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In vivo hypermutation and continuous evolution

Nature Reviews Methods Primers volume 2, Article number: 36 (2022) Cite this article

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Abstract

Directed evolution has revolutionized biomolecular engineering by applying cycles of mutation, amplification and selection to genes of interest (GOIs). However, classical directed evolution methods that rely on manually staged evolutionary cycles constrain the scale and depth of the evolutionary search that is possible. We describe genetic systems that achieve cycles of rapid mutation, amplification and selection fully inside living cells, enabling the continuous evolution of GOIs as cells grow. These systems advance the scale, evolutionary search depth, ease and overall power of directed evolution and access important new areas of protein evolution and engineering.

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Fig. 1: Continuous evolution with cellular systems.
Fig. 2: Generalized steps for carrying out a continuous evolution experiment.
Fig. 3: Mutagenesis mechanisms for in vivo hypermutation systems.
Fig. 4: Application of continuous evolution for studying drug resistance.
Fig. 5: Application of continuous evolution for enzyme engineering.
Fig. 6: Continuous evolution applications employing FACS-based screening.

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References

  1. Arnold, F. H. Design by directed evolution. Acc. Chem. Res. 31, 125–131 (1998).

    Google Scholar 

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

    Google Scholar 

  3. Drake, J. W., Charlesworth, B., Charlesworth, D. & Crow, J. F. Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998).

    Google Scholar 

  4. Chen, K. & Arnold, F. H. Engineering new catalytic activities in enzymes. Nat. Catal. 3, 203–213 (2020).

    Google Scholar 

  5. Esvelt, K. M., Carlson, J. C. & Liu, D. R. A system for the continuous directed evolution of biomolecules. Nature 472, 499–503 (2011).

    ADS  Google Scholar 

  6. Miller, S. M., Wang, T. & Liu, D. R. Phage-assisted continuous and non-continuous evolution. Nat. Protoc. 15, 4101–4127 (2020).

    Google Scholar 

  7. Morrison, M. S., Podracky, C. J. & Liu, D. R. The developing toolkit of continuous directed evolution. Nat. Chem. Biol. 16, 610–619 (2020).

    Google Scholar 

  8. Hendel, S. J. & Shoulders, M. D. Directed evolution in mammalian cells. Nat. Methods. 18, 346–357 (2021).

    Google Scholar 

  9. Fabret, C. et al. Efficient gene targeted random mutagenesis in genetically stable Escherichia coli strains. Nucleic Acids Res. 28, 95 (2000).

    ADS  Google Scholar 

  10. Camps, M., Naukkarinen, J., Johnson, B. P. & Loeb, L. A. Targeted gene evolution in Escherichia coli using a highly error-prone DNA polymerase I. Proc. Natl Acad. Sci. USA 100, 9727–9732 (2003).

    ADS  Google Scholar 

  11. Finney-Manchester, S. P. & Maheshri, N. Harnessing mutagenic homologous recombination for targeted mutagenesis in vivo by TaGTEAM. Nucleic Acids Res. 41, 1–10 (2013).

    Google Scholar 

  12. Crook, N. et al. In vivo continuous evolution of genes and pathways in yeast. Nat. Commun. 7, 13051 (2016).

    ADS  Google Scholar 

  13. Ravikumar, A., Arrieta, A. & Liu, C. C. An orthogonal DNA replication system in yeast. Nat. Chem. Biol. 10, 175–177 (2014). This work establishes an orthogonal DNA replication system in yeast that enables the elevation of mutation rates on an orthogonal plasmid replicated by a cognate orthogonal error-prone DNAP.

    Google Scholar 

  14. Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods. 13, 1036–1042 (2016). This work is an early demonstration that attachment of mutagenic machinery, in this case a cytidine deaminase, to dCas9 is an effective strategy for targeting hypermutation to desired loci in mammalian cells.

    Google Scholar 

  15. Ma, Y. et al. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells. Nat. Methods. 13, 1029–1035 (2016).

    Google Scholar 

  16. Moore, C. L., Papa, L. J. & Shoulders, M. D. A processive protein chimera introduces mutations across defined DNA regions in vivo. J. Am. Chem. Soc. 140, 11560–11564 (2018). This work establishes the MutaT7 strategy involving the fusion of a DNA-damaging cytidine deaminase to a processive RNA polymerase to achieve in vivo targeted hypermutation of multi-kilobyte DNA sequences, thereby enabling continuous evolution of GOIs inside E. coli.

    Google Scholar 

  17. Halperin, S. O. et al. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window. Nature. 560, 248–252 (2018). This work demonstrates that fusion of an error-prone DNAP to nCas9 achieves hypermutation at desired gRNA-targeted loci in E. coli to support continuous in vivo diversification and evolution of GOIs.

