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Predictive shifts in free energy couple mutations to their phenotypic consequences

Chure, Griffin and Razo-Mejia, Manuel and Belliveau, Nathan M. and Einav, Tal and Kaczmarek, Zofii A. and Barnes, Stephanie L. and Lewis, Mitchell and Phillips, Rob (2019) Predictive shifts in free energy couple mutations to their phenotypic consequences. Proceedings of the National Academy of Sciences of the United States of America, 116 (37). pp. 18275-18284. ISSN 0027-8424. PMCID PMC6744869.

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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 that 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 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.

Item Type:Article
Related URLs:
URLURL TypeDescription Information CentralArticle ItemData
Chure, Griffin0000-0002-2216-2057
Razo-Mejia, Manuel0000-0002-9510-0527
Belliveau, Nathan M.0000-0002-1536-1963
Einav, Tal0000-0003-0777-1193
Kaczmarek, Zofii A.0000-0002-4887-3121
Barnes, Stephanie L.0000-0002-5237-603X
Phillips, Rob0000-0003-3082-2809
Additional Information:© 2019 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). Edited by Ned S. Wingreen, Princeton University, Princeton, NJ, and accepted by Editorial Board Member David Baker July 29, 2019 (received for review May 15, 2019). Data and Code Availability: All data were collected, stored, and preserved by using the Git version control software. Code for data processing, analysis, and figure generation is available on the GitHub repository (; DOI:10.5281/zenodo.3366376) or can be accessed via the paper website ( (17). Raw flow cytometry data are 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, Michael Lässig, Heun Jin Lee, Muir Morrison, and Ned Wingreen for thoughtful advice and discussion. This work was supported by La Fondation Pierre-Gilles de Gennes; the Rosen Center at Caltech; and NIH Grants DP1 OD0002179 (Director’s Pioneer Award), R01 GM085286, and 1R35 GM118043 Maximizing Investigators’ Research Award (MIRA). N.M.B. was supported by a Howard Hughes Medical Institute International Student Research fellowship. Author contributions: G.C., M.R.-M., N.M.B., T.E., Z.A.K., S.L.B., M.L., and R.P. designed research; G.C., M.R.-M., N.M.B., T.E., Z.A.K., and S.L.B. performed research; G.C., M.R.-M., and N.M.B. analyzed data; M.L. and R.P. provided critical feedback and guidance; and G.C. and R.P. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. N.S.W. is a guest editor invited by the Editorial Board. Data deposition: Raw-flow cytometry files can be found on the CaltechDATA research data repository, (DOI: 10.22002/D1.1241). Processed data files and computer code used to perform all steps of the analysis are available on the project GitHub repository ( and are registered with Zenodo, (DOI:10.5281/zenodo.3366376). All files, along with instructions on how to generate each figure, are available on the paper webpage, accessible through This article contains supporting information online at
Group:Rosen Bioengineering Center
Funding AgencyGrant Number
La Fondation Pierre Gilles de GennesUNSPECIFIED
Donna and Benjamin M. Rosen Bioengineering CenterUNSPECIFIED
NIHDP1 OD0002179
NIHR01 GM085286
NIH1R35 GM118043
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Issue or Number:37
PubMed Central ID:PMC6744869
Record Number:CaltechAUTHORS:20190826-153157241
Persistent URL:
Official Citation:Predictive shifts in free energy couple mutations to their phenotypic consequences. Griffin Chure, Manuel Razo-Mejia, Nathan M. Belliveau, Tal Einav, Zofii A. Kaczmarek, Stephanie L. Barnes, Mitchell Lewis, Rob Phillips. Proceedings of the National Academy of Sciences Sep 2019, 116 (37) 18275-18284; DOI: 10.1073/pnas.1907869116
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98252
Deposited By: Tony Diaz
Deposited On:26 Aug 2019 22:53
Last Modified:03 Oct 2019 21:39

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