Analysis of emergent bivalent antibody binding identifies the molecular reach as a critical determinant of SARS-CoV-2 neutralisation potency
Key functions of antibodies, such as viral neutralisation, depend on bivalent binding but the factors that influence it remain poorly characterised. Here, we develop and employ a new bivalent model to mechanistically analyse binding between >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. Our method reproduces the monovalent on/off-rates and enables measurements of the bivalent on-rate and the molecular reach: the maximum antigen separation that supports bivalent binding. We find large variations in these parameters across antibodies, including variations in reach (22-46 nm) that exceed the physical antibody size (~15 nm) due to the antigen size. The bivalent model integrates all parameters, including reach and antigen density, to predict an emergent binding potency for each antibody that matches their neutralisation potency. Indeed, antibodies with similar monovalent affinities to the same RBD-epitope but with different reaches display differences in emergent bivalent binding that match differences in their neutralisation potency. Together, our work highlights that antibodies within an isotype class binding the same antigen can display differences in molecular reach that can substantially modulate their emergent binding and functional properties..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 15. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huhn, Anna [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.09.06.556503 |
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| PPN (Katalog-ID): |
XBI040784428 |
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| 520 | |a Key functions of antibodies, such as viral neutralisation, depend on bivalent binding but the factors that influence it remain poorly characterised. Here, we develop and employ a new bivalent model to mechanistically analyse binding between >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. Our method reproduces the monovalent on/off-rates and enables measurements of the bivalent on-rate and the molecular reach: the maximum antigen separation that supports bivalent binding. We find large variations in these parameters across antibodies, including variations in reach (22-46 nm) that exceed the physical antibody size (~15 nm) due to the antigen size. The bivalent model integrates all parameters, including reach and antigen density, to predict an emergent binding potency for each antibody that matches their neutralisation potency. Indeed, antibodies with similar monovalent affinities to the same RBD-epitope but with different reaches display differences in emergent bivalent binding that match differences in their neutralisation potency. Together, our work highlights that antibodies within an isotype class binding the same antigen can display differences in molecular reach that can substantially modulate their emergent binding and functional properties. | ||
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| 700 | 1 | |a Wilson, Daniel B |e verfasserin |0 (orcid)0000-0002-0940-3311 |4 aut | |
| 700 | 1 | |a Kutuzov, Mikhail A |e verfasserin |0 (orcid)0000-0003-3386-4350 |4 aut | |
| 700 | 1 | |a Donat, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Tiong Kit |e verfasserin |0 (orcid)0000-0001-8746-8308 |4 aut | |
| 700 | 1 | |a Zhang, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Barton, Michael I |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Chang |e verfasserin |4 aut | |
| 700 | 1 | |a Dejnirattisai, Wanwisa |e verfasserin |4 aut | |
| 700 | 1 | |a Supasa, Piyada |e verfasserin |4 aut | |
| 700 | 1 | |a Mongkolsapaya, Juthathip |e verfasserin |0 (orcid)0000-0003-3416-9480 |4 aut | |
| 700 | 1 | |a Townsend, Alain |e verfasserin |0 (orcid)0000-0002-3702-0107 |4 aut | |
| 700 | 1 | |a James, William S |e verfasserin |4 aut | |
| 700 | 1 | |a Screaton, Gavin |e verfasserin |0 (orcid)0000-0002-3549-4309 |4 aut | |
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