    ADS  Google Scholar 

  18. Ravikumar, A., Arzumanyan, G. A., Obadi, M. K. A., Javanpour, A. A. & Liu, C. C. Scalable, continuous evolution of genes at mutation rates above genomic error thresholds. Cell. 175, 1946–1957.e13 (2018). This work establishes a highly error-prone orthogonal DNA replication system that durably hypermutates an orthogonal plasmid at mutation rates 100,000-fold higher than the genome in yeast, thereby supporting the continuous evolution of GOIs for extended periods of time and at scale, as demonstrated through the replicate evolution of drug resistance by a malarial drug target.

    Google Scholar 

  19. Yi, X., Khey, J., Kazlauskas, R. J. & Travisano, M. Plasmid hypermutation using a targeted artificial DNA replisome. Sci. Adv. 7, eabg871 (2021).

    Google Scholar 

  20. Yi, X., Kazlauskas, R. & Travisano, M. Evolutionary innovation using EDGE, a system for localized elevated mutagenesis. PLoS ONE 15, 1–18 (2020).

    Google Scholar 

  21. Jensen, E. D. et al. A synthetic RNA-mediated evolution system in yeast. Nucleic Acids Res. 49, 1–12 (2021).

    Google Scholar 

  22. Chen, H. et al. Efficient, continuous mutagenesis in human cells using a pseudo-random DNA editor. Nat. Biotechnol. 38, 165–168 (2020). This work presents an extension of the MutaT7 strategy to mammalian cells, enabling the continuous targeted hypermutation and evolution of GOIs inside human cells.

    Google Scholar 

  23. Álvarez, B., Mencía, M., de Lorenzo, V. & Fernández, L. Á. In vivo diversification of target genomic sites using processive base deaminase fusions blocked by dCas9. Nat. Commun. 11, 6436 (2020). This work expands the MutaT7 technology through the fusion of new base deaminases to T7RNAP (thereby achieving targeted hypermutation with expanded mutational parameters in E. coli) and the addition of dCas9 to terminate polymerization by T7RNAP (thereby providing more control over the window of hypermutation).

    ADS  Google Scholar 

  24. Park, H. & Kim, S. Gene-specific mutagenesis enables rapid continuous evolution of enzymes in vivo. Nucleic Acids Res. 49, e32–e32 (2021). This work presents an expansion of MutaT7 technology to achieve exceptionally high rates of hypermutation in E. coli.

    Google Scholar 

  25. Cravens, A., Jamil, O. K., Kong, D., Sockolosky, J. T. & Smolke, C. D. Polymerase-guided base editing enables in vivo mutagenesis and rapid protein engineering. Nat. Commun. 12, 1579 (2021). This work extends the MutaT7 strategy to yeast, enabling the continuous targeted hypermutation and in vivo continuous evolution of GOIs in S. cerevisiae.

    ADS  Google Scholar 

  26. Butt, H., Ramirez, J. L. M. & Mahfouz, M. Synthetic evolution of herbicide resistance using a T7 RNAP-based random DNA base editor. Preprint at bioRxiv https://doi.org/10.1101/2021.11.30.470689 (2021).

    Article  Google Scholar 

  27. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 337, 816–821 (2012).

    ADS  Google Scholar 

  28. Tou, C. J., Schaffer, D. V. & Dueber, J. E. Targeted diversification in the S. cerevisiae genome with CRISPR-guided DNA polymerase i. ACS Synth. Biol. 9, 1911–1916 (2020).

    Google Scholar 

  29. Khanal, A., McLoughlin, S. Y., Kershner, J. P. & Copley, S. D. Differential effects of a mutation on the normal and promiscuous activities of orthologs: implications for natural and directed evolution. Mol. Biol. Evol. 32, 100–108 (2015).

    Google Scholar 

  30. Zheng, J., Payne, J. L. & Wagner, A. Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science 365, 347–353 (2019).

    ADS  Google Scholar 

  31. Gupta, R. D. & Tawfik, D. S. Directed enzyme evolution via small and effective neutral drift libraries. Nat. Methods. 5, 939–942 (2008).

    Google Scholar 

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

    Google Scholar 

  33. Baier, F. et al. Cryptic genetic variation shapes the adaptive evolutionary potential of enzymes. eLife 8, 1–20 (2019).

    Google Scholar 

  34. Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature. 500, 571–574 (2013).

    ADS  Google Scholar 

  35. Eigen, M., McCaskill, J. & Schuster, P. Molecular quasi-species. J. Phys. Chem. 92, 6881–6891 (1988).

    Google Scholar 

  36. Rix, G. & Liu, C. C. Systems for in vivo hypermutation: a quest for scale and depth in directed evolution. Curr. Opin. Chem. Biol. 64, 20–26 (2021). This work outlines the value of in vivo continuous evolution systems in accessing new categories of directed evolution experiments characterized by depth and scale.

