Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization
A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE2 2 .v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE2 2 .v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2.
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2021 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2021) vom: 23. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chan, Matthew C [VerfasserIn] |
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Date Revised 09.03.2023 published: Electronic UpdateIn: J Phys Chem B. 2023 Feb 24;:. - PMID 36827526 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2021.12.22.473902 |
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| PPN (Katalog-ID): |
NLM335165176 |
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| 500 | |a UpdateIn: J Phys Chem B. 2023 Feb 24;:. - PMID 36827526 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE2 2 .v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE2 2 .v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2 | ||
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| 700 | 1 | |a Shukla, Diwakar |e verfasserin |4 aut | |
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