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bioRxivGenomicsPreprint

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

Qiang HuangiD, Andreas HerrmanniD

PDFDOI

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.

Abstract summary

Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract · CC BY-NC-ND 4.0

Monte Carlo protein-protein docking estimates that the 2019-nCoV spike receptor-binding domain binds human ACE2 with about 73% of the affinity of SARS-CoV.

Assessing the human transmission potential of emerging coronaviruses (CoVs) depends on quantifying interactions between the viral spike protein (S-protein) and host cell surface receptors, yet quantitative methods operating directly from viral genome data have been lacking. Using an experimental SARS-CoV S-protein receptor-binding domain (S-RBD)–ACE2 complex as a reference, large-scale protein-protein docking quantifies 2019-nCoV receptor-binding affinity directly from sequence data.

Key results

  1. SuggestedMonte Carlo protein-protein docking recovers the experimental SARS-CoV S-RBD–ACE2 complex as the lowest-energy binding conformation and estimates 2019-nCoV S-RBD binding affinity to human ACE2 at roughly 73% of SARS-CoV.

    Quantified by sampling thermodynamically probable binding conformations against the experimental SARS-CoV S-RBD–ACE2 structure reference, yielding ~73% of SARS-CoV binding affinity. · Abstract

Methods, data and reproducibility

The authors performed large-scale protein-protein docking with a Monte Carlo sampling algorithm to evaluate interactions between the 2019-nCoV S-protein receptor-binding domain (S-RBD) and human ACE2, benchmarking lowest-energy conformations against the experimental SARS-CoV S-RBD–ACE2 complex structure.

  • Experimental SARS-CoV S-RBD-ACE2 complex structure and 2019-nCoV viral genome/S-RBD sequence · human, virus
Methods & tools
Monte Carlo protein-protein docking
Reproducibility gaps
Abstract only; no software links or structural database accessions are provided.
Limitations (2)
  • Results rely solely on computational protein-protein docking without experimental binding measurements for 2019-nCoV. · noted in this summary
  • The abstract does not provide structural accessions, energy scores, or docking software details. · noted in this summary

Read this if you use computational protein-protein docking to estimate receptor-binding affinity of emerging coronavirus spike variants from genomic sequences.

Citations
52

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