    Google Scholar 

  37. Gunge, N. & Sakaguchi, K. Intergeneric transfer of deoxyribonucleic acid killer plasmids, pGKl1 and pGKl2, from Kluyveromyces lactis into Saccharomyces cerevisiae by cell fusion. J. Bacteriol. 147, 155–160 (1981).

    Google Scholar 

  38. Arzumanyan, G. A., Gabriel, K. N., Ravikumar, A., Javanpour, A. A. & Liu, C. C. Mutually orthogonal DNA replication systems in vivo. ACS Synth. Biol. 7, 1722–1729 (2018).

    Google Scholar 

  39. Zhong, Z., Ravikumar, A. & Liu, C. C. Tunable expression systems for orthogonal DNA replication. ACS Synth. Biol. 7, 2930–2934 (2018).

    Google Scholar 

  40. Kämper, J., Esser, K., Gunge, N. & Meinhardt, F. Heterologous gene expression on the linear DNA killer plasmid from Kluyveromyces lactis. Curr. Genet. 19, 109–118 (1991).

    Google Scholar 

  41. Javanpour, A. A. & Liu, C. C. Genetic compatibility and extensibility of orthogonal replication. ACS Synth. Biol. 8, 1249–1256 (2019).

    Google Scholar 

  42. Chamberlin, M., Mcgrath, J. & Waskell, L. New RNA polymerase from Escherichia coli infected with bacteriophage T7. Nature. 228, 227–231 (1970).

    ADS  Google Scholar 

  43. Tabor, S. & Richardson, C. C. A bacteriophage T7 RNA polymerase/promoter system for controlled exclusive expression of specific genes. Proc. Natl Acad. Sci. USA 82, 1074–1078 (1985).

    ADS  Google Scholar 

  44. McAllister, W. T., Morris, C., Rosenberg, A. H. & Studier, F. W. Utilization of bacteriophage T7 late promoters in recombinant plasmids during infection. J. Mol. Biol. 153, 527–544 (1981).

    Google Scholar 

  45. Thiel, V., Herold, J., Schelle, B. & Siddell, S. G. Infectious RNA transcribed in vitro from a cDNA copy of the human coronavirus genome cloned in vaccinia virus. J. Gen. Virol. 82, 1273–1281 (2001).

    Google Scholar 

  46. Steitz, T. A. The structural changes of T7 RNA polymerase from transcription initiation to elongation. Curr. Opin. Struct. Biol. 19, 683–690 (2009).

    Google Scholar 

  47. Conticello, S. G. The AID/APOBEC family of nucleic acid mutators. Genome Biol. 9, 229 (2008).

    Google Scholar 

  48. Gerber, A. P. & Keller, W. An adenosine deaminase that generates inosine at the wobble position of tRNAs. Science 286, 1146–1149 (1999).

    Google Scholar 

  49. Harris, R. S., Petersen-Mahrt, S. K. & Neuberger, M. S. RNA editing enzyme APOBEC1 and some of its homologs can act as DNA mutators. Mol. Cell. 10, 1247–1253 (2002).

    Google Scholar 

  50. Navaratnam, N. & Sarwar, R. An overview of cytidine deaminases. Int. J. Hematol. 83, 195–200 (2006).

    Google Scholar 

  51. Cacciamani, T. et al. Purification of human cytidine deaminase: molecular and enzymatic characterization and inhibition by synthetic pyrimidine analogs. Arch. Biochem. Biophys. 290, 285–292 (1991).

    Google Scholar 

  52. Chung, S. J., Fromme, J. C. & Verdine, G. L. Structure of human cytidine deaminase bound to a potent inhibitor. J. Med. Chem. 48, 658–660 (2005).

    Google Scholar 

  53. Lada, A. G. et al. Mutator effects and mutation signatures of editing deaminases produced in bacteria and yeast. Biochem 76, 131–146 (2011).

    Google Scholar 

  54. Vik, E. S. et al. Endonuclease V cleaves at inosines in RNA. Nat. Commun. 4, 2271 (2013).

    ADS  Google Scholar 

  55. Krokan, H. E., Drabløs, F. & Slupphaug, G. Uracil in DNA — occurrence, consequences and repair. Oncogene 21, 8935–8948 (2002).

    Google Scholar 

  56. Alseth, I., Dalhus, B. & Bjørås, M. Inosine in DNA and RNA. Curr. Opin. Genet. Dev. 26, 116–123 (2014).

    Google Scholar 

  57. Hirano, K. I., Min, J., Funahashi, T. & Davidson, N. O. Cloning and characterization of the rat apobec-1 gene: a comparative analysis of gene structure and promoter usage in rat and mouse. J. Lipid Res. 38, 1103–1119 (1997).

