In silico investigation on the inhibitory effect of fungal secondary metabolites on RNA dependent RNA polymerase of SARS-CoV-II: A docking and molecular dynamic simulation study
The newly emerged Coronavirus Disease 2019 (COVID-19) rapidly outspread worldwide and now is one of the biggest infectious pandemics in human society. In this study, the inhibitory potential of 99 secondary metabolites obtained from endophytic fungi was investigated against the new coronavirus RNA-d...
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| Veröffentlicht in: | Computers in biology and medicine 2021-08, Vol.135, p.104613-104613, Article 104613 |
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| creator | Ebrahimi, Kosar Sadat Ansari, Mohabbat Hosseyni Moghaddam, Mahdieh S Ebrahimi, Zohre salehi, Zohre Shahlaei, Mohsen Moradi, Sajad |
| description | The newly emerged Coronavirus Disease 2019 (COVID-19) rapidly outspread worldwide and now is one of the biggest infectious pandemics in human society. In this study, the inhibitory potential of 99 secondary metabolites obtained from endophytic fungi was investigated against the new coronavirus RNA-dependent RNA polymerase (RdRp) using computational methods. A sequence of blind and targeted molecular dockings was performed to predict the more potent compounds on the viral enzyme. In the next step, the five selected compounds were further evaluated by molecular dynamics (MD) simulation. Moreover, the pharmacokinetics of the metabolites was assessed using SwissADME server. The results of molecular docking showed that compounds 18-methoxy cytochalasin J, (22E,24R)-stigmasta-5,7,22-trien-3-β-ol, beauvericin, dankasterone B, and pyrrocidine A had higher binding energy than others. The findings of MD and SwissADME demonstrated that two fungal metabolites, 18-methoxy cytochalasin J and pyrrocidine A had better results than others in terms of protein instability, strong complex formation, and pharmacokinetic properties. In conclusion, it is recommended to further evaluate the compounds 18-methoxy cytochalasin J and pyrrocidine A in the laboratory as good candidates for inhibiting COVID-19.
•Fungal secondary metabolites are tested against coronavirus RdRp computationally.•Docking, molecular dynamic simulation and pharmacokinetic predictions are used in this study.•The results predicted that some of the examined compounds can potentially inhibit this enzyme. |
| doi_str_mv | 10.1016/j.compbiomed.2021.104613 |
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•Fungal secondary metabolites are tested against coronavirus RdRp computationally.•Docking, molecular dynamic simulation and pharmacokinetic predictions are used in this study.•The results predicted that some of the examined compounds can potentially inhibit this enzyme.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2021.104613</identifier><identifier>PMID: 34242870</identifier><language>eng</language><publisher>OXFORD: Elsevier Ltd</publisher><subject>Algorithms ; Antiviral Agents - pharmacology ; Beauvericin ; Biology ; Complex formation ; Computer applications ; Computer Science ; Computer Science, Interdisciplinary Applications ; Coronavirus RNA-Dependent RNA Polymerase - antagonists & inhibitors ; Coronaviruses ; COVID-19 ; DNA-directed RNA polymerase ; Endophytes ; Endophytic fungi ; Energy ; Engineering ; Engineering, Biomedical ; Enzymes ; Evaluation ; Fungi ; Fungi - chemistry ; Genomes ; Life Sciences & Biomedicine ; Life Sciences & Biomedicine - Other Topics ; Ligands ; Mathematical & Computational Biology ; Metabolites ; Molecular docking ; Molecular Docking Simulation ; Molecular dynamics ; Molecular Dynamics Simulation ; Molecular modeling ; Nucleotide sequence ; Pandemics ; Pharmacokinetics ; Pharmacology ; Protein structure ; Proteins ; RNA polymerase ; RNA-Dependent RNA Polymerase ; RNA-directed RNA polymerase ; SARS-CoV-2 - drug effects ; Science & Technology ; Secondary metabolite ; Secondary metabolites ; Severe acute respiratory syndrome coronavirus 2 ; Simulation ; Technology ; Viral diseases ; Viruses</subject><ispartof>Computers in biology and medicine, 2021-08, Vol.135, p.104613-104613, Article 104613</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright © 2021 Elsevier Ltd. All rights reserved.</rights><rights>2021. Elsevier Ltd</rights><rights>2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>31</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000687882400003</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c573t-51e424fb4859bfc0e75e8bae189837d79bb3f5a08bbe2c170c08fc1c29225b803</citedby><cites>FETCH-LOGICAL-c573t-51e424fb4859bfc0e75e8bae189837d79bb3f5a08bbe2c170c08fc1c29225b803</cites><orcidid>0000-0001-8833-6235</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2563179898?