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Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

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  • Karol Buda

    (University of British Columbia)

  • Charlotte M. Miton

    (University of British Columbia)

  • Nobuhiko Tokuriki

    (University of British Columbia)

Abstract

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

Suggested Citation

  • Karol Buda & Charlotte M. Miton & Nobuhiko Tokuriki, 2023. "Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44333-5
    DOI: 10.1038/s41467-023-44333-5
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    References listed on IDEAS

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    1. Júlia Domingo & Guillaume Diss & Ben Lehner, 2018. "Pairwise and higher-order genetic interactions during the evolution of a tRNA," Nature, Nature, vol. 558(7708), pages 117-121, June.
    2. Juannan Zhou & David M. McCandlish, 2020. "Minimum epistasis interpolation for sequence-function relationships," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Dave W. Anderson & Florian Baier & Gloria Yang & Nobuhiko Tokuriki, 2021. "The adaptive landscape of a metallo-enzyme is shaped by environment-dependent epistasis," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Adam C. Palmer & Erdal Toprak & Michael Baym & Seungsoo Kim & Adrian Veres & Shimon Bershtein & Roy Kishony, 2015. "Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    5. Nobuhiko Tokuriki & Colin J. Jackson & Livnat Afriat-Jurnou & Kirsten T. Wyganowski & Renmei Tang & Dan S. Tawfik, 2012. "Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme," Nature Communications, Nature, vol. 3(1), pages 1-10, January.
    6. Jung-Eun Shin & Adam J. Riesselman & Aaron W. Kollasch & Conor McMahon & Elana Simon & Chris Sander & Aashish Manglik & Andrew C. Kruse & Debora S. Marks, 2021. "Protein design and variant prediction using autoregressive generative models," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    7. Jamie T. Bridgham & Eric A. Ortlund & Joseph W. Thornton, 2009. "An epistatic ratchet constrains the direction of glucocorticoid receptor evolution," Nature, Nature, vol. 461(7263), pages 515-519, September.
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