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Published May 16, 2019 | Submitted
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The Energetics of Molecular Adaptation in Transcriptional Regulation

Abstract

Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only a subset of the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. bioRxiv preprint first posted online May. 15, 2019. Data and Code Availability. All data was collected, stored, and preserved using the Git version control software. Code for data processing, analysis, and figure generation is available on the GitHub repository (https://www.github.com/rpgroup-pboc/mwc_mutants) or can be accessed via the paper website. Raw flow cytometry data is stored on the CaltechDATA data repository and can be accessed via DOI 10.22002/D1.1241. We thank Pamela Björkman, Rachel Galimidi, and Priyanthi Gnanapragasam for access and training for the use of the Miltenyi Biotec MACSQuant flow cytometer. The experimental efforts first took place at the Physiology summer course at the Marine Biological Laboratory in Woods Hole, MA, operated by the University of Chicago. We thank Ambika Nadkarni and Damian Dudka for their work on the project during the course. We also thank Suzannah Beeler, Justin Bois, Robert Brewster, Soichi Hirokawa, Heun Jin Lee, and Muir Morrison for thoughtful advice and discussion. This work was supported by La Fondation Pierre-Gilles de Gennes, the Rosen Center at Caltech, the NIH DP1 OD0002179 (Director's Pioneer Award), R01 GM085286, and 1R35 GM118043 (MIRA). Nathan M. Belliveau was supported by a Howard Hughes Medical Institute International Student Research fellowship.

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Submitted - 1905.06360.pdf

Submitted - 638270.full.pdf

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August 22, 2023
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October 23, 2023