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,315,781,785,886,3551,27929,27930,39263,46000,64390,64394,72474</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34242870$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ebrahimi, Kosar Sadat</creatorcontrib><creatorcontrib>Ansari, Mohabbat</creatorcontrib><creatorcontrib>Hosseyni Moghaddam, Mahdieh S</creatorcontrib><creatorcontrib>Ebrahimi, Zohre</creatorcontrib><creatorcontrib>salehi, Zohre</creatorcontrib><creatorcontrib>Shahlaei, Mohsen</creatorcontrib><creatorcontrib>Moradi, Sajad</creatorcontrib><title>In silico investigation on the inhibitory effect of fungal secondary metabolites on RNA dependent RNA polymerase of SARS-CoV-II: A docking and molecular dynamic simulation study</title><title>Computers in biology and medicine</title><addtitle>COMPUT BIOL MED</addtitle><addtitle>Comput Biol Med</addtitle><description>The newly emerged Coronavirus Disease 2019 (COVID-19) rapidly outspread worldwide and now is one of the biggest infectious pandemics in human society. In this study, the inhibitory potential of 99 secondary metabolites obtained from endophytic fungi was investigated against the new coronavirus RNA-dependent RNA polymerase (RdRp) using computational methods. A sequence of blind and targeted molecular dockings was performed to predict the more potent compounds on the viral enzyme. In the next step, the five selected compounds were further evaluated by molecular dynamics (MD) simulation. Moreover, the pharmacokinetics of the metabolites was assessed using SwissADME server. The results of molecular docking showed that compounds 18-methoxy cytochalasin J, (22E,24R)-stigmasta-5,7,22-trien-3-β-ol, beauvericin, dankasterone B, and pyrrocidine A had higher binding energy than others. The findings of MD and SwissADME demonstrated that two fungal metabolites, 18-methoxy cytochalasin J and pyrrocidine A had better results than others in terms of protein instability, strong complex formation, and pharmacokinetic properties. In conclusion, it is recommended to further evaluate the compounds 18-methoxy cytochalasin J and pyrrocidine A in the laboratory as good candidates for inhibiting COVID-19.
•Fungal secondary metabolites are tested against coronavirus RdRp computationally.•Docking, molecular dynamic simulation and pharmacokinetic predictions are used in this study.•The results predicted that some of the examined compounds can potentially inhibit this enzyme.</description><subject>Algorithms</subject><subject>Antiviral Agents - pharmacology</subject><subject>Beauvericin</subject><subject>Biology</subject><subject>Complex formation</subject><subject>Computer applications</subject><subject>Computer Science</subject><subject>Computer Science, Interdisciplinary Applications</subject><subject>Coronavirus RNA-Dependent RNA Polymerase - antagonists & inhibitors</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>DNA-directed RNA polymerase</subject><subject>Endophytes</subject><subject>Endophytic fungi</subject><subject>Energy</subject><subject>Engineering</subject><subject>Engineering, Biomedical</subject><subject>Enzymes</subject><subject>Evaluation</subject><subject>Fungi</subject><subject>Fungi - chemistry</subject><subject>Genomes</subject><subject>Life Sciences & Biomedicine</subject><subject>Life Sciences & Biomedicine - Other Topics</subject><subject>Ligands</subject><subject>Mathematical & Computational Biology</subject><subject>Metabolites</subject><subject>Molecular docking</subject><subject>Molecular Docking Simulation</subject><subject>Molecular dynamics</subject><subject>Molecular Dynamics Simulation</subject><subject>Molecular modeling</subject><subject>Nucleotide sequence</subject><subject>Pandemics</subject><subject>Pharmacokinetics</subject><subject>Pharmacology</subject><subject>Protein structure</subject><subject>Proteins</subject><subject>RNA polymerase</subject><subject>RNA-Dependent RNA Polymerase</subject><subject>RNA-directed RNA polymerase</subject><subject>SARS-CoV-2 - drug effects</subject><subject>Science & Technology</subject><subject>Secondary metabolite</subject><subject>Secondary metabolites</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Simulation</subject><subject>Technology</subject><subject>Viral 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silico investigation on the inhibitory effect of fungal secondary metabolites on RNA dependent RNA polymerase of SARS-CoV-II: A docking and molecular dynamic simulation study</title><author>Ebrahimi, Kosar Sadat ; Ansari, Mohabbat ; Hosseyni Moghaddam, Mahdieh S ; Ebrahimi, Zohre ; salehi, Zohre ; Shahlaei, Mohsen ; Moradi, Sajad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c573t-51e424fb4859bfc0e75e8bae189837d79bb3f5a08bbe2c170c08fc1c29225b803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Antiviral