Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Higher-order epistasis shapes the fitness landscape of a xenobiotic-degrading enzyme

Nature Chemical Biology volume 15, pages 1120–1128 (2019)Cite this article

Subjects

An Author Correction to this article was published on 12 June 2020

This article has been updated

Abstract

Characterizing the adaptive landscapes that encompass the emergence of novel enzyme functions can provide molecular insights into both enzymatic and evolutionary mechanisms. Here, we combine ancestral protein reconstruction with biochemical, structural and mutational analyses to characterize the functional evolution of methyl-parathion hydrolase (MPH), an organophosphate-degrading enzyme. We identify five mutations that are necessary and sufficient for the evolution of MPH from an ancestral dihydrocoumarin hydrolase. In-depth analyses of the adaptive landscapes encompassing this evolutionary transition revealed that the mutations form a complex interaction network, defined in part by higher-order epistasis, that constrained the adaptive pathways available. By also characterizing the adaptive landscapes in terms of their functional activities towards three additional organophosphate substrates, we reveal that subtle differences in the polarity of the substrate substituents drastically alter the network of epistatic interactions. Our work suggests that the mutations function collectively to enable substrate recognition via subtle structural repositioning.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Phylogeny and phenotype of MPH.
Fig. 2: Identification of five key adaptive mutations between AncDHCH1 and MPH.
Fig. 3: Structural and biochemical effects of five key mutations.
Fig. 4: Adaptive landscape and mutational effects of key mutations for MPH activity.
Fig. 5: Mutational analyses for three additional OP substrates.
Fig. 6: Changes in the singular and epistatic effects of h258L and i271T between methyl-parathion and methyl-paraoxon substrates.

Similar content being viewed by others

Data availability

The crystal structure of AncDHCH1 solved in this study has been deposited at the Protein Data Bank under accession code 6C2C. The raw data for the statistical analyses presented in Figs. 4, 5 and 6 and Supplementary Figs. 9 and 13 have been made publicly available at GitHub (https://github.com/danderson8/Yangetal2019.git). All other data supporting the findings of this study is available within the paper and its supplementary files.

Code availability

All analysis scripts, along with example data encoding, have been made publicly available via GitHub at https://github.com/danderson8/Yangetal2019.git.

Change history

  • 12 June 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sunden, F., Peck, A., Salzman, J., Ressl, S. & Herschlag, D. Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site. eLife 4, e06181 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  4. Tufts, D. M. et al. Epistasis constrains mutational pathways of hemoglobin adaptation in high-altitude pikas. Mol. Biol. Evol. 32, 287–298 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Meini, M.-R., Tomatis, P. E., Weinreich, D. M. & Vila, A. J. Quantitative description of a protein fitness landscape based on molecular features. Mol. Biol. Evol. 32, 1774–1787 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Canale, A. S., Cote-Hammarlof, P. A., Flynn, J. M. & Bolon, D. N. Evolutionary mechanisms studied through protein fitness landscapes. Curr. Opin. Struct. Biol. 48, 141–148 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. O’Maille, P. E. et al. Quantitative exploration of the catalytic landscape separating divergent plant sesquiterpene synthases. Nat. Chem. Biol. 4, 617–623 (2008).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  8. Lunzer, M., Miller, 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 

  9. Clifton, B. E. et al. Evolution of cyclohexadienyl dehydratase from an ancestral solute-binding protein. Nat. Chem. Biol. 14, 542–547 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Kaltenbach, M. et al. Evolution of chalcone isomerase from a non-catalytic ancestor. Nat. Chem. Biol. 14, 548–555 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Stormo, G. D. Maximally efficient modeling of DNA sequence motifs at all levels of complexity. Genetics 187, 1219–1224 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Anderson, D. W., McKeown, A. N. & Thornton, J. W. Intermolecular epistasis shaped the function and evolution of an ancient transcription factor and its DNA binding sites. eLife 4, e07864 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Weinreich, D. M., Lan, Y., Jaffe, J. & Heckendorn, R. B. The influence of higher-order epistasis on biological fitness landscape topography. J. Stat. Phys. 172, 208–225 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sailer, Z. R. & Harms, M. J. High-order epistasis shapes evolutionary trajectories. PLoS Comput. Biol. 13, e1005541 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Sun, L. et al. Crystallization and preliminary X-ray studies of methyl parathion hydrolase from Pseudomonas sp. WBC-3. Acta Crystallogr. D 60, 954–956 (2004).

