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Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

Identifieur interne : 000394 ( Pmc/Checkpoint ); précédent : 000393; suivant : 000395

Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

Auteurs : Canrong Wu [République populaire de Chine] ; Yang Liu [République populaire de Chine] ; Yueying Yang [République populaire de Chine] ; Peng Zhang [République populaire de Chine] ; Wu Zhong [République populaire de Chine] ; Yali Wang [République populaire de Chine] ; Qiqi Wang [République populaire de Chine] ; Yang Xu [République populaire de Chine] ; Mingxue Li [République populaire de Chine] ; Xingzhou Li [République populaire de Chine] ; Mengzhu Zheng [République populaire de Chine] ; Lixia Chen [République populaire de Chine] ; Hua Li [République populaire de Chine]

Source :

RBID : PMC:7102550

Abstract

SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.


Url:
DOI: 10.1016/j.apsb.2020.02.008
PubMed: 32292689
PubMed Central: 7102550


Affiliations:


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PMC:7102550

Le document en format XML

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<name sortKey="Wang, Yali" sort="Wang, Yali" uniqKey="Wang Y" first="Yali" last="Wang">Yali Wang</name>
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<name sortKey="Wang, Qiqi" sort="Wang, Qiqi" uniqKey="Wang Q" first="Qiqi" last="Wang">Qiqi Wang</name>
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<name sortKey="Xu, Yang" sort="Xu, Yang" uniqKey="Xu Y" first="Yang" last="Xu">Yang Xu</name>
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<name sortKey="Li, Mingxue" sort="Li, Mingxue" uniqKey="Li M" first="Mingxue" last="Li">Mingxue Li</name>
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<name sortKey="Li, Xingzhou" sort="Li, Xingzhou" uniqKey="Li X" first="Xingzhou" last="Li">Xingzhou Li</name>
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</placeName>
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<name sortKey="Zheng, Mengzhu" sort="Zheng, Mengzhu" uniqKey="Zheng M" first="Mengzhu" last="Zheng">Mengzhu Zheng</name>
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<name sortKey="Li, Hua" sort="Li, Hua" uniqKey="Li H" first="Hua" last="Li">Hua Li</name>
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<series>
<title level="j">Acta Pharmaceutica Sinica. B</title>
<idno type="ISSN">2211-3835</idno>
<idno type="eISSN">2211-3843</idno>
<imprint>
<date when="2020">2020</date>
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<div type="abstract" xml:lang="en">
<p>SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further
<italic>in vitro</italic>
and
<italic>in vivo</italic>
studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.</p>
</div>
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</TEI>
<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Acta Pharm Sin B</journal-id>
<journal-id journal-id-type="iso-abbrev">Acta Pharm Sin B</journal-id>
<journal-title-group>
<journal-title>Acta Pharmaceutica Sinica. B</journal-title>
</journal-title-group>
<issn pub-type="ppub">2211-3835</issn>
<issn pub-type="epub">2211-3843</issn>
<publisher>
<publisher-name>Elsevier</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">32292689</article-id>
<article-id pub-id-type="pmc">7102550</article-id>
<article-id pub-id-type="publisher-id">S2211-3835(20)30299-9</article-id>
<article-id pub-id-type="doi">10.1016/j.apsb.2020.02.008</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" id="au1">
<name>
<surname>Wu</surname>
<given-names>Canrong</given-names>
</name>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="fn1" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author" id="au2">
<name>
<surname>Liu</surname>
<given-names>Yang</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="fn1" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author" id="au3">
<name>
<surname>Yang</surname>
<given-names>Yueying</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="fn1" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author" id="au4">
<name>
<surname>Zhang</surname>
<given-names>Peng</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au5">
<name>
<surname>Zhong</surname>
<given-names>Wu</given-names>
</name>
<xref rid="aff3" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author" id="au6">
<name>
<surname>Wang</surname>
<given-names>Yali</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au7">
<name>
<surname>Wang</surname>
<given-names>Qiqi</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au8">
<name>
<surname>Xu</surname>
<given-names>Yang</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au9">
<name>
<surname>Li</surname>
<given-names>Mingxue</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author" id="au10">
<name>
<surname>Li</surname>
<given-names>Xingzhou</given-names>
</name>
<email>xingzhouli@aliyun.com</email>
<xref rid="aff3" ref-type="aff">c</xref>
<xref rid="cor4" ref-type="corresp">∗∗∗∗</xref>
</contrib>
<contrib contrib-type="author" id="au11">
<name>
<surname>Zheng</surname>
<given-names>Mengzhu</given-names>
</name>
<email>mengzhu_zheng@hust.edu.cn</email>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="cor3" ref-type="corresp">∗∗∗</xref>
</contrib>
<contrib contrib-type="author" id="au12">
<name>
<surname>Chen</surname>
<given-names>Lixia</given-names>
</name>