    Google Scholar 

  58. MacGinnitie, A. J., Anant, S. & Davidson, N. O. Mutagenesis of apobec-1, the catalytic subunit of the mammalian apolipoprotein B mRNA editing enzyme, reveals distinct domains that mediate cytosine nucleoside deaminase, RNA binding, and RNA editing activity. J. Biol. Chem. 270, 14768–14775 (1995).

    Google Scholar 

  59. Scott, J., Navaratnam, N., Bhattacharya, S. & Morrison, J. R. The apolipoprotein B messenger RNA editing enzyme. Curr. Opin. Lipidol. 5, 87–93 (1994).

    Google Scholar 

  60. Arakawa, H., HauschiLd, J. & Buerstedde, J. M. Requirement of the activation-induced deaminase (AID) gene for immunoglobulin gene conversion. Science 295, 1301–1306 (2002).

    ADS  Google Scholar 

  61. Muramatsu, M. et al. Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme. Cell. 102, 553–563 (2000).

    Google Scholar 

  62. Rogozin, I. B. et al. Evolution and diversification of lamprey antigen receptors: evidence for involvement of an AID–APOBEC family cytosine deaminase. Nat. Immunol. 8, 647–656 (2007).

    Google Scholar 

  63. Kim, J. et al. Structural and kinetic characterization of Escherichia coli TadA, the wobble-specific tRNA deaminase. Biochemistry. 45, 6407–6416 (2006).

    Google Scholar 

  64. Gaudelli, N. M. et al. Programmable base editing of T to G C in genomic DNA without DNA cleavage. Nature. 551, 464–471 (2017).

    ADS  Google Scholar 

  65. Richter, M. F. et al. Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity. Nat. Biotechnol. 38, 883–891 (2020).

    Google Scholar 

  66. Martínez-Salas, E. Internal ribosome entry site biology and its use in expression vectors. Curr. Opin. Biotechnol. 10, 458–464 (1999).

    Google Scholar 

  67. Wang, Z. & Mosbaugh, D. W. Uracil-DNA glycosylase inhibitor of bacteriophage PBS2: cloning and effects of expression of the inhibitor gene in Escherichia coli. J. Bacteriol. 170, 1082–1091 (1988).

    Google Scholar 

  68. Wang, Z. & Mosbaugh, D. W. Uracil-DNA glycosylase inhibitor gene of bacteriophage PBS2 encodes a binding protein specific for uracil-DNA glycosylase. J. Biol. Chem. 264, 1163–1171 (1989).

    Google Scholar 

  69. Karran, P., Cone, R. & Friedberg, E. C. Specificity of the bacteriophage PBS2 induced inhibitor of uracil-DNA glycosylase. Biochemistry. 20, 6092–6096 (1981).

    Google Scholar 

  70. Bennett, S. E. & Mosbaugh, D. W. Characterization of the Escherichia coli uracil-DNA glycosylase·inhibitor protein complex. J. Biol. Chem. 267, 22512–22521 (1992).

    Google Scholar 

  71. Bennett, S. E., Schimerlik, M. I. & Mosbaugh, D. W. Kinetics of the uracil-DNA glycosylase/inhibitor protein association. Ung interaction with Ugi, nucleic acids, and uracil compounds. J. Biol. Chem. 268, 26879–26885 (1993).

    Google Scholar 

  72. Wang, Y. et al. A novel strategy to engineer DNA polymerases for enhanced processivity and improved performance in vitro. Nucleic Acids Res. 32, 1197–1207 (2004).

    Google Scholar 

  73. Tizei, P. A. G., Csibra, E., Torres, L. & Pinheiro, V. B. Selection platforms for directed evolution in synthetic biology. Biochem. Soc. Trans. 44, 1165–1175 (2016).

    Google Scholar 

  74. Wang, Y. et al. Directed evolution: methodologies and applications. Chem. Rev. 121, 12384–12444 (2021).

    Google Scholar 

  75. 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).

    ADS  Google Scholar 

  76. Wang, L., Brock, A., Herberich, B. & Schultz, P. G. Expanding the genetic code of Escherichia coli. Science. 292, 498–500 (2001).

    ADS  Google Scholar 

  77. Carlson, J. C., Badran, A. H., Guggiana-Nilo, D. A. & Liu, D. R. Negative selection and stringency modulation in phage-assisted continuous evolution. Nat. Chem. Biol. 10, 216–222 (2014).

    Google Scholar 

  78. Blum, T. R. et al. Phage-assisted evolution of botulinum neurotoxin proteases with reprogrammed specificity. Science. 371, 803–810 (2021).

    ADS  Google Scholar 

  79. Szendro, I. G., Franke, J., De Visser, J. A. G. M. & Krug, J. Predictability of evolution depends nonmonotonically on population size. Proc. Natl Acad. Sci. USA 110, 571–576 (2013).