Agents - pharmacology</topic><topic>Beauvericin</topic><topic>Biology</topic><topic>Complex formation</topic><topic>Computer applications</topic><topic>Computer Science</topic><topic>Computer Science, Interdisciplinary Applications</topic><topic>Coronavirus RNA-Dependent RNA Polymerase - antagonists & inhibitors</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>DNA-directed RNA polymerase</topic><topic>Endophytes</topic><topic>Endophytic fungi</topic><topic>Energy</topic><topic>Engineering</topic><topic>Engineering, Biomedical</topic><topic>Enzymes</topic><topic>Evaluation</topic><topic>Fungi</topic><topic>Fungi - chemistry</topic><topic>Genomes</topic><topic>Life Sciences & Biomedicine</topic><topic>Life Sciences & Biomedicine - Other Topics</topic><topic>Ligands</topic><topic>Mathematical & Computational Biology</topic><topic>Metabolites</topic><topic>Molecular docking</topic><topic>Molecular Docking Simulation</topic><topic>Molecular dynamics</topic><topic>Molecular Dynamics Simulation</topic><topic>Molecular modeling</topic><topic>Nucleotide sequence</topic><topic>Pandemics</topic><topic>Pharmacokinetics</topic><topic>Pharmacology</topic><topic>Protein structure</topic><topic>Proteins</topic><topic>RNA polymerase</topic><topic>RNA-Dependent RNA 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MED</stitle><addtitle>Comput Biol Med</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>135</volume><spage>104613</spage><epage>104613</epage><pages>104613-104613</pages><artnum>104613</artnum><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>The newly emerged Coronavirus Disease 2019 (COVID-19) rapidly outspread worldwide and now is one of the biggest infectious pandemics in human society. In this study, the inhibitory potential of 99 secondary metabolites obtained from endophytic fungi was investigated against the new coronavirus RNA-dependent RNA polymerase (RdRp) using computational methods. A sequence of blind and targeted molecular dockings was performed to predict the more potent compounds on the viral enzyme. In the next step, the five selected compounds were further evaluated by molecular dynamics (MD) simulation. Moreover, the pharmacokinetics of the metabolites was assessed using SwissADME server. The results of molecular docking showed that compounds 18-methoxy cytochalasin J, (22E,24R)-stigmasta-5,7,22-trien-3-β-ol, beauvericin, dankasterone B, and pyrrocidine A had higher binding energy than others. The findings of MD and SwissADME demonstrated that two fungal metabolites, 18-methoxy cytochalasin J and pyrrocidine A had better results than others in terms of protein instability, strong complex formation, and pharmacokinetic properties. In conclusion, it is recommended to further evaluate the compounds 18-methoxy cytochalasin J and pyrrocidine A in the laboratory as good candidates for inhibiting COVID-19.
•Fungal secondary metabolites are tested against coronavirus RdRp computationally.•Docking, molecular dynamic simulation and pharmacokinetic predictions are used in this study.•The results predicted that some of the examined compounds can potentially inhibit this enzyme.</abstract><cop>OXFORD</cop><pub>Elsevier Ltd</pub><pmid>34242870</pmid><doi>10.1016/j.compbiomed.2021.104613</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-8833-6235</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Computers in biology and medicine, 2021-08, Vol.135, p.104613-104613, Article 104613 |
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| subjects | Algorithms Antiviral Agents - pharmacology Beauvericin Biology Complex formation Computer applications Computer Science Computer Science, Interdisciplinary Applications Coronavirus RNA-Dependent RNA Polymerase - antagonists & inhibitors Coronaviruses COVID-19 DNA-directed RNA polymerase Endophytes Endophytic fungi Energy Engineering Engineering, Biomedical Enzymes Evaluation Fungi Fungi - chemistry Genomes Life Sciences & Biomedicine Life Sciences & Biomedicine - Other Topics Ligands Mathematical & Computational Biology Metabolites Molecular docking Molecular Docking Simulation Molecular dynamics Molecular Dynamics Simulation Molecular modeling Nucleotide sequence Pandemics Pharmacokinetics Pharmacology Protein structure Proteins RNA polymerase RNA-Dependent RNA Polymerase RNA-directed RNA polymerase SARS-CoV-2 - drug effects Science & Technology Secondary metabolite Secondary metabolites Severe acute respiratory syndrome coronavirus 2 Simulation Technology Viral diseases Viruses |
| title | In silico investigation on the inhibitory effect of fungal secondary metabolites on RNA dependent RNA polymerase of SARS-CoV-II: A docking and molecular dynamic simulation study |
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