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Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening
Background: Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the inhibition of SARS-CoV-2 virus replicat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Newlands Press Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920029/ https://www.ncbi.nlm.nih.gov/pubmed/35220726 http://dx.doi.org/10.4155/fmc-2021-0269 |
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author | Zhang, Li-chuan Zhao, Hui-lin Liu, Jin He, Lei Yu, Ri-lei Kang, Cong-min |
author_facet | Zhang, Li-chuan Zhao, Hui-lin Liu, Jin He, Lei Yu, Ri-lei Kang, Cong-min |
author_sort | Zhang, Li-chuan |
collection | PubMed |
description | Background: Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the inhibition of SARS-CoV-2 virus replication and proliferation. Materials & methods: In this study, deep reinforcement learning, covalent docking and molecular dynamics simulations were used to identify novel compounds that have the potential to inhibit both Mpro and PLpro. Results and conclusion: Three compounds were identified that can effectively occupy the Mpro protein cavity with the PLpro protein cavity and form high frequency contacts with key amino acid residues (Mpro: His41, Cys145, Glu166, PLpro: Cys111). These three compounds can be further investigated as potential lead compounds for SARS-CoV-2 inhibitors. |
format | Online Article Text |
id | pubmed-8920029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Newlands Press Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-89200292022-03-15 Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening Zhang, Li-chuan Zhao, Hui-lin Liu, Jin He, Lei Yu, Ri-lei Kang, Cong-min Future Med Chem Research Article Background: Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the inhibition of SARS-CoV-2 virus replication and proliferation. Materials & methods: In this study, deep reinforcement learning, covalent docking and molecular dynamics simulations were used to identify novel compounds that have the potential to inhibit both Mpro and PLpro. Results and conclusion: Three compounds were identified that can effectively occupy the Mpro protein cavity with the PLpro protein cavity and form high frequency contacts with key amino acid residues (Mpro: His41, Cys145, Glu166, PLpro: Cys111). These three compounds can be further investigated as potential lead compounds for SARS-CoV-2 inhibitors. Newlands Press Ltd 2022-02-27 2021-11 /pmc/articles/PMC8920029/ /pubmed/35220726 http://dx.doi.org/10.4155/fmc-2021-0269 Text en © 2022 Newlands Press https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Research Article Zhang, Li-chuan Zhao, Hui-lin Liu, Jin He, Lei Yu, Ri-lei Kang, Cong-min Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title | Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title_full | Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title_fullStr | Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title_full_unstemmed | Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title_short | Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
title_sort | design of sars-cov-2 mpro, plpro dual-target inhibitors based on deep reinforcement learning and virtual screening |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920029/ https://www.ncbi.nlm.nih.gov/pubmed/35220726 http://dx.doi.org/10.4155/fmc-2021-0269 |
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