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Prediction of proteins associated with covid-19 based ligand designing and molecular modeling   
Yazarlar
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
Angiotensin converting enzyme-2 | COVID-19 | Gillan's leaves plants | Protease domain | Receptor binding domain
Makale Türü Özgün Makale
Makale Alt Türü SCOPUS dergilerinde yayımlanan tam makale
Dergi Adı CMES - Computer Modeling in Engineering and Sciences
Dergi ISSN 1526-1492
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 12-2020
Cilt No 125
Sayı 3
Sayfalar 907 / 926
Doi Numarası 10.32604/cmes.2020.012846