| Yazarlar (4) |
Majid Monajjemi
|
|
Rahim Esmkhani
|
Dr. Öğr. Üyesi Fatemeh MOLLAAMIN
Kastamonu Üniversitesi |
|
Sara Shahriari
|
| Özet |
| Current understanding about how the virus that causes COVID-19 spreads is largely based on what is known about similar coronaviruses. Some of the Natural products are suitable drugs against SARS-CoV-2 main protease. For recognizing a strong inhibitor, we have accomplished docking studies on the major virus protease with 4 natural product species as anti COVID-19 (SARS-CoV-2), namely "Vidarabine", "Cytarabine", "Gemcitabine" and "Matrine" which have been extracted from Gillan's leaves plants. These are known as Chuchaq, Trshvash, Cote-Couto and Khlvash in Iran. Among these four studied compounds, Cytarabine appears as a suitable compound with high effectiveness inhibitors to this protease. Finally by this work we present a method on the Computational Prediction of Protein Structure Associated with COVID-19 Based Ligand Design and Molecular Modeling. By this investigation, auto dock software (iGEM-DOCK) has been used and via this tool, the suitable receptors can be distinguished in whole COVID-19 component structures for forming a complex. "iGEMDOCK" is suitable to define the binding site quickly. With docking simulation and NMR investigation, we have demonstrated these compounds exhibit a suitable binding energy around 9 Kcal/mol with various ligand proteins modes in the binding to COVID-19 viruses. However, these data need further evaluation for repurposing these drugs against COVID-19 viruses, in both vivo & vitro. |
| Anahtar Kelimeler |
| COVID-19 | receptor binding domain | Gillan's leaves plants | angiotensin converting enzyme-2 | protease domain |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayımlanan tam makale |
| Dergi Adı | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES |
| Dergi ISSN | 1526-1492 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Exp, SCOPUS, Curation, Current Contents Engineering Computing & Technology, Essential Science Indicators, Pdf2xml, Pdf2xml, Reference Master, Sophia |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2020 |
| Cilt No | 125 |
| Sayı | 3 |
| Sayfalar | 907 / 926 |
| Doi Numarası | 10.32604/cmes.2020.012846 |
| Atıf Sayıları | |
| WoS | 3 |
| SCOPUS | 4 |
