Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations
Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using...
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| Published in | RSC advances Vol. 1; no. 53; pp. 31991 - 31996 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Royal Society of Chemistry
28.08.2020
The Royal Society of Chemistry |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2046-2069 2046-2069 |
| DOI | 10.1039/d0ra06212j |
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| Abstract | Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of
R
Dock
= 0.72 ± 0.14 and
R
W
= −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are
periandrin V
,
penimocycline
,
cis-p-Coumaroylcorosolic acid
,
glycyrrhizin
, and
uralsaponin B
. The obtained results could probably lead to enhance the COVID-19 therapy.
A combination of Autodock Vina and FPL calculations suggested that
periandrin V
,
penimocycline
,
cis-p-Coumaroylcorosolic acid
,
glycyrrhizin
, and
uralsaponin B
are able to bind well to SARS-CoV-2 Mpro. |
|---|---|
| AbstractList | Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of
R
Dock
= 0.72 ± 0.14 and
R
W
= −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are
periandrin V
,
penimocycline
,
cis-p-Coumaroylcorosolic acid
,
glycyrrhizin
, and
uralsaponin B
. The obtained results could probably lead to enhance the COVID-19 therapy.
A combination of Autodock Vina and FPL calculations suggested that
periandrin V
,
penimocycline
,
cis-p-Coumaroylcorosolic acid
,
glycyrrhizin
, and
uralsaponin B
are able to bind well to SARS-CoV-2 Mpro. Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of = 0.72 ± 0.14 and = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are , , , , and . The obtained results could probably lead to enhance the COVID-19 therapy. Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V , penimocycline , cis-p-Coumaroylcorosolic acid , glycyrrhizin , and uralsaponin B . The obtained results could probably lead to enhance the COVID-19 therapy. Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of RDock = 0.72 ± 0.14 and RW = −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy. Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of RDₒcₖ = 0.72 ± 0.14 and RW = −0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy. Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy. |
| Author | Han Pham, T. Ngoc Nguyen, Trung Hai Pham, Minh Quan Ngo, Son Tung Tung, Nguyen Thanh Thuy Huong, Le Thi Vu, Van V Tran, Linh Hoang Vu, Khanh B |
| AuthorAffiliation | Institute of Natural Products Chemistry International University Graduate University of Science and Technology Vietnam Academy of Science and Technology School of Biotechnology Faculty of Applied Sciences Faculty of Pharmacy NTT Hi-Tech Institute Faculty of Civil Energeering Nguyen Tat Thanh University Institute of Materials Science Ton Duc Thang University Vietnam National University Ho Chi Minh University of Technology (HCMUT) Laboratory of Theoretical and Computational Biophysics |
| AuthorAffiliation_xml | – name: International University – name: Ho Chi Minh University of Technology (HCMUT) – name: Vietnam National University – name: Faculty of Civil Energeering – name: Laboratory of Theoretical and Computational Biophysics – name: Faculty of Pharmacy – name: Faculty of Applied Sciences – name: Nguyen Tat Thanh University – name: NTT Hi-Tech Institute – name: Vietnam Academy of Science and Technology – name: Institute of Natural Products Chemistry – name: Ton Duc Thang University – name: Graduate University of Science and Technology – name: Institute of Materials Science – name: School of Biotechnology |
| Author_xml | – sequence: 1 givenname: Minh Quan surname: Pham fullname: Pham, Minh Quan – sequence: 2 givenname: Khanh B surname: Vu fullname: Vu, Khanh B – sequence: 3 givenname: T. Ngoc surname: Han Pham fullname: Han Pham, T. Ngoc – sequence: 4 givenname: Le Thi surname: Thuy Huong fullname: Thuy Huong, Le Thi – sequence: 5 givenname: Linh Hoang surname: Tran fullname: Tran, Linh Hoang – sequence: 6 givenname: Nguyen Thanh surname: Tung fullname: Tung, Nguyen Thanh – sequence: 7 givenname: Van V surname: Vu fullname: Vu, Van V – sequence: 8 givenname: Trung Hai surname: Nguyen fullname: Nguyen, Trung Hai – sequence: 9 givenname: Son Tung surname: Ngo fullname: Ngo, Son Tung |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35518150$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Chemistry China computer simulation Coronavirus infections correlation Correlation coefficients glycyrrhizin lead Ligands Molecular docking prediction Protease Protease inhibitors proteinases Severe acute respiratory syndrome coronavirus 2 Simulation therapeutics |
| Title | Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations |
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