Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization

Year
2021
Type(s)
Author(s)
Matthew C. Chan, Kui K. Chan, Erik Procko and Diwakar Shukla
Source
bioRxiv, 2021
Url(s)
https://doi.org/10.1101/2021.12.22.473902
BibTeX
BibTeX

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.

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