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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7485| Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization | |
| Matthew Chan Kui Chan Erik Procko . Diwakar Shukla | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2021.12.22.473902 | |
| https://www.biorxiv.org/content/10.1101/2021.12.22.473902v1 | |
| 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 ACE22.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 ACE22.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. | |
| medRxiv and bioRxiv | |
| 23-12-2021 | |
| Preimpreso | |
| www.biorxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| OTRAS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Machine learning guided design of high affinity ACE2 decoys for SARS COV2 neutralization.pdf | 1.64 MB | Adobe PDF | Visualizar/Abrir |