Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies
SARS coronavirus 2 (SARS-CoV-2) has spread rapidly around the world and continues to have a massive global health effect, contributing to an infectious respiratory illness called coronavirus infection-19 (COVID-19). TMPRSS2 is an emerging molecular target that plays a role in the early stages of SAR...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914821002331 |
| _version_ | 1831569813838757888 |
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| author | Abdulrahim A. Alzain, PhD Fatima A. Elbadwi |
| author_facet | Abdulrahim A. Alzain, PhD Fatima A. Elbadwi |
| author_sort | Abdulrahim A. Alzain, PhD |
| collection | DOAJ |
| description | SARS coronavirus 2 (SARS-CoV-2) has spread rapidly around the world and continues to have a massive global health effect, contributing to an infectious respiratory illness called coronavirus infection-19 (COVID-19). TMPRSS2 is an emerging molecular target that plays a role in the early stages of SARS-CoV-2 infection; hence, inhibiting its activity might be a target for COVID-19. This study aims to use many computational approaches to provide compounds that could be optimized into clinical candidates. As there is no experimentally derived protein information, initially we develop the TMPRSS2 model. Then, we generate a pharmacophore model from TMPRSS2 active site consequently, and the developed models were employed for the screening of one million molecules from the Enamine database. The created model was then screened using e-pharmacophore-based screening, molecular docking, free energy estimation and molecular dynamic simulation. Also, ADMET prediction and density functional theory calculations were performed. Three potential molecules (Z126202570, Z46489368, and Z422255982) exhibited promising stable binding interactions with the target. In conclusion, these findings empower further in vitro and clinical assessment for these compounds as novel anti-COVID19 agents. |
| first_indexed | 2024-12-17T12:40:26Z |
| format | Article |
| id | doaj.art-2a97d1d7426b4c3e8abfee2ba47a4629 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-17T12:40:26Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-2a97d1d7426b4c3e8abfee2ba47a46292022-12-21T21:48:02ZengElsevierInformatics in Medicine Unlocked2352-91482021-01-0126100758Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studiesAbdulrahim A. Alzain, PhD0Fatima A. Elbadwi1Corresponding author. 21111 Barakat Street, Medani, Gezira, Sudan.; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Gezira, SudanDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Gezira, SudanSARS coronavirus 2 (SARS-CoV-2) has spread rapidly around the world and continues to have a massive global health effect, contributing to an infectious respiratory illness called coronavirus infection-19 (COVID-19). TMPRSS2 is an emerging molecular target that plays a role in the early stages of SARS-CoV-2 infection; hence, inhibiting its activity might be a target for COVID-19. This study aims to use many computational approaches to provide compounds that could be optimized into clinical candidates. As there is no experimentally derived protein information, initially we develop the TMPRSS2 model. Then, we generate a pharmacophore model from TMPRSS2 active site consequently, and the developed models were employed for the screening of one million molecules from the Enamine database. The created model was then screened using e-pharmacophore-based screening, molecular docking, free energy estimation and molecular dynamic simulation. Also, ADMET prediction and density functional theory calculations were performed. Three potential molecules (Z126202570, Z46489368, and Z422255982) exhibited promising stable binding interactions with the target. In conclusion, these findings empower further in vitro and clinical assessment for these compounds as novel anti-COVID19 agents.http://www.sciencedirect.com/science/article/pii/S2352914821002331SARS-CoV-2TMPRSS2Homology modelinge-pharmacophore mapping and screeningDockingMolecular dynamics |
| spellingShingle | Abdulrahim A. Alzain, PhD Fatima A. Elbadwi Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies Informatics in Medicine Unlocked SARS-CoV-2 TMPRSS2 Homology modeling e-pharmacophore mapping and screening Docking Molecular dynamics |
| title | Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies |
| title_full | Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies |
| title_fullStr | Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies |
| title_full_unstemmed | Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies |
| title_short | Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies |
| title_sort | identification of novel tmprss2 inhibitors for covid 19 using e pharmacophore modelling molecular docking molecular dynamics and quantum mechanics studies |
| topic | SARS-CoV-2 TMPRSS2 Homology modeling e-pharmacophore mapping and screening Docking Molecular dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S2352914821002331 |
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