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Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures
We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR oper...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802038/ https://www.ncbi.nlm.nih.gov/pubmed/36712320 http://dx.doi.org/10.1093/pnasnexus/pgac180 |
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author | Zaccaria, Marco Genovese, Luigi Dawson, William Cristiglio, Viviana Nakajima, Takahito Johnson, Welkin Farzan, Michael Momeni, Babak |
author_facet | Zaccaria, Marco Genovese, Luigi Dawson, William Cristiglio, Viviana Nakajima, Takahito Johnson, Welkin Farzan, Michael Momeni, Babak |
author_sort | Zaccaria, Marco |
collection | PubMed |
description | We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue’s contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes. |
format | Online Article Text |
id | pubmed-9802038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98020382023-01-26 Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures Zaccaria, Marco Genovese, Luigi Dawson, William Cristiglio, Viviana Nakajima, Takahito Johnson, Welkin Farzan, Michael Momeni, Babak PNAS Nexus Biological, Health, and Medical Sciences We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue’s contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes. Oxford University Press 2022-09-01 /pmc/articles/PMC9802038/ /pubmed/36712320 http://dx.doi.org/10.1093/pnasnexus/pgac180 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Biological, Health, and Medical Sciences Zaccaria, Marco Genovese, Luigi Dawson, William Cristiglio, Viviana Nakajima, Takahito Johnson, Welkin Farzan, Michael Momeni, Babak Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title | Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title_full | Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title_fullStr | Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title_full_unstemmed | Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title_short | Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures |
title_sort | probing the mutational landscape of the sars-cov-2 spike protein via quantum mechanical modeling of crystallographic structures |
topic | Biological, Health, and Medical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802038/ https://www.ncbi.nlm.nih.gov/pubmed/36712320 http://dx.doi.org/10.1093/pnasnexus/pgac180 |
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