    ADS  Google Scholar 

  80. Salverda, M. L. M., Koomen, J., Koopmanschap, B., Zwart, M. P. & de Visser, J. A. G. M. Adaptive benefits from small mutation supplies in an antibiotic resistance enzyme. Proc. Natl Acad. Sci. USA 114, 12773–12778 (2017).

    Google Scholar 

  81. Steinberg, B. & Ostermeier, M. Environmental changes bridge evolutionary valleys. Sci. Adv. 2, e1500921 (2016).

    ADS  Google Scholar 

  82. Wong, B. G., Mancuso, C. P., Kiriakov, S., Bashor, C. J. & Khalil, A. S. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat. Biotechnol. 36, 614–623 (2018).

    Google Scholar 

  83. Zhong, Z. et al. Automated continuous evolution of proteins in vivo. ACS Synth. Biol. 9, 1270–1276 (2020).

    Google Scholar 

  84. DeBenedictis, E. A. et al. Systematic molecular evolution enables robust biomolecule discovery. Nat. Methods. 19, 55–64 (2022).

    Google Scholar 

  85. Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).

    ADS  Google Scholar 

  86. Stiffler, M. A. et al. Protein structure from experimental evolution. Cell Syst. 10, 15–24.e5 (2020).

    Google Scholar 

  87. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    ADS  Google Scholar 

  88. Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods. 16, 687–694 (2019).

    Google Scholar 

  89. Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl Acad. Sci. USA 116, 8852–8858 (2019).

    Google Scholar 

  90. Carr, I. M. et al. Inferring relative proportions of DNA variants from sequencing electropherograms. Bioinformatics 25, 3244–3250 (2009).

    Google Scholar 

  91. Shen, M. W., Zhao, K. T. & Liu, D. R. Reconstruction of evolving gene variants and fitness from short sequencing reads. Nat. Chem. Biol. 17, 1188–1198 (2021).

    Google Scholar 

  92. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Google Scholar 

  93. Amarasinghe, S. L. et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 21, 1–16 (2020).

    Google Scholar 

  94. Ravi, R. K., Walton, K. & Khosroheidari, M. in Disease Gene Identification: Methods and Protocols (ed. DiStefano, J. K.) 223–232 (Springer, 2018).

  95. Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR. Genomics Bioinforma. 3, 1–9 (2021).

    Google Scholar 

  96. Wang, Y., Zhao, Y., Bollas, A., Wang, Y. & Au, K. F. Nanopore sequencing technology, bioinformatics and applications. Nat. Biotechnol. 39, 1348–1365 (2021).

    Google Scholar 

  97. Karst, S. M. et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat. Methods. 18, 165–169 (2021).

    Google Scholar 

  98. Wenger, A. M. et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat. Biotechnol. 37, 1155–1162 (2019).

    Google Scholar 

  99. Zurek, P. J., Knyphausen, P., Neufeld, K., Pushpanath, A. & Hollfelder, F. UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution. Nat. Commun. 11, 6023 (2020).

    ADS  Google Scholar 

  100. Wilson, B. D., Eisenstein, M. & Soh, H. T. High-fidelity nanopore sequencing of ultra-short DNA targets. Anal. Chem. 91, 6783–6789 (2019).

    Google Scholar 

  101. Murray, K. D. & Borevitz, J. O. Axe: rapid, competitive sequence read demultiplexing using a trie. Bioinformatics 34, 3924–3925 (2018).

    Google Scholar 

  102. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Google Scholar 

  103. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Google Scholar 

  104. Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods. Mol. Biol. 1151, 165–188 (2014).

    Google Scholar 

  105. Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods. 11, 801–807 (2014).

    Google Scholar 

  106. Sirawaraporn, W., Sathitkul, T., Sirawaraporn, R., Yuthavong, Y. & Santi, D. V. Antifolate-resistant mutants of Plasmodium falciparum dihydrofolate reductase. Biochemistry 94, 1124–1129 (1997).

    Google Scholar 

  107. Hankins, E. G., Warhurst, D. C. & Sibley, C. H. Novel alleles of the Plasmodium falciparum dhfr highly resistant to pyrimethamine and chlorcycloguanil, but not WR99210. Mol. Biochem. Parasitol. 117, 91–102 (2001).

    Google Scholar 

  108. Long, M. et al. Directed evolution of ornithine cyclodeaminase using an EvolvR-based growth-coupling strategy for efficient biosynthesis of l-proline. ACS Synth. Biol. 9, 1855–1863 (2020).

    Google Scholar 

  109. García-García, J. D. et al. Potential for applying continuous directed evolution to plant enzymes: an exploratory study. Life 10, 1–16 (2020).

