Paper

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

Chan, Kui K, Procko, Erik, Chan, Matthew, Shukla, Diwakar

2021

Overview

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. Competing Interest Statement E.P. is an inventor on a patent filing by the University of Illinois covering the use of engineered peptides targeting coronaviruses. E.P. and K.K.C. are cofounders of Orthogonal Biologics, which licenses the intellectual property and is in a business partnership with Cyrus Biotechnology.

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: ACE2 / Affinity / Angiotensin-converting enzyme 2 / Biophysics / Biotechnology / Coronaviruses / Intellectual property / Learning algorithms / Machine learning / Mutants / Peptides / Protein engineering / Protein interaction / Proteins / Severe acute respiratory syndrome coronavirus 2 / Transfer learning