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Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein

Author

Listed:
  • Shimon Bershtein

    (Weizmann Institute of Science)

  • Michal Segal

    (Weizmann Institute of Science)

  • Roy Bekerman

    (Weizmann Institute of Science)

  • Nobuhiko Tokuriki

    (Weizmann Institute of Science)

  • Dan S. Tawfik

    (Weizmann Institute of Science)

Abstract

The distribution of fitness effects of protein mutations is still unknown1,2. Of particular interest is whether accumulating deleterious mutations interact, and how the resulting epistatic effects shape the protein’s fitness landscape. Here we apply a model system in which bacterial fitness correlates with the enzymatic activity of TEM-1 β-lactamase (antibiotic degradation). Subjecting TEM-1 to random mutational drift and purifying selection (to purge deleterious mutations) produced changes in its fitness landscape indicative of negative epistasis; that is, the combined deleterious effects of mutations were, on average, larger than expected from the multiplication of their individual effects. As observed in computational systems3,4,5, negative epistasis was tightly associated with higher tolerance to mutations (robustness). Thus, under a low selection pressure, a large fraction of mutations was initially tolerated (high robustness), but as mutations accumulated, their fitness toll increased, resulting in the observed negative epistasis. These findings, supported by FoldX stability computations of the mutational effects6, prompt a new model in which the mutational robustness (or neutrality) observed in proteins, and other biological systems, is due primarily to a stability margin, or threshold, that buffers the deleterious physico-chemical effects of mutations on fitness. Threshold robustness is inherently epistatic—once the stability threshold is exhausted, the deleterious effects of mutations become fully pronounced, thereby making proteins far less robust than generally assumed.

Suggested Citation

  • Shimon Bershtein & Michal Segal & Roy Bekerman & Nobuhiko Tokuriki & Dan S. Tawfik, 2006. "Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein," Nature, Nature, vol. 444(7121), pages 929-932, December.
  • Handle: RePEc:nat:nature:v:444:y:2006:i:7121:d:10.1038_nature05385
    DOI: 10.1038/nature05385
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    Cited by:

    1. Jingzhi Lou & Weiwen Liang & Lirong Cao & Inchi Hu & Shi Zhao & Zigui Chen & Renee Wan Yi Chan & Peter Pak Hang Cheung & Hong Zheng & Caiqi Liu & Qi Li & Marc Ka Chun Chong & Yexian Zhang & Eng-kiong , 2024. "Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Steven Schulz & Sébastien Boyer & Matteo Smerlak & Simona Cocco & Rémi Monasson & Clément Nizak & Olivier Rivoire, 2021. "Parameters and determinants of responses to selection in antibody libraries," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-24, March.
    3. Zachary R Sailer & Sarah H Shafik & Robert L Summers & Alex Joule & Alice Patterson-Robert & Rowena E Martin & Michael J Harms, 2020. "Inferring a complete genotype-phenotype map from a small number of measured phenotypes," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-27, September.
    4. Jordan Yang & Nandita Naik & Jagdish Suresh Patel & Christopher S Wylie & Wenze Gu & Jessie Huang & F Marty Ytreberg & Mandar T Naik & Daniel M Weinreich & Brenda M Rubenstein, 2020. "Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-26, May.
    5. 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.
    6. Manhart, Michael & Haldane, Allan & Morozov, Alexandre V., 2012. "A universal scaling law determines time reversibility and steady state of substitutions under selection," Theoretical Population Biology, Elsevier, vol. 82(1), pages 66-76.

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