Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion
Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.
| Errataetall: | |
|---|---|
| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
|---|---|
| Enthalten in: |
JCI insight - 8(2023), 13 vom: 10. Juli |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Lubin, Joseph H [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 11.07.2023 Date Revised 13.12.2023 published: Electronic UpdateOf: bioRxiv. 2021 Dec 13;:. - PMID 34931193 Citation Status MEDLINE |
|---|
| doi: |
10.1172/jci.insight.168296 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM357639960 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM357639960 | ||
| 003 | DE-627 | ||
| 005 | 20231227131307.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1172/jci.insight.168296 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1225.xml |
| 035 | |a (DE-627)NLM357639960 | ||
| 035 | |a (NLM)37261904 | ||
| 035 | |a (PII)e168296 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lubin, Joseph H |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.07.2023 | ||
| 500 | |a Date Revised 13.12.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateOf: bioRxiv. 2021 Dec 13;:. - PMID 34931193 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Immunoglobulins | |
| 650 | 4 | |a Structural biology | |
| 650 | 7 | |a Angiotensin-Converting Enzyme 2 |2 NLM | |
| 650 | 7 | |a EC 3.4.17.23 |2 NLM | |
| 650 | 7 | |a Ligands |2 NLM | |
| 650 | 7 | |a Antibodies, Monoclonal |2 NLM | |
| 700 | 1 | |a Markosian, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Balamurugan, D |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Minh T |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Chih-Hsiung |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Dongfang |e verfasserin |4 aut | |
| 700 | 1 | |a Pasqualini, Renata |e verfasserin |4 aut | |
| 700 | 1 | |a Arap, Wadih |e verfasserin |4 aut | |
| 700 | 1 | |a Burley, Stephen K |e verfasserin |4 aut | |
| 700 | 1 | |a Khare, Sagar D |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t JCI insight |d 2016 |g 8(2023), 13 vom: 10. Juli |w (DE-627)NLM257703918 |x 2379-3708 |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2023 |g number:13 |g day:10 |g month:07 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1172/jci.insight.168296 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2023 |e 13 |b 10 |c 07 | ||