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MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.
Feng, Zhiwei; Chen, Maozi; Xue, Ying; Liang, Tianjian; Chen, Hui; Zhou, Yuehan; Nolin, Thomas D; Smith, Randall B; Xie, Xiang-Qun.
  • Feng Z; University of Pittsburgh.
  • Chen M; University of Pittsburgh.
  • Xue Y; University of Pittsburgh.
  • Liang T; University of Pittsburgh.
  • Chen H; University of Pittsburgh.
  • Zhou Y; University of Pittsburgh.
  • Nolin TD; University of Pittsburgh.
  • Smith RB; University of Pittsburgh.
  • Xie XQ; University of Pittsburgh.
Brief Bioinform ; 22(2): 946-962, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1352109
ABSTRACT
Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / SARS-CoV-2 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Brief Bioinform Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / SARS-CoV-2 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Brief Bioinform Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article