{"doi":"10.1021/acs.jpcb.3c00469","title":"Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization","abstract":"A potential therapeutic strategy for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding to the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant variant, named sACE2 2 .v.2.4, that exhibits subnanomolar affinity to the receptor-binding domain (RBD) of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identify other ACE2 variants that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified multiple ACE2 double mutants that bind SARS-CoV-2 S with tighter affinity as compared to the wild type, most notably L79V;N90D that binds RBD similarly to ACE2 2 .v.2.4. The experimental validation of the double mutants successfully demonstrates the use of machine learning approaches for engineering protein–protein interactions and identifying high-affinity ACE2 peptides for targeting SARS-CoV-2.","journal":"The Journal of Physical Chemistry B","year":2023,"id":360276,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9561,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552628,"name":"Kui K. Chan","orcid":"0000-0002-6351-2885","position":1,"is_corresponding":false},{"id":343873,"name":"Erik Procko","orcid":"0000-0002-0028-490X","position":2,"is_corresponding":false},{"id":625516,"name":"Diwakar Shukla","orcid":"0000-0003-4079-5381","position":3,"is_corresponding":false},{"id":815481,"name":"Matthew C. Chan","orcid":"0000-0002-9826-1983","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:14:01.896928Z","pmid":"36827526","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}