Studying the Effects of ACE2 Mutations on the Stability, Dynamics, and Dissociation Process of SARS-CoV-2 S1/hACE2 Complexes

Pattern of SARS-CoV-2 variant B.1.1.519 emergence in Alaska

Alaska has the lowest population density in the United States (US) with a mix of urban centers and isolated rural communities. Alaska's distinct population dynamics compared to the contiguous US may have contributed to unique patterns of SARS-CoV-2 variants observed in early 2021. Here we examined 2323 SARS-CoV-2 genomes from Alaska and 278,635 from the contiguous US collected from December 2020 through June 2021 because of the notable emergence and spread of lineage B.1.1.519 in Alaska. We found that B.1.1.519 was consistently detected from late January through June of 2021 in Alaska with a peak prevalence in April of 77.9% unlike the rest of the US at 4.6%. The earlier emergence of B.1.1.519 coincided with a later peak of Alpha (B.1.1.7) compared to the contiguous US. We also observed differences in variant composition over time between the two most populated regions of Alaska and a modest increase in COVID-19 cases during the peak incidence of B.1.1.519. However, it is difficult to disentangle how social dynamics conflated changes in COVID-19 during this time. We suggest that the viral characteristics, such as amino acid substitutions in the spike protein, likely contributed to the unique spread of B.1.1.519 in Alaska.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged “ensemble” estimates for groups of residues when multiple structures are available.

Characterization of ACE2 naturally occurring missense variants: impact on subcellular localization and trafficking

Background

Human angiotensin-converting enzyme 2 (ACE2), a type I transmembrane receptor physiologically acting as a carboxypeptidase enzyme within the renin-angiotensin system (RAS), is a critical mediator of infection by several severe acute respiratory syndrome (SARS) corona viruses. For instance, it has been demonstrated that ACE2 is the primary receptor for the SARS-CoV-2 entry to many human cells through binding to the viral spike S protein. Consequently, genetic variability in ACE2 gene has been suggested to contribute to the variable clinical manifestations in COVID-19. Many of those genetic variations result in missense variants within the amino acid sequence of ACE2. The potential effects of those variations on binding to the spike protein have been speculated and, in some cases, demonstrated experimentally. However, their effects on ACE2 protein folding, trafficking and subcellular targeting have not been established.

Results

In this study we aimed to examine the potential effects of 28 missense variants (V801G, D785N, R768W, I753T, L731F, L731I, I727V, N720D, R710H, R708W, S692P, E668K, V658I, N638S, A627V, F592L, G575V, A501T, I468V, M383I, G173S, N159S, N149S, D38E, N33D, K26R, I21T, and S19P) distributed across the ACE2 receptor domains on its subcellular trafficking and targeting through combinatorial approach involving in silico analysis and experimental subcellular localization analysis. Our data show that none of the studied missense variants (including 3 variants predicted to be deleterious R768W, G575V, and G173S) has a significant effect on ACE2 intracellular trafficking and subcellular targeting to the plasma membrane.

Conclusion

Although the selected missense variants display no significant change in ACE2 trafficking and subcellular localization, this does not rule out their effect on viral susceptibility and severity. Further studies are required to investigate the effect of ACE2 variants on its expression, binding, and internalization which might explain the variable clinical manifestations associated with the infection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00411-1.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Analysis of the docking property of host variants of hACE2 for SARS-CoV-2 in a large cohort

The recent novel coronavirus disease (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is threatening global health. However, an understanding of the interaction of SARS-CoV-2 with human cells, including the physical docking property influenced by the host's genetic diversity, is still lacking. Here, based on germline variants in the UK Biobank covering 502,543 individuals, we revealed the molecular interactions between human angiotensin-converting enzyme 2 (hACE2), which is the representative receptor for SARS-CoV-2 entry, and COVID-19 infection. We identified six nonsense and missense variants of hACE2 from 2585 subjects in the UK Biobank covering 500000 individuals. Using our molecular dynamics simulations, three hACE2 variants from 2585 individuals we selected showed higher levels of binding free energy for docking in the range of 1.44-3.69 kcal/mol. Although there are diverse contributors to SARS-CoV-2 infections, including the mobility of individuals, we analyzed the diagnosis records of individuals with these three variants of hACE2. Our molecular dynamics simulations combined with population-based genomic data provided an atomistic understanding of the interaction between hACE2 and the spike protein of SARS-CoV-2.

