Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/108815
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dc.contributor.authorSorokina, Marija-
dc.contributor.authorTeixeira, João M. C.-
dc.contributor.authorBarrera-Vilarmau, Susana-
dc.contributor.authorPaschke, Reinhard-
dc.contributor.authorPapasotiriou, Ioannis-
dc.contributor.authorRodrigues, João P. G. L. M.-
dc.contributor.authorKastritis, Panagiotis L.-
dc.date.accessioned2023-07-04T12:16:46Z-
dc.date.available2023-07-04T12:16:46Z-
dc.date.issued2020-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/110770-
dc.identifier.urihttp://dx.doi.org/10.25673/108815-
dc.description.abstractEmergence of coronaviruses poses a threat to global health and economy. The current outbreak of SARS-CoV-2 has infected more than 28,000,000 people and killed more than 915,000. To date, there is no treatment for coronavirus infections, making the development of therapies to prevent future epidemics of paramount importance. To this end, we collected information regarding naturally-occurring variants of the Angiotensin-converting enzyme 2 (ACE2), an epithelial receptor that both SARS-CoV and SARS-CoV-2 use to enter the host cells. We built 242 structural models of variants of human ACE2 bound to the receptor binding domain (RBD) of the SARS-CoV-2 surface spike glycoprotein (S protein) and refined their interfaces with HADDOCK. Our dataset includes 140 variants of human ACE2 representing missense mutations found in genome-wide studies, 39 mutants with reported effects on the recognition of the RBD, and 63 predictions after computational alanine scanning mutagenesis of ACE2-RBD interface residues. This dataset will help accelerate the design of therapeutics against SARS-CoV-2, as well as contribute to prevention of possible future coronaviruses outbreaks.eng
dc.language.isoeng-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subject.ddc572-
dc.titleStructural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug designeng
dc.typeArticle-
local.versionTypepublishedVersion-
local.bibliographicCitation.journaltitleScientific data-
local.bibliographicCitation.volume7-
local.bibliographicCitation.pagestart1-
local.bibliographicCitation.pageend10-
local.bibliographicCitation.publishernameNature Publ. Group-
local.bibliographicCitation.publisherplaceLondon-
local.bibliographicCitation.doi10.1038/s41597-020-00652-6-
local.openaccesstrue-
dc.identifier.ppn1851582665-
local.bibliographicCitation.year2020-
cbs.sru.importDate2023-07-04T12:15:57Z-
local.bibliographicCitationEnthalten in Scientific data - London : Nature Publ. Group, 2014-
local.accessrights.dnbfree-
Appears in Collections:Open Access Publikationen der MLU

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