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dc.contributor.authorFuglebakk, Edvin
dc.contributor.authorReuter, Nathalie
dc.date.accessioned2019-05-15T14:02:48Z
dc.date.available2019-05-15T14:02:48Z
dc.date.issued2018-07-26
dc.PublishedFuglebakk E, Reuter N. A model for hydrophobic protrusions on peripheral membrane proteins. PloS Computational Biology. 2018;14(7):e1006325eng
dc.identifier.issn1553-7358en_US
dc.identifier.issn1553-734Xen_US
dc.identifier.urihttps://hdl.handle.net/1956/19651
dc.description.abstractWith remarkable spatial and temporal specificities, peripheral membrane proteins bind to biological membranes. They do this without compromising solubility of the protein, and their binding sites are not easily distinguished. Prototypical peripheral membrane binding sites display a combination of patches of basic and hydrophobic amino acids that are also frequently present on other protein surfaces. The purpose of this contribution is to identify simple but essential components for membrane binding, through structural criteria that distinguish exposed hydrophobes at membrane binding sites from those that are frequently found on any protein surface. We formulate the concepts of protruding hydrophobes and co-insertability and have analysed more than 300 families of proteins that are classified as peripheral membrane binders. We find that this structural motif strongly discriminates the surfaces of membrane-binding and non-binding proteins. Our model constitutes a novel formulation of a structural pattern for membrane recognition and emphasizes the importance of subtle structural properties of hydrophobic membrane binding sites.en_US
dc.language.isoengeng
dc.publisherPublic Library of Scienceen_US
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/eng
dc.titleA model for hydrophobic protrusions on peripheral membrane proteinsen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2019-01-24T08:59:13Z
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2018 The Authorsen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1006325
dc.identifier.cristin1605487
dc.source.journalPloS Computational Biology
dc.relation.projectNorges forskningsråd: 251247


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