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Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 24 Mar 2020 English Publisher:WileyJournal:FEBS Letters, volume 594, pages 1,960-1,973 (issn: 0014-5793, eissn: 1873-3468,

Authors: Atsushi Hijikata; Clara Shionyu-Mitsuyama; Setsu Nakae; Masafumi Shionyu; Motonori Ota; Shigehiko Kanaya; Tsuyoshi Shirai;

doi: 10.1002/1873-3468.13806 , 10.26434/chemrxiv.12021330.v1

pmid: 32379896

pmc: PMC7267562

Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs

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Abstract

The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID‐19) caused by the novel coronavirus SARS‐CoV‐2 a pandemic. There is, however, no confirmed anti‐COVID‐19 therapeutic currently. In order to assist structure‐based discovery efforts for repurposing drugs against this disease, we constructed knowledge‐based models of SARS‐CoV‐2 proteins and compared the ligand molecules in the template structures with approved/experimental drugs and components of natural medicines. Our theoretical models suggest several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, that could be further investigated for their potential for treating COVID‐19.

Related Organizations

Nagoya University
Japan
Japan Science and Technology Agency
Japan
Nagahama Institute of Bio-Science and Technology
Japan
Nara Institute of Science and Technology
Japan

Keywords

Models, Molecular, Protein Conformation, SARS-CoV-2, Biophysics, Cell Biology, Biochemistry, Antiviral Agents, Betacoronavirus, Viral Proteins, Structural Biology, Genetics, Molecular Biology

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	21
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	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%