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

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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.

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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.

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.

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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

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.

Correspondence to Nobuhiko Tokuriki.

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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) doi:10.1038/s41589-019-0386-3

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