Fast assessment of human receptor-binding capability of 2019 novel coronavirus (2019-nCoV)

  1. Qiang Huang
  2. Andreas Herrmann

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Abstract

The outbreaks of 2002/2003 SARS, 2012/2015 MERS and 2019/2020 Wuhan respiratory syndrome clearly indicate that genome evolution of an animal coronavirus (CoV) may enable it to acquire human transmission ability, and thereby to cause serious threats to global public health. It is widely accepted that CoV human transmission is driven by the interactions of its spike protein (S-protein) with human receptor on host cell surface; so, quantitative evaluation of these interactions may be used to assess the human transmission capability of CoVs. However, quantitative methods directly using viral genome data are still lacking. Here, we perform large-scale protein-protein docking to quantify the interactions of 2019-nCoV S-protein receptor-binding domain (S-RBD) with human receptor ACE2, based on experimental SARS-CoV S-RBD-ACE2 complex structure. By sampling a large number of thermodynamically probable binding conformations with Monte Carlo algorithm, this approach successfully identified the experimental complex structure as the lowest-energy receptor-binding conformations, and hence established an experiment-based strength reference for evaluating the receptor-binding affinity of 2019-nCoV via comparison with SARS-CoV. Our results show that this binding affinity is about 73% of that of SARS-CoV, supporting that 2019-nCoV may cause human transmission similar to that of SARS-CoV. Thus, this study presents a method for rapidly assessing the human transmission capability of a newly emerged CoV and its mutant strains, and demonstrates that post-genome analysis of protein-protein interactions may provide early scientific guidance for viral prevention and control.

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    SciScore for 10.1101/2020.02.01.930537: (What is this?)

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    This provides a very solid basis for using this approach to investigate the S-RBD-ACE2 interactions of 2019-nCoV, in which no experimental complex structure is available because of time limitation, but only the first-determined genome data that can be used to predict the 2019-nCoV S-RBD structure. The successful identification of the experimental complex structure as the lowest-energy binding conformations also validates the accuracy of the used Rosetta all-atom energy function(Alford et al., 2017) for quantifying the interactions of S-RBD with ACE2. As we will show, we could use the thermodynamic average of the binding energy scores of these conformations as an experiment-based energy reference to characterize the interactions of the 2019-nCoV S-RBD binding to ACE2, and then to evaluate the relative strength with respect to that of SARS-CoV. Such a comparison assessment avoids using the calculated, absolute free-energy scores to correlate with the binding strength, which might systematically differ from the actual measurement values because of the intrinsic, theoretical approximations of the used Rosetta all-atom energy function(Alford et al., 2017). We want to point out that all the docking simulations for SARS-CoV and 2019-nCoV use the same protocols and a consistent all-atom energy function. So, even the calculated energy scores contain certain errors caused by theoretical approximations, determination of the relative binding strength by comparing the calculated energy sc...

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1101/2020.02.01.930537v2 on bioRxiv
  3. Version published to 10.1101/2020.02.01.930537v1 on bioRxiv