    Article  PubMed  CAS  Google Scholar 

  16. Malla, R. K., Bandyopadhyay, S., Spilling, C. D., Dutta, S. & Dupureur, C. M. The first total synthesis of (±)-cyclophostin and (±)-cyclipostin P: inhibitors of the serine hydrolases acetyl cholinesterase and hormone sensitive lipase. Org. Lett. 13, 3094–3097 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nguyen, P. C. et al. Cyclipostins and cyclophostin analogs as promising compounds in the fight against tuberculosis. Sci. Rep. 7, 11751 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  18. Liu, H., Zhang, J.-J., Wang, S.-J., Zhang, X.-E. & Zhou, N.-Y. Plasmid-borne catabolism of methyl parathion and p-nitrophenol in Pseudomonas sp. strain WBC-3. Biochem. Biophys. Res. Commun. 334, 1107–1114 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Luo, X. J. et al. Switching a newly discovered lactonase into an efficient and thermostable phosphotriesterase by simple double mutations His250Ile/Ile263Trp. Biotechnol. Bioeng. 111, 1920–1930 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Baier, F. & Tokuriki, N. Connectivity between catalytic landscapes of the metallo-β-lactamase superfamily. J. Mol. Biol. 426, 2442–2456 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Khersonsky, O. & Tawfik, D. S. Structure–reactivity studies of serum paraoxonase PON1 suggest that its native activity is lactonase. Biochemistry 44, 6371–6382 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. Purg, M. et al. Probing the mechanisms for the selectivity and promiscuity of methyl parathion hydrolase. Philos. Trans. A Math. Phys. Eng. Sci 374, 20160150 (2016).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  23. Hong, S. B. & Raushel, F. M. Metal–substrate interactions facilitate the catalytic activity of the bacterial phosphotriesterase. Biochemistry 35, 10904–10912 (1996).

    Article  CAS  PubMed  Google Scholar 

  24. Jackson, C. J., Liu, J.-W., Coote, M. L. & Ollis, D. L. The effects of substrate orientation on the mechanism of a phosphotriesterase. Org. Biomol. Chem. 3, 4343–4350 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. McKeown, A. N. et al. Evolution of DNA specificity in a transcription factor family produced a new gene regulatory module. Cell 159, 58–68 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Boucher, J. I., Jacobowitz, J. R., Beckett, B. C., Classen, S. & Theobald, D. L. An atomic-resolution view of neofunctionalization in the evolution of apicomplexan lactate dehydrogenases. eLife 3, e02304 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  27. 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 

  28. Kratzer, J. T. et al. Evolutionary history and metabolic insights of ancient mammalian uricases. Proc. Natl Acad. Sci. USA 111, 3763–3768 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hochberg, G. K. A. & Thornton, J. W. Reconstructing ancient proteins to understand the causes of structure and function. Annu. Rev. Biophys. 46, 247–269 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Russell, R. J. et al. The evolution of new enzyme function: lessons from xenobiotic metabolizing bacteria versus insecticide-resistant insects. Evol. Appl. 4, 225–248 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Copley, S. D. Evolution of a metabolic pathway for degradation of a toxic xenobiotic: the patchwork approach. Trends Biochem. Sci. 25, 261–265 (2000).

    Article  CAS  PubMed  Google Scholar 

  32. Afriat-Jurnou, L., Jackson, C. J. & Tawfik, D. S. Reconstructing a missing link in the evolution of a recently diverged phosphotriesterase by active-site loop remodeling. Biochemistry 51, 6047–6055 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Crawford, R. L., Jung, C. M. & Strap, J. L. The recent evolution of pentachlorophenol (PCP)-4-monooxygenase (PcpB) and associated pathways for bacterial degradation of PCP. Biodegradation 18, 525–539 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Siddiq, M. A., Hochberg, G. K. & Thornton, J. W. Evolution of protein specificity: insights from ancestral protein reconstruction. Curr. Opin. Struct. Biol. 47, 113–122 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Starr, T. N., Picton, L. K. & Thornton, J. W. Alternative evolutionary histories in the sequence space of an ancient protein. Nature 549, 409–413 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Miton, C. M. & Tokuriki, N. How mutational epistasis impairs predictability in protein evolution and design. Protein Sci. 7, 1260–1272 (2016).

    Article  CAS  Google Scholar 

  37. Kaltenbach, M. & Tokuriki, N. Dynamics and constraints of enzyme evolution. J. Exp. Zool. B Mol. Dev. Evol. 322, 468–487 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Koonin, E. V. Replaying the tape of life: quantification of the predictability of evolution. Front. Genet. 3, 246 (2012).

