IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-44333-5.html
   My bibliography  Save this article

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44333-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-44333-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Juannan Zhou & David M. McCandlish, 2020. "Minimum epistasis interpolation for sequence-function relationships," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nan Zheng & Yongchao Cai & Zehua Zhang & Huimin Zhou & Yu Deng & Shuang Du & Mai Tu & Wei Fang & Xiaole Xia, 2025. "Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    2. Yeonwoo Park & Brian P. H. Metzger & Joseph W. Thornton, 2024. "The simplicity of protein sequence-function relationships," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Soham Dibyachintan & Alexandre K. Dubé & David Bradley & Pascale Lemieux & Ugo Dionne & Christian R. Landry, 2025. "Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network," Nature Communications, Nature, vol. 16(1), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yeonwoo Park & Brian P. H. Metzger & Joseph W. Thornton, 2024. "The simplicity of protein sequence-function relationships," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Andreas Wagner, 2023. "Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Xin-Xin Zhu & Wen-Qing Zheng & Zi-Wei Xia & Xin-Ru Chen & Tian Jin & Xu-Wei Ding & Fei-Fei Chen & Qi Chen & Jian-He Xu & Xu-Dong Kong & Gao-Wei Zheng, 2024. "Evolutionary insights into the stereoselectivity of imine reductases based on ancestral sequence reconstruction," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Solip Park & Fran Supek & Ben Lehner, 2021. "Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    5. Fatma-Elzahraa Eid & Albert T. Chen & Ken Y. Chan & Qin Huang & Qingxia Zheng & Isabelle G. Tobey & Simon Pacouret & Pamela P. Brauer & Casey Keyes & Megan Powell & Jencilin Johnston & Binhui Zhao & K, 2024. "Systematic multi-trait AAV capsid engineering for efficient gene delivery," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Rachapun Rotrattanadumrong & Yohei Yokobayashi, 2022. "Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Lucile Vigué & Giancarlo Croce & Marie Petitjean & Etienne Ruppé & Olivier Tenaillon & Martin Weigt, 2022. "Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Mireia Seuma & Ben Lehner & Benedetta Bolognesi, 2022. "An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Kevin E. Wu & Kevin K. Yang & Rianne Berg & Sarah Alamdari & James Y. Zou & Alex X. Lu & Ava P. Amini, 2024. "Protein structure generation via folding diffusion," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    10. Jeffrey A. Ruffolo & Lee-Shin Chu & Sai Pooja Mahajan & Jeffrey J. Gray, 2023. "Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Lin Li & Esther Gupta & John Spaeth & Leslie Shing & Rafael Jaimes & Emily Engelhart & Randolph Lopez & Rajmonda S. Caceres & Tristan Bepler & Matthew E. Walsh, 2023. "Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    12. Evgenii Lobzaev & Michael A. Herrera & Martyna Kasprzyk & Giovanni Stracquadanio, 2024. "Protein engineering using variational free energy approximation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    13. Evgenii Lobzaev & Giovanni Stracquadanio, 2024. "Dirichlet latent modelling enables effective learning and sampling of the functional protein design space," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Steve O'Hagan & Joshua Knowles & Douglas B Kell, 2012. "Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-14, November.
    15. Kaushal Sharma & Ramandeep Singh & Suresh Kumar Sharma & Akshay Anand, 2021. "Sleeping pattern and activities of daily living modulate protein expression in AMD," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.
    16. Javier Santos-Moreno & Eve Tasiudi & Hadiastri Kusumawardhani & Joerg Stelling & Yolanda Schaerli, 2023. "Robustness and innovation in synthetic genotype networks," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    17. Cauã Antunes Westmann & Leander Goldbach & Andreas Wagner, 2024. "The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    18. Nicki Skafte Detlefsen & Søren Hauberg & Wouter Boomsma, 2022. "Learning meaningful representations of protein sequences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Enrico Orsi & Lennart Schada von Borzyskowski & Stephan Noack & Pablo I. Nikel & Steffen N. Lindner, 2024. "Automated in vivo enzyme engineering accelerates biocatalyst optimization," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Emily K. Makowski & Patrick C. Kinnunen & Jie Huang & Lina Wu & Matthew D. Smith & Tiexin Wang & Alec A. Desai & Craig N. Streu & Yulei Zhang & Jennifer M. Zupancic & John S. Schardt & Jennifer J. Lin, 2022. "Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44333-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.