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Learning the pattern of epistasis linking genotype and phenotype in a protein

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  • Frank J. Poelwijk

    (Dana-Farber Cancer Institute)

  • Michael Socolich

    (University of Chicago)

  • Rama Ranganathan

    (University of Chicago)

Abstract

Understanding the pattern of epistasis—the non-independence of mutations—is critical for relating genotype and phenotype. However, the combinatorial complexity of potential epistatic interactions has severely limited the analysis of this problem. Using new mutational approaches, we report a comprehensive experimental study of all 213 mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein—an opportunity to examine epistasis up to the 13th order. The data show the existence of many high-order epistatic interactions between mutations, but also reveal extraordinary sparsity, enabling novel experimental and computational strategies for learning the relevant epistasis. We demonstrate that such information, in turn, can be used to accurately predict phenotypes in practical situations where the number of measurements is limited. Finally, we show how the observed epistasis shapes the solution space of single-mutation trajectories between the parental fluorescent proteins, informative about the protein’s evolutionary potential. This work provides conceptual and experimental strategies to profoundly characterize epistasis in a protein, relevant to both natural and laboratory evolution.

Suggested Citation

  • Frank J. Poelwijk & Michael Socolich & Rama Ranganathan, 2019. "Learning the pattern of epistasis linking genotype and phenotype in a protein," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12130-8
    DOI: 10.1038/s41467-019-12130-8
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    Cited by:

    1. Remkes A. Scheele & Laurens H. Lindenburg & Maya Petek & Markus Schober & Kevin N. Dalby & Florian Hollfelder, 2022. "Droplet-based screening of phosphate transfer catalysis reveals how epistasis shapes MAP kinase interactions with substrates," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. 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.
    3. 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.
    4. Federica Luppino & Ivan A. Adzhubei & Christopher A. Cassa & Agnes Toth-Petroczy, 2023. "DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Brian M. Petersen & Monica B. Kirby & Karson M. Chrispens & Olivia M. Irvin & Isabell K. Strawn & Cyrus M. Haas & Alexis M. Walker & Zachary T. Baumer & Sophia A. Ulmer & Edgardo Ayala & Emily R. Rhod, 2024. "An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. Jonathan Yaacov Weinstein & Carlos Martí-Gómez & Rosalie Lipsh-Sokolik & Shlomo Yakir Hoch & Demian Liebermann & Reinat Nevo & Haim Weissman & Ekaterina Petrovich-Kopitman & David Margulies & Dmitry I, 2023. "Designed active-site library reveals thousands of functional GFP variants," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. 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.
    8. David C. Marciano & Chen Wang & Teng-Kuei Hsu & Thomas Bourquard & Benu Atri & Ralf B. Nehring & Nicholas S. Abel & Elizabeth A. Bowling & Taylor J. Chen & Pamela D. Lurie & Panagiotis Katsonis & Susa, 2022. "Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. David Ding & Ada Y. Shaw & Sam Sinai & Nathan Rollins & Noam Prywes & David F. Savage & Michael T. Laub & Debora S. Marks, 2024. "Protein design using structure-based residue preferences," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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