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Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes
Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA..
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
Univ Michigan, Dept Biophys, Ann Arbor, MI 48109 USA..
Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA.;Rice Univ, Dept Bioengn, Houston, TX USA..
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2016 (English)In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719, Vol. 33, no 12, p. 3054-3064Article in journal (Refereed) Published
Abstract [en]

Two-component signaling (TCS) is the primary means by which bacteria sense and respond to the environment. TCS involves two partner proteins working in tandem, which interact to perform cellular functions whereas limiting interactions with non-partners (i.e., cross-talk). We construct a Potts model for TCS that can quantitatively predict how mutating amino acid identities affect the interaction between TCS partners and non-partners. The parameters of this model are inferred directly from protein sequence data. This approach drastically reduces the computational complexity of exploring the sequence-space of TCS proteins. As a stringent test, we compare its predictions to a recent comprehensive mutational study, which characterized the functionality of 20 4 mutational variants of the PhoQ kinase in Escherichia coli. We find that our best predictions accurately reproduce the amino acid combinations found in experiment, which enable functional signaling with its partner PhoP. These predictions demonstrate the evolutionary pressure to preserve the interaction between TCS partners as well as prevent unwanted cross-talk. Further, we calculate the mutational change in the binding affinity between PhoQ and PhoP, providing an estimate to the amount of destabilization needed to disrupt TCS.

Place, publisher, year, edition, pages
2016. Vol. 33, no 12, p. 3054-3064
Keywords [en]
statistical inference, mutational phenotypes, interaction specificity, epistasis, fitness landscape, bacterial signaling
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-310740DOI: 10.1093/molbev/msw188ISI: 000387925300003OAI: oai:DiVA.org:uu-310740DiVA, id: diva2:1058165
Funder
eSSENCE - An eScience CollaborationThe Swedish Foundation for International Cooperation in Research and Higher Education (STINT)Available from: 2016-12-20 Created: 2016-12-19 Last updated: 2017-11-29Bibliographically approved

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