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  Computational epitope map of SARS-CoV-2 spike protein

Sikora, M., von Bülow, S., Blanc, F. E. C., Gecht, M., Covino, R., & Hummer, G. (2021). Computational epitope map of SARS-CoV-2 spike protein. PLoS Computational Biology, 17(4): e1008790. doi:10.1371/journal.pcbi.1008790.

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 Creators:
Sikora, Mateusz1, 2, Author              
von Bülow, Sören1, Author              
Blanc, Florian E. C.1, Author              
Gecht, Michael1, Author              
Covino, Roberto1, 3, Author              
Hummer, Gerhard1, 4, Author              
Affiliations:
1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
2Faculty of Physics, University of Vienna, Vienna, Austria, ou_persistent22              
3Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany, ou_persistent22              
4Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany, ou_persistent22              

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 Abstract: The primary immunological target of COVID-19 vaccines is the SARS-CoV-2 spike (S) protein. S is exposed on the viral surface and mediates viral entry into the host cell. To identify possible antibody binding sites, we performed multi-microsecond molecular dynamics simulations of a 4.1 million atom system containing a patch of viral membrane with four full-length, fully glycosylated and palmitoylated S proteins. By mapping steric accessibility, structural rigidity, sequence conservation, and generic antibody binding signatures, we recover known epitopes on S and reveal promising epitope candidates for structure-based vaccine design. We find that the extensive and inherently flexible glycan coat shields a surface area larger than expected from static structures, highlighting the importance of structural dynamics. The protective glycan shield and the high flexibility of its hinges give the stalk overall low epitope scores. Our computational epitope-mapping procedure is general and should thus prove useful for other viral envelope proteins whose structures have been characterized.

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Language(s): eng - English
 Dates: 2020-10-092021-02-142021-04-01
 Publication Status: Published online
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1008790
BibTex Citekey: sikora_computational_2021
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 17 (4) Sequence Number: e1008790 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1