Connecting the sequence-space of bacterial signaling proteins to phenotypes using coevolutionary landscapes

R. R. Cheng, O. Nordesjö, R. L. Hayes, H. Levine, S. C. Flores, J. N. Onuchic, F. Morcos

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

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 Pottsmodel for TCS that can quantitatively predict howmutating 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.

Original languageEnglish
Pages (from-to)3054-3064
Number of pages11
JournalMolecular Biology and Evolution
Volume33
Issue number12
DOIs
StatePublished - Dec 1 2016

Bibliographical note

Publisher Copyright:
© The Author 2016.

Keywords

  • Bacterial signaling
  • Epistasis
  • Fitness landscape
  • Interaction specificity
  • Mutational phenotypes
  • Statistical inference

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

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