The Impact of ACE2 Polymorphisms on COVID-19 Disease: Susceptibility, Severity, and Therapy

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has currently spread worldwide, leading to high morbidity and mortality. As the putative receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2) is widely distributed in various tissues and organs of the human body. Simultaneously, ACE2 acts as the physiological counterbalance of ACE providing homeostatic regulation of circulating angiotensin II levels. Given that some ACE2 variants are known to cause an increase in the ligand-receptor affinity, their roles in acquisition, progression and severity of COVID-19 disease have aroused widespread concerns. Therefore, we summarized the latest literature and explored how ACE2 variants and epigenetic factors influence an individual’s susceptibility to SARS-CoV-2 infection and disease outcome in aspects of ethnicity, gender and age. Meanwhile, the possible mechanisms for these phenomena were discussed. Notably, recombinant human ACE2 and ACE2-derived peptides may have special benefits for combating SARS-CoV-2 variants and further studies are warranted to confirm their effects in later stages of the disease process. As the uncertainty regarding the severity and transmissibility of disease rises, a more in-depth understanding of the host genetics and functional characteristics of ACE2 variants will not only help explain individual clinical differences of the disease, but also contribute to providing effective measures to develop solutions and manage future outbreaks of SARS-CoV-2.

Strong Association of Angiotensin Converting Enzyme-2 Gene Insertion/Deletion Polymorphism with Susceptibility to SARS-CoV-2, Hypertension, Coronary Artery Disease and COVID-19 Disease Mortality

BACKGROUND

The ongoing outbreak of SARS-CoV-2 represents a significant challenge to international health. Several reports have highlighted the importance of ACE2 on the pathogenesis of COVID-19. The spike protein of SARS-CoV-2 efficiently binds to the angiotensin-converting enzyme 2 (ACE2) receptors and facilitates virus entry into the host cell. In the present study, we hypothesize that a functional insertion/deletion polymorphism-rs4646994 I/D and rs4240157 T > C in the ACE gene could be associated with SARS-CoV-2 infection and mortality.

METHODOLOGY

This study included 117 consecutive COVID-19 patients and 150 age matched healthy controls (ACE2-rs4646994 I/D) and 100 age matched healthy controls with ACE2 rs4240157 T > C. We used Mutation specific PCR (MSP) for ACE2-rs4646994 I/D genotyping and amplification refractory mutation system (ARMS-PCR) for ACE2 rs4240157 T > C genotyping.

RESULTS

Results indicated that there were significant differences in the genotype distributions of ACE2-rs4646994 I/D polymorphisms (p < 0.030) and ACE2 rs4240157 T > C between COVID-19 patients and controls (p-values < 0.05). Higher frequency of DD genotype (48.71%) and D allele (0.67) was reported in COVID-19 patients than controls. Our results showed that the ACE2-DD genotype was strongly associated with increased COVID-19 severity (OR 2.37 (95%) CI = (1.19-4.70), RR = 1.39 (1.09-1.77), p < 0.013) and also a strong association was seen with ACE2-ID genotype with COVID-19 severity (OR 2.20 (95%) CI = (1.08-4.46), p < 0.020) in the codominant model. In allelic comparison, the D allele was strongly associated with COVID-19 severity (OR 1.58 (95% CI) (1.11-2.27), RR 1.21 (1.05-1.41) p < 0.010). A significant correlation of ACE2-I/D genotypes was reported with Age (p < 0.035), T2D (p < 0.0013), hypertension (p < 0.0031) and coronary artery disease (p < 0.0001). Our results indicated ACE2-DD genotype was strongly associated with increased COVID-19 mortality (OR 8.25 (95%) CI = (2.40 to 28.34), p < 0.008) and also ACE2-DD + DI genotype was strongly associated with increased COVID-19 mortality with OR 4.74 (95%) CI = (1.5214 to 14.7915), p < 0.007. A significant correlation was reported between COVID-19 patients and age matched controls (p < 0.0007). Higher frequency of heterozygosity TC (40%) followed by ACE2-CC genotype (24.78%) was reported among COVID-19 patients. Using multivariate analysis, ACE2-CT genotype was strong associated with SARS-CoV-2 severity with an OR 2.18 (95% CI) (1.92-3.99), p < 0.010 and also ACE2-CC genotype was linked with COVID-19 severity with an OR 2.66 (95% CI) (1.53-4.62), p < 0.005. A significant correlation of ACE2-T > C genotypes was reported with gender (p < 0.04), T2D (p < 0.035). ACE2-CC genotype was strongly associated with increased COVID-19 mortality OR 3.66 (95%) CI = (1.34 to 9.97), p < 0.011 and also ACE2-C allele was associated with COVID-19 mortality OR 2, 01 (1.1761-3.45), p < 0.010.

CONCLUSIONS

It is concluded that ACE-DD genotype and D allele was strongly associated with increased COVID-19 patient severity. In addition, ACE I/D polymorphism were strongly associated with advanced age, diabetes and ischemic heart disease in COVID-19 patients whereas ACE-II genotype was a protective factor against the development of severe COVID-19. ACE2-DD genotype was strongly associated with increased COVID-19 mortality. Additionally, ACE2-CC and CT genotypes were strongly associated with COVID-19 severity. Therefore, our study might be useful for identifying the susceptible population groups for targeted interventions and for making relevant public health policy decisions.

