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The genotype-phenotype landscape of an allosteric protein

Drew S. Tack, Peter D. Tonner, Abe Pressman, View ORCID ProfileNathanael D. Olson, View ORCID ProfileSasha F. Levy, Eugenia F. Romantseva, Nina Alperovich, Olga Vasilyeva, View ORCID ProfileDavid Ross
doi: https://doi.org/10.1101/2020.09.30.320812
Drew S. Tack
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Peter D. Tonner
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Abe Pressman
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Nathanael D. Olson
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Sasha F. Levy
2SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
3Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
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Eugenia F. Romantseva
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Nina Alperovich
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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Olga Vasilyeva
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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David Ross
1National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
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  • ORCID record for David Ross
  • For correspondence: david.ross@nist.gov
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Abstract

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://doi.org/10.18434/M32259

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
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Posted September 30, 2020.
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The genotype-phenotype landscape of an allosteric protein
Drew S. Tack, Peter D. Tonner, Abe Pressman, Nathanael D. Olson, Sasha F. Levy, Eugenia F. Romantseva, Nina Alperovich, Olga Vasilyeva, David Ross
bioRxiv 2020.09.30.320812; doi: https://doi.org/10.1101/2020.09.30.320812
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The genotype-phenotype landscape of an allosteric protein
Drew S. Tack, Peter D. Tonner, Abe Pressman, Nathanael D. Olson, Sasha F. Levy, Eugenia F. Romantseva, Nina Alperovich, Olga Vasilyeva, David Ross
bioRxiv 2020.09.30.320812; doi: https://doi.org/10.1101/2020.09.30.320812

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