In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets
The merging of distinct computational approaches has become a powerful strategy for discovering new biologically active compounds. By using molecular modeling, significant efforts have recently resulted in the development of new molecules, demonstrating high efficiency in reducing the replication of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Biomolecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-273X/12/4/482 |
| _version_ | 1797436764179136512 |
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| author | Federico Ricci Rosaria Gitto Giovanna Pitasi Laura De Luca |
| author_facet | Federico Ricci Rosaria Gitto Giovanna Pitasi Laura De Luca |
| author_sort | Federico Ricci |
| collection | DOAJ |
| description | The merging of distinct computational approaches has become a powerful strategy for discovering new biologically active compounds. By using molecular modeling, significant efforts have recently resulted in the development of new molecules, demonstrating high efficiency in reducing the replication of severe acute respiratory coronavirus 2 (SARS-CoV-2), the agent responsible for the COVID-19 pandemic. We have focused our interest on non-structural protein Nsp13 (NTPase/helicase), as a crucial protein, embedded in the replication–transcription complex (RTC), that controls the virus life cycle. To assist in the identification of the most druggable surfaces of Nsps13, we applied a combination of four computational tools: FTMap, SiteMap, Fpocket and LigandScout. These software packages explored the binding sites for different three-dimensional structures of RTC complexes (PDB codes: 6XEZ, 7CXM, 7CXN), thus, detecting several hot spots, that were clustered to obtain ensemble consensus sites, through a combination of four different approaches. The comparison of data provided new insights about putative druggable sites that might be employed for further docking simulations on druggable surfaces of Nsps13, in a scenario of repurposing drugs. |
| first_indexed | 2024-03-09T11:07:19Z |
| format | Article |
| id | doaj.art-92ae8584aa094e9484bd474a1fd4e96f |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T11:07:19Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-92ae8584aa094e9484bd474a1fd4e96f2023-12-01T00:55:11ZengMDPI AGBiomolecules2218-273X2022-03-0112448210.3390/biom12040482In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable PocketsFederico Ricci0Rosaria Gitto1Giovanna Pitasi2Laura De Luca3Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, ItalyDepartment of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, ItalyDepartment of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, ItalyDepartment of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, ItalyThe merging of distinct computational approaches has become a powerful strategy for discovering new biologically active compounds. By using molecular modeling, significant efforts have recently resulted in the development of new molecules, demonstrating high efficiency in reducing the replication of severe acute respiratory coronavirus 2 (SARS-CoV-2), the agent responsible for the COVID-19 pandemic. We have focused our interest on non-structural protein Nsp13 (NTPase/helicase), as a crucial protein, embedded in the replication–transcription complex (RTC), that controls the virus life cycle. To assist in the identification of the most druggable surfaces of Nsps13, we applied a combination of four computational tools: FTMap, SiteMap, Fpocket and LigandScout. These software packages explored the binding sites for different three-dimensional structures of RTC complexes (PDB codes: 6XEZ, 7CXM, 7CXN), thus, detecting several hot spots, that were clustered to obtain ensemble consensus sites, through a combination of four different approaches. The comparison of data provided new insights about putative druggable sites that might be employed for further docking simulations on druggable surfaces of Nsps13, in a scenario of repurposing drugs.https://www.mdpi.com/2218-273X/12/4/482COVID-19Nsp13binding site predictionprotein structureFTMapSiteMap |
| spellingShingle | Federico Ricci Rosaria Gitto Giovanna Pitasi Laura De Luca In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets Biomolecules COVID-19 Nsp13 binding site prediction protein structure FTMap SiteMap |
| title | In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets |
| title_full | In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets |
| title_fullStr | In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets |
| title_full_unstemmed | In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets |
| title_short | In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets |
| title_sort | in silico insights towards the identification of sars cov 2 nsp13 helicase druggable pockets |
| topic | COVID-19 Nsp13 binding site prediction protein structure FTMap SiteMap |
| url | https://www.mdpi.com/2218-273X/12/4/482 |
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