    Google Scholar 

  110. García-García, J. D. et al. Using continuous directed evolution to improve enzymes for plant applications. Plant. Physiol. 188, 971–983 (2022).

    Google Scholar 

  111. Chatterjee, A. et al. Saccharomyces cerevisiae THI4p is a suicide thiamine thiazole synthase. Nature 478, 542–546 (2011).

    ADS  Google Scholar 

  112. Joshi, J. et al. Structure and function of aerotolerant, multiple-turnover THI4 thiazole synthases. Biochem. J. 478, 3265–3279 (2021).

    Google Scholar 

  113. Rix, G. et al. Scalable continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities. Nat. Commun. 11, 5644 (2020). This work demonstrates the use of OrthoRep to evolve in a scalable manner a large collection of highly diverse orthologues of an enzyme (TrpB), which was mined for promiscuous activities leading to the biosynthesis of valuable chemicals.

    ADS  Google Scholar 

  114. Dunn, M. F. Allosteric regulation of substrate channeling and catalysis in the tryptophan synthase bienzyme complex. Arch. Biochem. Biophys. 519, 154–166 (2012).

    Google Scholar 

  115. Buller, A. R. et al. Directed evolution of the tryptophan synthase β-subunit for stand-alone function recapitulates allosteric activation. Proc. Natl Acad. Sci. USA 112, 14599–14604 (2015).

    ADS  Google Scholar 

  116. Watkins-Dulaney, E., Straathof, S. & Arnold, F. Tryptophan synthase: biocatalyst extraordinaire. ChemBioChem 22, 5–16 (2021).

    Google Scholar 

  117. Romney, D. K., Murciano-Calles, J., Wehrmüller, J. E. & Arnold, F. H. Unlocking reactivity of TrpB: a general biocatalytic platform for synthesis of tryptophan analogues. J. Am. Chem. Soc. 139, 10769–10776 (2017).

    Google Scholar 

  118. Boville, C. E., Romney, D. K., Almhjell, P. J., Sieben, M. & Arnold, F. H. Improved synthesis of 4-cyanotryptophan and other tryptophan analogues in aqueous solvent using variants of TrpB from Thermotoga maritima. J. Org. Chem. 83, 7447–7452 (2018).

    Google Scholar 

  119. Javanpour, A. A. & Liu, C. C. Evolving small-molecule biosensors with improved performance and reprogrammed ligand preference using OrthoRep. ACS Synth. Biol. 10, 2705–2714 (2021).

    Google Scholar 

  120. Jensen, E. D. et al. Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast. Microb. Biotechnol. 14, 2617–2626 (2021).

    Google Scholar 

  121. Wellner, A. et al. Rapid generation of potent antibodies by autonomous hypermutation in yeast. Nat. Chem. Biol. 17, 1057–1064 (2021). This work demonstrates the use of OrthoRep to drive the rapid evolution of custom antibodies displayed on the surface of yeast cells, including nanomolar-affinity nanobodies that bind and neutralize SARS-CoV-2.

    Google Scholar 

  122. Pezo, V. et al. Noncanonical DNA polymerization by aminoadenine-based siphoviruses. Science 372, 520–524 (2021).

    ADS  Google Scholar 

  123. Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019).

    ADS  Google Scholar 

  124. Ling, X. et al. Improving the efficiency of precise genome editing with site-specific Cas9–oligonucleotide conjugates. Sci. Adv. 6, 1–9 (2020).

    ADS  Google Scholar 

  125. Wang, C. et al. Microbial single-strand annealing proteins enable CRISPR gene-editing tools with improved knock-in efficiencies and reduced off-target effects. Nucleic Acids Res. 49, 1–16 (2021).

    Google Scholar 

  126. Stevens, A. J. et al. Design of a split intein with exceptional protein splicing activity. J. Am. Chem. Soc. 138, 2162–2165 (2016).

    Google Scholar 

  127. Mills, D. R., Peterson, R. L. & Spiegelman, S. An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule. Proc. Natl Acad. Sci. USA 58, 217–224 (1967).

    ADS  Google Scholar 

  128. Beaudry, A. A. & Joyce, G. F. Directed evolution of an RNA enzyme. Science 257, 635–641 (1992).

    ADS  Google Scholar 

  129. Leman, J. K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods. 17, 665–680 (2020).

    MathSciNet  Google Scholar 

  130. Stoltenburg, R., Reinemann, C. & Strehlitz, B. SELEX — a (r)evolutionary method to generate high-affinity nucleic acid ligands. Biomol. Eng. 24, 381–403 (2007).

    Google Scholar 

  131. Torrisi, M., Pollastri, G. & Le, Q. Deep learning methods in protein structure prediction. Comput. Struct. Biotechnol. J. 18, 1301–1310 (2020).

