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The discovery of potential natural products for targeting SARS-CoV-2 spike protein by virtual screening
Yang-Chang Wu; Guan-Yu Chen; Tsung-You Yao; Azaj Ahmed; Yi-Cheng Pan; Juan-Cheng Yang.
Affiliation
  • Yang-Chang Wu; China Medical University
  • Guan-Yu Chen; China Medical University Hospital
  • Tsung-You Yao; Kaohsiung Medical University
  • Azaj Ahmed; China Medical University Hospital
  • Yi-Cheng Pan; China Medical University
  • Juan-Cheng Yang; China Medical University Hospital
Preprint in English | bioRxiv | ID: ppbiorxiv-170639
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters into the cells through its spike proteins binding to human angiotensin-converting enzyme 2 (ACE2) protein and causes virus infection in host cells. Until now, there are no available antiviral drugs have been reported that can effectively block virus infection. The study aimed to discover the potential compounds to prevent viral spike proteins to bind to the human ACE2 proteins from Taiwan Database of Extracts and Compounds (TDEC) by structure-based virtual screening. In this study, to rapidly discover potential inhibitors against SARS-CoV-2 spike proteins, the molecular docking calculation was performed by AutoDock Vina program. Herein, we found that 39 potential compounds may have good binding affinities and can respectively bind to the viral receptor-binding domain (RBD) of spike protein in the prefusion conformation and spike-ACE2 complex protein in silico. Among those compounds, especially natural products thioflexibilolide A and candidine that were respectively isolated from the soft corals Sinularia flexibilis and Phaius mishmensis may have better binding affinity than others. This study provided the predictions that these compounds may have the potential to prevent SARS-CoV-2 spike protein from entry into cells.
License
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Full text: Available Collection: Preprints Database: bioRxiv Type of study: Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: bioRxiv Type of study: Prognostic study Language: English Year: 2020 Document type: Preprint
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