. 30:543–48
    [Google Scholar]
  113. 113.
    Wright S 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the Sixth International Congress of Genetics 1356–66 Menasha, WI: Brooklyn Botan. Gard.
    [Google Scholar]
  114. 114.
    Wu NC, Dai L, Olson CA, Lloyd-Smith JO, Sun R 2016. Adaptation in protein fitness landscapes is facilitated by indirect paths. eLife 5:e16965Demonstrates the importance of sampling over broad areas when drawing inferences about the fitness landscape.
    [Google Scholar]
  115. 115.
    Yu H, Zhang S, Chaput JC 2012. Darwinian evolution of an alternative genetic system provides support for TNA as an RNA progenitor. Nat. Chem. 4:183–87
    [Google Scholar]
  116. 116.
    Zhang L, Yang Z, Sefah K, Bradley KM, Hoshika S et al. 2015. Evolution of functional six-nucleotide DNA. J. Am. Chem. Soc. 137:6734–37
    [Google Scholar]
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