<email>syzyclx@163.com</email>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="cor2" ref-type="corresp">∗∗</xref>
</contrib>
<contrib contrib-type="author" id="au13">
<name>
<surname>Li</surname>
<given-names>Hua</given-names>
</name>
<email>li_hua@hust.edu.cn</email>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="cor1" ref-type="corresp"></xref>
</contrib>
</contrib-group>
<aff id="aff1">
<label>a</label>
Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China</aff>
<aff id="aff2">
<label>b</label>
Wuya College of Innovation, Key Laboratory of Structure-Based Drug Design & Discovery, Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, China</aff>
<aff id="aff3">
<label>c</label>
National Engineering Research Center for the Emergency Drug, Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China</aff>
<author-notes>
<corresp id="cor1">
<label></label>
Corresponding authors. Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. .
<email>li_hua@hust.edu.cn</email>
</corresp>
<corresp id="cor2">
<label>∗∗</label>
Corresponding author. .
<email>syzyclx@163.com</email>
</corresp>
<corresp id="cor3">
<label>∗∗∗</label>
Corresponding authors. .
<email>mengzhu_zheng@hust.edu.cn</email>
</corresp>
<corresp id="cor4">
<label>∗∗∗∗</label>
Corresponding author. .
<email>xingzhouli@aliyun.com</email>
</corresp>
<fn id="fn1">
<label>1</label>
<p id="ntpara0010">These authors made equal contributions to this work.</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release">
<day>27</day>
<month>2</month>
<year>2020</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="epub">
<day>27</day>
<month>2</month>
<year>2020</year>
</pub-date>
<elocation-id></elocation-id>
<history>
<date date-type="received">
<day>12</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="rev-recd">
<day>17</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>18</day>
<month>2</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>© 2020 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences</copyright-holder>
<license>
<license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p>
</license>
</permissions>
<abstract id="abs0010">
<p>SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further
<italic>in vitro</italic>
and
<italic>in vivo</italic>
studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.</p>
</abstract>
<abstract abstract-type="graphical" id="abs0015">
<title>Graphical abstract</title>
<p>Twenty structures including 19 SARS-CoV-2 targets and 1 human target were built by homology modeling. Library of ZINC drug database, natural products, 78 anti-viral drugs were screened against these targets plus human ACE2. This study provides drug repositioning candidates and targets for further
<italic>in vitro</italic>
and
<italic>in vivo</italic>
studies of SARS-CoV-2.
<fig id="undfig1" position="anchor">
<alt-text id="alttext0010">Image 1</alt-text>
<graphic xlink:href="fx1_lrg"></graphic>
</fig>
</p>
</abstract>
<kwd-group id="kwrds0010">
<title>Keywords</title>
<kwd>SARS-CoV-2</kwd>
<kwd>Drug repurposing</kwd>
<kwd>Molecular docking</kwd>
<kwd>Remdesivir</kwd>
<kwd>Homology modeling</kwd>
</kwd-group>
<kwd-group id="kwrds0015">
<title>Abbreviations</title>
<kwd>3CLpro, 3-chymotrypsin-like protease</kwd>
<kwd>E, envelope</kwd>
<kwd>M, membrane protein</kwd>
<kwd>N, nucleocapsid protein</kwd>
<kwd>Nsp, non-structure protein</kwd>
<kwd>ORF, open reading frame</kwd>
<kwd>PDB, protein data bank</kwd>
<kwd>RdRp, RNA-dependence RNA polymerase</kwd>
<kwd>S, Spike</kwd>
<kwd>SUD, SARS unique domain</kwd>
<kwd>UB, ubiquitin-like domain</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>République populaire de Chine</li>
</country>
<region>
<li>Hubei</li>
</region>
<settlement>
<li>Pékin</li>
<li>Wuhan</li>
</settlement>
</list>
<tree>
<country name="République populaire de Chine">
<region name="Hubei">
<name sortKey="Wu, Canrong" sort="Wu, Canrong" uniqKey="Wu C" first="Canrong" last="Wu">Canrong Wu</name>
</region>
<name sortKey="Chen, Lixia" sort="Chen, Lixia" uniqKey="Chen L" first="Lixia" last="Chen">Lixia Chen</name>
<name sortKey="Li, Hua" sort="Li, Hua" uniqKey="Li H" first="Hua" last="Li">Hua Li</name>
<name sortKey="Li, Hua" sort="Li, Hua" uniqKey="Li H" first="Hua" last="Li">Hua Li</name>
<name sortKey="Li, Mingxue" sort="Li, Mingxue" uniqKey="Li M" first="Mingxue" last="Li">Mingxue Li</name>
<name sortKey="Li, Xingzhou" sort="Li, Xingzhou" uniqKey="Li X" first="Xingzhou" last="Li">Xingzhou Li</name>
<name sortKey="Liu, Yang" sort="Liu, Yang" uniqKey="Liu Y" first="Yang" last="Liu">Yang Liu</name>
<name sortKey="Wang, Qiqi" sort="Wang, Qiqi" uniqKey="Wang Q" first="Qiqi" last="Wang">Qiqi Wang</name>
<name sortKey="Wang, Yali" sort="Wang, Yali" uniqKey="Wang Y" first="Yali" last="Wang">Yali Wang</name>
<name sortKey="Xu, Yang" sort="Xu, Yang" uniqKey="Xu Y" first="Yang" last="Xu">Yang Xu</name>
<name sortKey="Yang, Yueying" sort="Yang, Yueying" uniqKey="Yang Y" first="Yueying" last="Yang">Yueying Yang</name>
<name sortKey="Zhang, Peng" sort="Zhang, Peng" uniqKey="Zhang P" first="Peng" last="Zhang">Peng Zhang</name>
<name sortKey="Zheng, Mengzhu" sort="Zheng, Mengzhu" uniqKey="Zheng M" first="Mengzhu" last="Zheng">Mengzhu Zheng</name>
<name sortKey="Zhong, Wu" sort="Zhong, Wu" uniqKey="Zhong W" first="Wu" last="Zhong">Wu Zhong</name>
</country>
</tree>
</affiliations>
</record>

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