In silico mutagenesis of human ACE2 with S protein and translational efficiency explain SARS-CoV-2 infectivity in different species

Abstract

The coronavirus disease COVID-19 constitutes the most severe pandemic of the last decades having caused more than 1 million deaths worldwide. The SARS-CoV-2 virus recognizes the angiotensin converting enzyme 2 (ACE2) on the surface of human cells through its spike protein. It has been reported that the coronavirus can mildly infect cats, and ferrets, and perhaps dogs while not pigs, mice, chicken and ducks. Differences in viral infectivity among different species or individuals could be due to amino acid differences at key positions of the host proteins that interact with the virus, the immune response, expression levels of host proteins and translation efficiency of the viral proteins among other factors. Here, first we have addressed the importance that sequence variants of different animal species, human individuals and virus isolates have on the interaction between the RBD domain of the SARS-CoV-2 spike S protein and human angiotensin converting enzyme 2 (ACE2). Second, we have looked at viral translation efficiency by using the tRNA adaptation index. We find that integration of both interaction energy with ACE2 and translational efficiency explains animal infectivity. Humans are the top species in which SARS-CoV-2 is both efficiently translated as well as optimally interacting with ACE2. We have found some viral mutations that increase affinity for hACE and some hACE2 variants affecting ACE2 stability and virus binding. These variants suggest that different sensitivities to coronavirus infection in humans could arise in some cases from allelic variability affecting ACE2 stability and virus binding.

Fig 1. Structural description of the ACE2-S protein complex.

(A) Complex between the soluble ACE2 protein and a domain of the S protein from the virus (S1 domain grey/ACE2 blue). (B) ACE2 surfaces for all models described (6m0j, 6lzg, 6vw1, and civet-human ACE2 chimaera: 3doi), 9 predicted conserved water clusters are shown in different colors. (C) Water prediction over the ACE2/SARS-CoV-2 S protein contact surface using 6m0j structure and contribution to binding energies as determined by FoldX. (D) Atomic detail of 6m0j with the water molecules corresponding to the nine conserved clusters showing the protein residues interacting with them. S1 domain (backbone in gray) and ACE2 (backbone in blue). In S6 Table we show the coordinates of the predicted water molecules for complex 6moj.

Fig 2. Binding affinities of animal species.

(A) ACE2 full sequence alignment of the selected species for the binding region to the S protein (B) Global ΔΔG interaction for different species by adding single residue contributions with respect to hACE2. Green bars for species susceptible to be infected, red bars for species not infected. (C) Per-residue interaction ΔΔG values in kcal/mol with respect to the hACE2 residues. We don't show the results for K353 in this table since it is not on the helical interface and it is only mutated to His in mice.

Fig 3. SARS-CoV-2 translational efficiency and ACE2 expression across species.

(A) Codon Adaptation Index (CAI) of all viral proteins across different species. (B) tRNA Adaptation Index (tAI) of all viral proteins across different species. Boxes expand from the first to the third quartile, with the center values indicating the median. The whiskers define a confidence interval of median ± 1.58*IQR/sqrt(n). (C) Median ACE2 gene expression in lung tissues of each species, normalized by the house-keeping gene ACTB. RNA-seq expression levels were retrieved from Sun et al. (2020) or from the Bgee database [42].

Fig 4. ACE2 human variants that affect ACE2/SARS-CoV-2 S protein complex interaction energy for 6m0j.

G326E increases hACE2 affinity for S protein whereas T27A decreases it by means of H bonding and water bridge creation or deletion without having a significant change in hACE2 stability.