    Google Scholar 

  132. Rollins, N. J. et al. Inferring protein 3D structure from deep mutation scans. Nat. Genet. 51, 1170–1176 (2019).

    Google Scholar 

  133. Langan, R. A. et al. De novo design of bioactive protein switches. Nature 572, 205–210 (2019).

    ADS  Google Scholar 

  134. Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018).

    ADS  Google Scholar 

  135. Basanta, B. et al. An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proc. Natl Acad. Sci. USA 117, 22135–22145 (2020).

    Google Scholar 

  136. Anishchenko, I. et al. De novo protein design by deep network hallucination. Nature 600, 547–552 (2021).

    ADS  Google Scholar 

  137. Hadadi, N., MohammadiPeyhani, H., Miskovic, L., Seijo, M. & Hatzimanikatis, V. Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc. Natl Acad. Sci. USA 116, 7298–7307 (2019).

    Google Scholar 

  138. Gumulya, Y. et al. Engineering highly functional thermostable proteins using ancestral sequence reconstruction. Nat. Catal. 1, 878–888 (2018).

    Google Scholar 

  139. Xie, V. C., Pu, J., Metzger, B. P. H., Thornton, J. W. & Dickinson, B. C. Contingency and chance erase necessity in the experimental evolution of ancestral proteins. eLife 10, 1–87 (2021).

    Google Scholar 

  140. Kaltenbach, M. et al. Evolution of chalcone isomerase from a noncatalytic ancestor article. Nat. Chem. Biol. 14, 548–555 (2018).

    Google Scholar 

  141. Biebricher, C. K. & Eigen, M. The error threshold. Virus Res. 107, 117–127 (2005).

    Google Scholar 

  142. Pu, J., Zinkus-Boltz, J. & Dickinson, B. C. Evolution of a split RNA polymerase as a versatile biosensor platform. Nat. Chem. Biol. 13, 432–438 (2017).

    Google Scholar 

  143. Packer, M. S., Rees, H. A. & Liu, D. R. Phage-assisted continuous evolution of proteases with altered substrate specificity. Nat. Commun. 8, 956 (2017).

    ADS  Google Scholar 

  144. Badran, A. H. et al. Continuous evolution of Bacillus thuringiensis toxins overcomes insect resistance. Nature 533, 58–63 (2016).

    ADS  Google Scholar 

  145. Inamoto, I., Sheoran, I., Popa, S. C., Hussain, M. & Shin, J. A. Combining rational design and continuous evolution on minimalist proteins that target the E-box DNA site. ACS Chem. Biol. 16, 35–44 (2021).

    Google Scholar 

  146. Wang, T., Badran, A. H., Huang, T. P. & Liu, D. R. Continuous directed evolution of proteins with improved soluble expression. Nat. Chem. Biol. 14, 972–980 (2018).

    Google Scholar 

  147. Thuronyi, B. W. et al. Continuous evolution of base editors with expanded target compatibility and improved activity. Nat. Biotechnol. 37, 1070–1079 (2019).

    Google Scholar 

  148. Berman, C. M. et al. An adaptable platform for directed evolution in human cells. J. Am. Chem. Soc. 140, 18093–18103 (2018).

    Google Scholar 

  149. English, J. G. et al. VEGAS as a platform for facile directed evolution in mammalian cells. Cell 178, 748–761.e17 (2019).

    Google Scholar 

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Acknowledgements

The authors thank members of their groups for insightful discussions. This work was funded by National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) 1R35GM139513 (C.C.L.); NIH NIGMS 1R35GM136354 (M.D.S.); MIT Robert J Silbey Fellowship (A.A.M.); MIT School of Science Fund for Future of Science (A.A.M.); US Department of Energy, Office of Science, Basic Energy Sciences under Award DE-SC0020153 (A.D.H.); Innovative Genomics Institute and Laboratory for Genomics Research (J.E.H., J.E.D. and D.V.S.); UC Berkeley Miller Basic Research Fellowship (Q.Z.); NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) 1R01EB027793 (A.S.K.); Department of Defense (DoD) Vannevar Bush Faculty Fellowship N00014-20-1-2825 (A.S.K.); NIH NIGMS 1R01GM125887 (F.H.A.); and Ministerio de Ciencia e Innovación - Consejo Superior de Investigaciones Científicas (MICIN-CSIC) PTI + REC-EU SGL2103051 and EU Horizon 2020 research and innovation programme FET Open 965018-BIOCELLPHE (L.A.F.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or other funding agencies.

