Chan MC, Chan KK, Procko E, Shukla D. Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization.
BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.12.22.473902. [PMID:
34981064 PMCID:
PMC8722601 DOI:
10.1101/2021.12.22.473902]
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Abstract
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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