Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning
Introduction: The COVID-19 pandemic has cast a heavy toll in human lives and global economics. COVID-19 is caused by the SARS-CoV-2 virus, which infects cells via its spike protein binding human ACE2.Methods: To discover potential inhibitory peptidomimetic macrocycles for the spike/ACE2 complex we d...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Drug Discovery |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fddsv.2022.1085701/full |
| _version_ | 1827165591097573376 |
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| author | Lev Shapira Shaul Lerner Guila Assayag Alexandra Vardi Dikla Haham Gideon Bar Vicky Fidelsky Kozokaro Maayan Elias Robicsek Immanuel Lerner Amit Michaeli |
| author_facet | Lev Shapira Shaul Lerner Guila Assayag Alexandra Vardi Dikla Haham Gideon Bar Vicky Fidelsky Kozokaro Maayan Elias Robicsek Immanuel Lerner Amit Michaeli |
| author_sort | Lev Shapira |
| collection | DOAJ |
| description | Introduction: The COVID-19 pandemic has cast a heavy toll in human lives and global economics. COVID-19 is caused by the SARS-CoV-2 virus, which infects cells via its spike protein binding human ACE2.Methods: To discover potential inhibitory peptidomimetic macrocycles for the spike/ACE2 complex we deployed Artificial Intelligence guided virtual screening with three distinct strategies: 1) Allosteric spike inhibitors 2) Competitive ACE2 inhibitors and 3) Competitive spike inhibitors. Screening was performed by docking macrocycles to the relevant sites, clustering and synthesizing cluster representatives. Synthesized molecules were screened for inhibition using AlphaLISA and RSV particles.Results: All three strategies yielded inhibitory peptides, but only the competitive spike inhibitors showed “hit” level activity.Discussion: These results suggest that direct inhibition of the spike RBD domain is the most attractive strategy for peptidomimetic, “head-to-tail” macrocycle drug development against the ongoing pandemic. |
| first_indexed | 2024-04-11T05:27:10Z |
| format | Article |
| id | doaj.art-7a6cb3b1fa4a404799dd97bf143d0409 |
| institution | Directory Open Access Journal |
| issn | 2674-0338 |
| language | English |
| last_indexed | 2025-03-21T01:34:56Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Drug Discovery |
| spelling | doaj.art-7a6cb3b1fa4a404799dd97bf143d04092024-08-03T00:50:19ZengFrontiers Media S.A.Frontiers in Drug Discovery2674-03382022-12-01210.3389/fddsv.2022.10857011085701Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learningLev ShapiraShaul LernerGuila AssayagAlexandra VardiDikla HahamGideon BarVicky Fidelsky KozokaroMaayan Elias RobicsekImmanuel LernerAmit MichaeliIntroduction: The COVID-19 pandemic has cast a heavy toll in human lives and global economics. COVID-19 is caused by the SARS-CoV-2 virus, which infects cells via its spike protein binding human ACE2.Methods: To discover potential inhibitory peptidomimetic macrocycles for the spike/ACE2 complex we deployed Artificial Intelligence guided virtual screening with three distinct strategies: 1) Allosteric spike inhibitors 2) Competitive ACE2 inhibitors and 3) Competitive spike inhibitors. Screening was performed by docking macrocycles to the relevant sites, clustering and synthesizing cluster representatives. Synthesized molecules were screened for inhibition using AlphaLISA and RSV particles.Results: All three strategies yielded inhibitory peptides, but only the competitive spike inhibitors showed “hit” level activity.Discussion: These results suggest that direct inhibition of the spike RBD domain is the most attractive strategy for peptidomimetic, “head-to-tail” macrocycle drug development against the ongoing pandemic.https://www.frontiersin.org/articles/10.3389/fddsv.2022.1085701/fullmacrocyclepeptidomimeticSARS-CoV-2inhibitorscreeningspike |
| spellingShingle | Lev Shapira Shaul Lerner Guila Assayag Alexandra Vardi Dikla Haham Gideon Bar Vicky Fidelsky Kozokaro Maayan Elias Robicsek Immanuel Lerner Amit Michaeli Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning Frontiers in Drug Discovery macrocycle peptidomimetic SARS-CoV-2 inhibitor screening spike |
| title | Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning |
| title_full | Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning |
| title_fullStr | Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning |
| title_full_unstemmed | Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning |
| title_short | Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning |
| title_sort | discovery of novel spike ace2 inhibitory macrocycles using in silico reinforcement learning |
| topic | macrocycle peptidomimetic SARS-CoV-2 inhibitor screening spike |
| url | https://www.frontiersin.org/articles/10.3389/fddsv.2022.1085701/full |
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