Author information

Author notes

  1. These authors contributed equally: Rosana S. Molina, Gordon Rix.

Authors and Affiliations

  1. Department of Biomedical Engineering, University of California, Irvine, CA, USA

    Rosana S. Molina & Chang C. Liu

  2. Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, USA

    Gordon Rix & Chang C. Liu

  3. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Amanuella A. Mengiste & Matthew D. Shoulders

  4. Department of Microbial Biotechnology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid, Spain

    Beatriz Álvarez & Luis Ángel Fernández

  5. Department of Chemistry, Seoul National University, Seoul, South Korea

    Daeje Seo & Seokhee Kim

  6. Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haiqi Chen

  7. Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haiqi Chen

  8. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

    Juan E. Hurtado, Qiong Zhang, John E. Dueber & David V. Schaffer

  9. Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Jalisco, Mexico

    Jorge Donato García-García

  10. Biological Design Center, Boston University, Boston, MA, USA

    Zachary J. Heins & Ahmad S. Khalil

  11. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Zachary J. Heins & Ahmad S. Khalil

  12. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

    Patrick J. Almhjell & Frances H. Arnold

  13. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Frances H. Arnold

  14. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Ahmad S. Khalil

  15. Horticultural Sciences Department, University of Florida, Gainesville, FL, USA

    Andrew D. Hanson

  16. Innovative Genomics Institute, University of California Berkeley and San Francisco, Berkeley, CA, USA

    John E. Dueber & David V. Schaffer

  17. Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    John E. Dueber

  18. Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA

    David V. Schaffer

  19. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    David V. Schaffer

  20. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    David V. Schaffer

  21. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Fei Chen

  22. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Fei Chen

  23. Department of Chemistry, University of California, Irvine, CA, USA

    Chang C. Liu

  24. Center for Synthetic Biology, University of California, Irvine, CA, USA

    Chang C. Liu

Authors
  1. Rosana S. Molina
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  2. Gordon Rix
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  3. Amanuella A. Mengiste
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  21. Chang C. Liu
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Contributions

Introduction (R.S.M., G.R., A.A.M., M.D.S. and C.C.L.); Experimentation (R.S.M., G.R., A.A.M., B.A., D.S., H.C., J.E.H., Q.Z., J.E.D., D.V.S., F.C., S.K., L.A.F., M.D.S. and C.C.L.); Results (R.S.M., G.R. and C.C.L.); Applications (R.S.M., G.R., A.A.M., B.A., D.S., H.C., J.E.H., Q.Z., J.D.G.-G., Z.J.H., P.J.A., F.H.A., A.S.K., A.D.H., J.E.D., D.V.S., F.C., S.K., L.A.F., M.D.S. and C.C.L.); Reproducibility and data deposition (R.S.M., G.R. and C.C.L.); Limitations and optimizations (R.S.M., G.R., A.A.M., B.A., D.S., H.C., J.E.H., Q.Z., J.E.D., D.V.S., F.C., S.K., L.A.F., M.D.S. and C.C.L.); Outlook (R.S.M., G.R. and C.C.L.). R.S.M. and G.R. contributed equally and are listed in alphabetical order.

Corresponding author

Correspondence to Chang C. Liu.

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Competing interests

C.C.L. is a co-founder of K2 Biotechnologies, which applies continuous evolution to antibody engineering. All other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Methods Primers thanks Jumi Shin, Chong Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

European Nucleotide Archive (ENA): https://www.ebi.ac.uk/ena/browser/home

National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA): https://www.ncbi.nlm.nih.gov/sra

NCBI BioProject: https://www.ncbi.nlm.nih.gov/bioproject/

Supplementary information

Glossary

Directed evolution

A method that employs the evolutionary process of mutation, amplification and screening or selection to improve a protein or other biomolecule towards a desired function on laboratory timescales.

Hypermutation

A marked increase in the mutation rate of a DNA sequence.

Clonal interference

When one clone with a (new) beneficial mutation fails to fix because another lineage with a (new) beneficial mutation arises in the same population, common in asexual populations when mutation rates are high.

Integration cassettes

Pieces of DNA designed to integrate into a specific location within another piece of DNA such as a genome or a plasmid.

Processivity

The ability of an enzyme to catalyse multiple consecutive reactions without releasing its substrate.

Cheater mutations

Mutations that allow a cell to satisfy selection without actually improving the desired function of the biomolecule under evolution.

Unique molecular identifier

(UMI). A random barcode added to sequencing libraries to differentiate individual molecules from each other before amplification.

Consensus sequencing

An approach used in high-throughput sequencing (HTS) that corrects errors by sequencing a particular sequence multiple times and taking the consensus.

Greedy mutations

Single mutations representing the locally optimal choice for improving the function of a gene during a given stage of evolution.

Sign epistasis

When one mutation that has a particular effect on the desired biomolecular function causes the opposite effect when it is in the presence of another mutation.

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Molina, R.S., Rix, G., Mengiste, A.A. et al. In vivo hypermutation and continuous evolution. Nat Rev Methods Primers 2, 36 (2022). https://doi.org/10.1038/s43586-022-00119-5

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