    PubMed  PubMed Central  Google Scholar 

  39. Ingles, D. W. & Knowles, J. R. Specificity and stereospecificity of alpha-chymotrypsin. Biochem. J. 104, 369–377 (1967).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Miton, C. M. et al. Evolutionary repurposing of a sulfatase: a new Michaelis complex leads to efficient transition state charge offset. Proc. Natl Acad. Sci. USA 115, E7293–E7302 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jiménez-Osés, G. et al. The role of distant mutations and allosteric regulation on LovD active site dynamics. Nat. Chem. Biol. 10, 431–436 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  42. Tokuriki, N. & Tawfik, D. S. Protein dynamism and evolvability. Science 324, 203–207 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Campbell, E. et al. The role of protein dynamics in the evolution of new enzyme function. Nat. Chem. Biol. 12, 944–950 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wallace, I. M., O’Sullivan, O., Higgins, D. G. & Notredame, C. M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 34, 1692–1699 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Abascal, F., Zardoya, R. & Posada, D. ProtTest: selection of best-fit models of protein evolution. Bioinformatics 21, 2104–2105 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article  CAS  PubMed  Google Scholar 

  51. Sambrook, J. & Russell, D. W. Molecular Cloning: A Laboratory Manual 3rd edn (Spring Harbor Laboratory Press, 2001).

  52. Kabsch, W. Integration, scaling, space-group assignment and post-refinement. Acta Crystallogr. D 66, 133–144 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 64, 352–367 (2012).

    Article  CAS  Google Scholar 

  54. Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D 67, 355–367 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Schrödinger, Release 2018-3 Maestro (Schrödinger, 2018).

  57. Bas, D. C., Rogers, D. M. & Jensen, J. H. Very fast prediction and rationalization of pK a values for protein–ligand complexes. Proteins 73, 765–783 (2008).

    Article  CAS  PubMed  Google Scholar 

  58. Harder, E. et al. OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281–296 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Friesner, R. A. et al. Extra precise glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem. 49, 6177–6196 (2006).

    Article  CAS  PubMed  Google Scholar 

  60. Jackson, C. J. et al. In crystallo capture of a Michaelis complex and product-binding modes of a bacterial phosphotriesterase. J. Mol. Biol. 375, 1189–1119 (2008).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A. Pabis for performing computational analysis and providing revision and comments on the manuscript. N.T. and E.B.-B. thank the Human Frontier Science Program (HFSP) for support via research grant RGP0006/2013. N.T. acknowledges support by the Natural Sciences and Engineering Research Council of Canada (NSERC) via discovery grants RGPIN 418262-12 and RGPIN 2017-04909. N.T. is a CIHR new investigator and a Michael Smith Foundation of Health Research (MSFHR) career investigator. S.C.L.K. thanks the Knut and Alice Wallenberg Foundation (Wallenberg Academy Fellowships 2013.0124 and 2018.0140) and the Swedish National Infrastructure for Computing (SNIC). D.W.A. thanks NSERC and the MSFHR for post-doctoral support.

Author information

Authors and Affiliations

  1. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    Gloria Yang, Dave W Anderson, Florian Baier & Nobuhiko Tokuriki

  2. Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada

    Dave W Anderson

  3. Institute for Evolution and Biodiversity, Evolutionary Bioinformatics, Westfälische Wilhelms University, Münster, Germany

    Elias Dohmen & Erich Bornberg-Bauer

  4. Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia

    Nansook Hong, Paul D Carr & Colin J Jackson

  5. Science for Life Laboratory, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden

    Shina Caroline Lynn Kamerlin

Authors
  1. Gloria Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dave W Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florian Baier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elias Dohmen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nansook Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paul D Carr
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shina Caroline Lynn Kamerlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Colin J Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Erich Bornberg-Bauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nobuhiko Tokuriki
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.Y. and N.T. conceived and designed this study. G.Y. and F.B. performed activity assays and mutational analysis. D.W.A. performed statistical analyses. E.B.-B. supervised bioinformatics. E.D. performed ancestral sequence reconstruction. F.B., N.H. and P.D.C. collected structural data under the supervision of C.J.J. N.H. and C.J.J. carried out molecular docking. S.C.L.K. designed computational analysis. G.Y. and N.T. wrote the paper with input from all authors.

Corresponding author

Correspondence to Nobuhiko Tokuriki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–8 and Supplementary Figures 1–13.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, G., Anderson, D.W., Baier, F. et al. Higher-order epistasis shapes the fitness landscape of a xenobiotic-degrading enzyme. Nat Chem Biol 15, 1120–1128 (2019). https://doi.org/10.1038/s41589-019-0386-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-019-0386-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing