Modeling study predicts SARS-CoV-2 variants with high receptor binding affinity

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed enormous challenges to the world of science. Not only is its origin a mystery, but the frequent emergence of variants of concern (VOCs) prevents the possibility of complacency with the current level of pandemic containment measures.

A new preprint, which was released on the bioRxiv* preprint server, adds to the sum of knowledge about the virus, with respect to its natural and biologically significant mutations, using computational modeling of the virus-receptor interactions. Understanding how viral mutations affect the virus-host interactions is crucial for shaping proper strategies to control the virus.

The need for this study

Genomic sequencing has been carried out since the early phase of the pandemic, resulting in the availability of hundreds of thousands of whole genomes on several databases. This indicates the existence of thousands of mutations, which need to be evaluated for their biological impact. This requires the development of methods to selectively identify those mutations that define variants of concern. Such methods can then be used to monitor the emergence of such strains and to provide a roadmap for the development of new vaccines.

SARS-CoV-2 is an enveloped virus with a ribonucleic acid (RNA) genome, which enters into the host cell via the spike protein on the virus’s surface. The spike protein comprises the S1 and S2 subunits, of which the first contains the receptor-binding domain (RBD). The host receptor for this virus is the angiotensin-converting enzyme 2 (ACE2).

On binding to the receptor, the virus and host cell fuse to accomplish viral entry. Thus, the spike protein is central to SARS-CoV-2 infectivity, and its close study is crucial for understanding how the virus adapts to different hosts and to developing specific antivirals to treat or prevent infection.

Mutations at the binding interface of SARS-CoV spike

Earlier research on the SARS-CoV virus has shown that high-affinity ACE2 binding to the spike RBD is determined by the N479 and T487 residues at the binding interface. The K31 and K353 on the ACE2 interface are affected by the 479 and 487 positions on the spike protein, respectively.

Adaptation of the SARS-CoV across species is determined by certain specific spike residues with favorable binding profiles for ACE2 molecules from different species. The human ACE2 (hACE2) receptor, for instance, favors the N479 variant, but in the palm civet, the K, N and R residues are found at this position.

Again, T487 is favored by both human and civet ACE2, but not S487 in humans. Y442 is found in human and civet spike protein, but F442 is preferred in humans. Again, L472 is found in hACE2 and P472 in civets. D480 and G480 are preferred in human and civet ACE2, respectively.

The simultaneous occurrence of Y442F and L472F mutations can be brought about to favor hACE2 adaptation very strongly. These examples show the importance of spike RBD mutations in virus-host recognition and transmission, as well as adaptation to different species.

Mutations in the SARS-CoV ACE2 receptor

Interestingly, rats do not acquire SARS-CoV because their ACE2 varies from hACE2 by one residue. The K353H mutation in hACE2 thus confers a low affinity for the spike protein in humans. This is seen with other species too.

This implies that hACE2 variants must also be studied to understand the range of spike-hACE2 interactions.

SARS-CoV-2 variants

Numerous fast-spreading VOCs of SARS-CoV-2 have been identified, including the UK, Brazil and South African variants, which appear to be more infectious than the ancestral lineage.

The D614G mutation allowed all strains containing it to become dominant the world over.

Other commonly found mutations include spike mutations E484K and N501Y. Such new variants need to be characterized to understand their impact on receptor binding and on antibody-mediated neutralization.

Prediction model

To address this, the current study uses structural modeling to screen a large number of SARS-CoV-2-hACE2 interactions, thus fast-forwarding the process of functional characterization of mutations in this region.

This body of data was used to develop a method of evaluating SARS-CoV-2 mutations at the spike-ACE2 interface. The reliability of this method was shown by its ability to duplicate already established biological effects associated with the five important mutations of the SARS-CoV mentioned above, which facilitates its leap across species barriers.

Moreover, it mirrors the reported infectivity profile of SARS-CoV-2. Specifically, it correctly predicted the infectivity results for 9 of 12 variants known to have lower infectivity than the wildtype virus, with a mean lowering by 40%.

Computational framework for predicting the effects of variants in the SARS-CoV-2 Spike/human ACE2 complex. (a) Top: SARS-CoV S-hACE2 complex showing key S interface residues and mutations that play a role in cross-species adaptation to hACE2 (listed in table below). Recognition of hACE2 by the S protein is centered at hACE2 residues K31 in the α1 helix and K353 (highlighted in blue). Bottom: Known adaptations of selected S protein mutations. (b) Computational workflow for structural assessment of variants in the S-hACE2 complex. First, genetic variants are identified bioinformatically from publicly available genome sequence data. Second, starting from their native conformations, mutations in 3D structures of component proteins are generated and the mutant complex is then assembled and refined using an energy minimization algorithm. Third, the binding affinity of each mutant S-hACE2 complex is computed using physics-based interaction energies, including solvation, van der Waals, electrostatics with ionic effects, and entropy. (c) Model of a wild-type (WT) SARS-CoV-2 S-hACE2 complex highlighting positions of modeled variants at the interface (spheres). Spike residues, yellow; hACE2 residues, orange.
Computational framework for predicting the effects of variants in the SARS-CoV-2 Spike/human ACE2 complex. (a) Top: SARS-CoV S-hACE2 complex showing key S interface residues and mutations that play a role in cross-species adaptation to hACE2 (listed in table below). Recognition of hACE2 by the S protein is centered at hACE2 residues K31 in the α1 helix and K353 (highlighted in blue). Bottom: Known adaptations of selected S protein mutations. (b) Computational workflow for structural assessment of variants in the S-hACE2 complex. First, genetic variants are identified bioinformatically from publicly available genome sequence data. Second, starting from their native conformations, mutations in 3D structures of component proteins are generated and the mutant complex is then assembled and refined using an energy minimization algorithm. Third, the binding affinity of each mutant S-hACE2 complex is computed using physics-based interaction energies, including solvation, van der Waals, electrostatics with ionic effects, and entropy. (c) Model of a wild-type (WT) SARS-CoV-2 S-hACE2 complex highlighting positions of modeled variants at the interface (spheres). Spike residues, yellow; hACE2 residues, orange.

SARS-CoV-2 spike-hACE2 interactions

Modeling of the virus spike structure suggests that this mutation favors the ‘open’ RBD conformation, promoting ACE2 binding.

The model showed that hACE2 binds to SARS-CoV-2 RBD much more strongly than the SARS-CoV RBD.

The study also shows that 31 of 731 combinations of natural mutations at the binding interface increase the binding affinity of the SARS-CoV-2 spike. These mutations, such as N440K, S443A, G476S, E484R, and G502P, are all found to be in the proximity of two opposite poles of the RBD interaction surface.

This may be due to the increased vulnerability of the exposed ends of the receptor-binding motif (RBM) within the RBD, the loop that actually binds to hACE2, lending mutations at these regions the ability to promote stable and high-affinity binding.

Why is the central part of the RBM interface spared? It could be that the hACE2 α1 ridge region facing this part of the spike does not recognize mutations in the spike.

Single spike or hACE2 mutations

Typically, naturally occurring hACE2 mutations are associated with lower binding affinity, but spike variants, notably N440K and G476S, with a larger affinity range. This is mediated by both short-range and other environment-dependent interactions.

Combinatorial preference

Combinations of mutations in both hACE2 and the spike protein occurred in 29 of the 31 high-affinity virus-host interactions, indicating that such combinations as strengthening the effects of each component are significantly more likely to emerge. If such high-affinity binding indicates increased infectivity, these predictions can be used to evaluate the expected susceptibility to a mutant spike-bearing strain of a specific hACE2 mutation.

Most double spike-hACE2 mutations with improved binding affinity display favorable energetic gains due to structural movements and conformational changes that optimize the binding interface.

Prediction of high-affinity spike-ACE2 binding

Using the knowledge of conserved residues, analysis of energy changes and structural determination, the study shows that specific single mutations at or near the binding interface change its conformation to result in high-affinity spike-hACE2 complexes, and thus to enhance the transmission of this and similar ACE2-utilizing coronaviruses.

The model predicted N440K to be an immune evasion mutation to the C134 neutralizing antibody (Nab). Earlier assays show it to be resistant to neutralization by the Nab 135, and E484K to C121.

Going by the change in charge as a predictor of resistance, the E484K mutation is similar to the predicted highest-affinity candidate, E484R. The latter has been shown, indeed, to evade the NAbs COV2-2050, COV2-2096, COV2-2479.

Both N440K and E484R overlap the epitopes of C135 and C121, respectively. These require strict surveillance.

Prediction of biology of novel variants

Notably, the recent VOCs are predicted to have high binding affinities in this model, from K417T/E484K/N501Y downwards. This variant is predicted to have similar binding efficacy to the wildtype virus, but has been reported to be spreading all over the world.

The highest increase in binding affinity, by 80-90% compared to the wildtype, was predicted with the triple mutant S477N/E484K/N501Y, which has not yet been reported anywhere.

The double mutants S477N/E484K, E484K/N501Y, as seen in the Brazil, South African, US and UK lineages, also belong to the highest affinity group.

High affinity (40% or more above wildtype) was shown with the S477N, E484K and N501Y single mutations. These are probably combined with other mutations to enhance their efficacy.

The variants defined by the combination of S477N/N501Y, K417T/E484K/N501Y, or by N439K, or Y453F, have similar affinity to the wildtype virus.

These are also resistant to neutralizing antibodies, or overlap with their binding epitopes. These predicted effects explain the rapid spread of strains with these mutations, as well as the emergence of new lineages with combinations of advantageous spike mutations.

The structural model suggests that while E484K increases the solvation energy, the very high-frequency N501Y mutation stabilizes the bond between the Y501 on the spike and the K353 on the hACE2.

Three-dimensional modeling of the Spike/hACE2 complex predicts changes in structure and binding affinity that correlate with transmissibility and therefore can help inform future intervention strategies.”

*Important Notice

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Dr. Liji Thomas

Written by

Dr. Liji Thomas

Dr. Liji Thomas is an OB-GYN, who graduated from the Government Medical College, University of Calicut, Kerala, in 2001. Liji practiced as a full-time consultant in obstetrics/gynecology in a private hospital for a few years following her graduation. She has counseled hundreds of patients facing issues from pregnancy-related problems and infertility, and has been in charge of over 2,000 deliveries, striving always to achieve a normal delivery rather than operative.

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