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Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2

View ORCID ProfileXiaoqiang Huang, Robin Pearce, View ORCID ProfileYang Zhang
doi: https://doi.org/10.1101/2020.03.28.013607
Xiaoqiang Huang
1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
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Robin Pearce
1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
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Yang Zhang
1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
2Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
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  • For correspondence: zhng@umich.edu
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ABSTRACT

The outbreak of COVID-19 has now become a global pandemic and it continues to spread rapidly worldwide, severely threatening lives and economic stability. Making the problem worse, there is no specific antiviral drug that can be used to treat COVID-19 to date. SARS-CoV-2 initiates its entry into human cells by binding to angiotensin-converting enzyme 2 (hACE2) via the receptor binding domain (RBD) of its spike protein. Therefore, molecules that can block SARS-CoV-2 from binding to hACE2 may potentially prevent the virus from entering human cells and serve as an effective antiviral drug. Based on this idea, we designed a series of peptides that can strongly bind to SARS-CoV-2 RBD in computational experiments. Specifically, we first constructed a 31-mer peptidic scaffold by linking two fragments grafted from hACE2 (a.a. 22-44 and 351-357) with a linker glycine, and then redesigned the peptide sequence to enhance its binding affinity to SARS-CoV-2 RBD. Compared with several computational studies that failed to identify that SARS-CoV-2 shows higher binding affinity for hACE2 than SARS-CoV, our protein design scoring function, EvoEF2, makes a correct identification, which is consistent with the recently reported experimental data, implying its high accuracy. The top designed peptide binders exhibited much stronger binding potency to hACE2 than the wild-type (−53.35 vs. −46.46 EvoEF2 energy unit for design and wild-type, respectively). The extensive and detailed computational analyses support the high reasonability of the designed binders, which not only recapitulated the critical native binding interactions but also introduced new favorable interactions to enhance binding. Due to the urgent situation created by COVID-19, we share these computational data to the community, which should be helpful to develop potential antiviral peptide drugs to combat this pandemic.

Footnotes

  • https://zhanglab.ccmb.med.umich.edu/EvoEF/COVID-19

  • Abbreviations

    ACE2
    angiotensin-converting enzyme 2
    COVID-19
    coronavirus disease 2019
    Cryo-EM
    cryogenic electron microscopy
    EEU
    EvoEF2 energy unit
    hACE2
    human angiotensin-converting enzyme 2
    MSA
    multiple sequence alignment
    NIL
    non-redundant interface library
    PDB
    protein data bank
    PPI
    protein-protein interaction
    PSSM
    position specific scoring matrix
    RBD
    receptor binding domain
    RBM
    receptor binding motif
    SAMC
    simulated annealing Monte Carlo
    SARS-CoV
    severe acute respiratory syndrome coronavirus
    SARS-CoV-2
    severe acute respiratory syndrome coronavirus 2
  • Copyright 
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    Posted March 31, 2020.
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    Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2
    Xiaoqiang Huang, Robin Pearce, Yang Zhang
    bioRxiv 2020.03.28.013607; doi: https://doi.org/10.1101/2020.03.28.013607
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    Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2
    Xiaoqiang Huang, Robin Pearce, Yang Zhang
    bioRxiv 2020.03.28.013607; doi: https://doi.org/10.1101/2020.03.28.013607

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