Designing bacterial signaling interactions with coevolutionary landscapes

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dc.citation.articleNumbere0201734en_US
dc.citation.issueNumber8en_US
dc.citation.journalTitlePLoS ONEen_US
dc.citation.volumeNumber13en_US
dc.contributor.authorCheng, Ryan R.en_US
dc.contributor.authorHaglund, Ellinoren_US
dc.contributor.authorTiee, Nicholas S.en_US
dc.contributor.authorMorcos, Farucken_US
dc.contributor.authorLevine, Herberten_US
dc.contributor.authorAdams, Joseph A.en_US
dc.contributor.authorJennings, Patricia A.en_US
dc.contributor.authorOnuchic, José N.en_US
dc.date.accessioned2018-11-09T15:00:01Zen_US
dc.date.available2018-11-09T15:00:01Zen_US
dc.date.issued2018en_US
dc.description.abstractSelecting amino acids to design novel protein-protein interactions that facilitate catalysis is a daunting challenge. We propose that a computational coevolutionary landscape based on sequence analysis alone offers a major advantage over expensive, time-consuming brute-force approaches currently employed. Our coevolutionary landscape allows prediction of single amino acid substitutions that produce functional interactions between non-cognate, interspecies signaling partners. In addition, it can also predict mutations that maintain segregation of signaling pathways across species. Specifically, predictions of phosphotransfer activity between the Escherichia coli histidine kinase EnvZ to the non-cognate receiver Spo0F from Bacillus subtilis were compiled. Twelve mutations designed to enhance, suppress, or have a neutral effect on kinase phosphotransfer activity to a non-cognate partner were selected. We experimentally tested the ability of the kinase to relay phosphate to the respective designed Spo0F receiver proteins against the theoretical predictions. Our key finding is that the coevolutionary landscape theory, with limited structural data, can significantly reduce the search-space for successful prediction of single amino acid substitutions that modulate phosphotransfer between the two-component His-Asp relay partners in a predicted fashion. This combined approach offers significant improvements over large-scale mutations studies currently used for protein engineering and design.en_US
dc.identifier.citationCheng, Ryan R., Haglund, Ellinor, Tiee, Nicholas S., et al.. "Designing bacterial signaling interactions with coevolutionary landscapes." <i>PLoS ONE,</i> 13, no. 8 (2018) Public Library of Science: https://doi.org/10.1371/journal.pone.0201734.en_US
dc.identifier.digitaljournal.pone.0201734en_US
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0201734en_US
dc.identifier.urihttps://hdl.handle.net/1911/103306en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDesigning bacterial signaling interactions with coevolutionary landscapesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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