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Knowledge-Based Structural Models of SARS-CoV-2 Proteins and Their Complex with Potential Drugs

preprint
submitted on 24.03.2020 and posted on 24.03.2020 by Atsushi Hijikata, Clara Shionyu-Mitsuyama, Setsu Nakae, Masafumi Shionyu, Motonori Ota, Shigehiko Kanaya, Tsuyoshi Shirai
The World Health Organization (WHO) has declared a pandemic of the 2019 novel cornavirus SARS-CoV-2 infection (COVID-19). There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery of repurposing drugs against this disease, knowledge-based models of SARS-CoV-2 proteins were constructed using MODELLER software, and their models were refined by PHENIX and COOT. The model quality was assessed with MolProbity. The ligand molecules in the template structures were compared with approved/experimental drugs and components of natural medicines from the KEGG and KNApSAcK databases. The models suggested several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, as potential drugs for COVID-19.

Funding

the Platform Project for Supporting Drug Discovery and Life Science Research (Basis of Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED (JP20am0101111 and JP20am0101069).

History

Email Address of Submitting Author

t_shirai@nagahama-i-bio.ac.jp

Institution

Nagahama Institute of Bio-Science and Technology

Country

Japan

ORCID For Submitting Author

0000-0002-2506-5738

Declaration of Conflict of Interest

The authors declare no competing interests.

Version Notes

version 1.0

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