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Prediction of Proteins Associated with COVID-19 Based Ligand Designing and Molecular Modeling

Majid Monajjemi1,*, Rahim Esmkhani2, Fatemeh Mollaamin1, Sara Shahriari3

1 Department of Chemical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, 14987-54891, Iran
2 Department of Chemistry, Khoy Branch, Islamic Azad University, Khoy, 58159-88838, Iran
3 Department of Chemistry, Central Tehran Branch, Islamic Azad University, Tehran, 1498754891, Iran

* Corresponding Author: Majid Monajjemi. Email: email

(This article belongs to this Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)

Computer Modeling in Engineering & Sciences 2020, 125(3), 907-926.


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.


Cite This Article

Monajjemi, M., Esmkhani, R., Mollaamin, F., Shahriari, S. (2020). Prediction of Proteins Associated with COVID-19 Based Ligand Designing and Molecular Modeling. CMES-Computer Modeling in Engineering & Sciences, 125(3), 907–926.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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