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dc.contributor.authorCheng, R.R.
Nordesjӧ, O.
Hayes, R.L.
Levine, H.
Flores, S.C.
Onuchic, J.N.
Morcos, F.
dc.date.accessioned 2017-05-05T19:00:53Z
dc.date.available 2017-05-05T19:00:53Z
dc.date.issued 2016
dc.identifier.citation Cheng, R.R., Nordesjӧ, O., Hayes, R.L., et al.. "Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes." Molecular Biology and Evolution, 33, no. 12 (2016) Oxford University Press: 3054-3064. https://doi.org/10.1093/molbev/msw188.
dc.identifier.urihttps://hdl.handle.net/1911/94197
dc.description.abstract 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 204 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.
dc.language.iso eng
dc.publisher Oxford University Press
dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.title Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes
dc.type Journal article
dc.citation.journalTitle Molecular Biology and Evolution
dc.contributor.org Center for Theoretical Biological Physics
dc.subject.keywordstatistical inference
mutational phenotypes
interaction specificity
epistasis
fitness landscape
bacterial signaling
dc.citation.volumeNumber 33
dc.citation.issueNumber 12
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1093/molbev/msw188
dc.identifier.pmcid PMC5100047
dc.identifier.pmid 27604223
dc.type.publication publisher version
dc.citation.firstpage 3054
dc.citation.lastpage 3064


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