Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that the recognition of the binding ACE2 proteins is the first step for the coronaviruses to invade host cells, we proposed a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated on the modeled Spike-RBD/ACE2 complex structures correlate closely with the effectiveness of animal infections as determined by multiple experimental datasets. Built on the optimized binding affinity cutoff, we suggested a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggested a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation; but more importantly, it proposed a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

Identifying zoonotic origin of SARS-CoV-2 by modeling the binding affinity between Spike receptor-binding domain and host ACE2