Which ones, when and why should renin-angiotensin system inhibitors work against COVID-19?

The article describes the possible pathophysiological origin of COVID-19 and the crucial role of renin-angiotensin system (RAS), providing several “converging” evidence in support of this hypothesis. SARS-CoV-2 has been shown to initially upregulate ACE2 systemic activity (early phase), which can subsequently induce compensatory responses leading to upregulation of both arms of the RAS (late phase) and consequently to critical, advanced and untreatable stages of COVID-19 disease. The main and initial actors of the process are ACE2 and ADAM17 zinc-metalloproteases, which, initially triggered by SARS-CoV-2 spike proteins, work together in increasing circulating Ang 1–7 and Ang 1–9 peptides and downstream (Mas and Angiotensin type 2 receptors) pathways with anti-inflammatory, hypotensive and antithrombotic activities. During the late phase of severe COVID-19, compensatory secretion of renin and ACE enzymes are subsequently upregulated, leading to inflammation, hypertension and thrombosis, which further sustain ACE2 and ADAM17 upregulation. Based on this hypothesis, COVID-19-phase-specific inhibition of different RAS enzymes is proposed as a pharmacological strategy against COVID-19 and vaccine-induced adverse effects. The aim is to prevent the establishment of positive feedback-loops, which can sustain hyperactivity of both arms of the RAS independently of viral trigger and, in some cases, may lead to Long-COVID syndrome.

Accelerating COVID-19 Research Using Molecular Dynamics Simulation

The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Molecular basis of the new COVID-19 target neuropilin-1 in complex with SARS-CoV-2 S1 C-end rule peptide and small-molecule antagonists

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for causing the current coronavirus 2019 (COVID-19) pandemic, uses its spike (S1) protein for host cell attachment and entry. Apart from angiotensin-converting enzyme 2, neuropilin-1 (NRP1) has been recently found to serve as another host factor for SARS-CoV-2 infection; thus, blocking S1-NRP1 interaction can be a potential treatment for COVID-19. Herein, molecular recognition between SARS-CoV-2 S1 C-end rule (CendR) heptapeptide including small-molecule antagonists (EG00229 and EG01377) and the NRP1 was investigated using molecular dynamics simulations and binding free energy calculations based on MM-PBSA method. The binding affinity and the number of hot-spot residues of EG01377/NRP1 complex were higher than those of CendR/NRP1 and EG00229/NRP1 systems, in line with the reported experimental data as well as with the lower water accessibility at the ligand-binding site. The (i) T316, P317, and D320 and (ii) S346, T349, and Y353 residues of NRP1 were confirmed to respectively form H-bonds with the positively charged guanidinium group and the negatively charged carboxyl moiety of all studied ligands. Moreover, Rosetta protein design was employed to improve the binding affinity between CendR peptide and NRP1. The newly designed peptides, especially R683G and A684M, exhibited higher binding efficiency than the native CendR heptapeptide as well as the small-molecule EG00229 by forming more H-bonds and hydrophobic interactions with NPR1, suggesting that these designed peptides could be promising NRP1 inhibitors to combat SARS-CoV-2 infection.

Upregulation of the Renin-Angiotensin System Pathways and SARS-CoV-2 Infection: The Rationale for the Administration of Zinc-Chelating Agents in COVID-19 Patients

The article describes the rationale for the administration of zinc-chelating agents in COVID-19 patients. In a previous work I have highlighted that the binding of the SARS-CoV spike proteins to the zinc-metalloprotease ACE2 has been shown to induce ACE2 shedding by activating the zinc-metalloprotease ADAM17, which ultimately leads to systemic upregulation of ACE2 activity. Moreover, based on experimental models, it was also shown the detrimental effect of the excessive systemic activity of ACE2 through its downstream pathways, which leads to "clinical" manifestations resembling COVID-19. In this regard, strong upregulation of circulating ACE2 activity was recently reported in COVID-19 patients, thus supporting the previous hypothesis that COVID-19 may derive from upregulation of ACE2 activity. Based on this, a reasonable hypothesis of using inhibitors that curb the upregulation of both ACE2 and ADAM17 zinc-metalloprotease activities and consequent positive feedback-loops (initially triggered by SARS-CoV-2 and subsequently sustained independently on viral trigger) is proposed as therapy for COVID-19. In particular, zinc-chelating agents such as citrate and ethylenediaminetetraacetic acid (EDTA) alone or in combination are expected to act in protecting from COVID-19 at different levels thanks to their both anticoagulant properties and inhibitory activity on zinc-metalloproteases. Several arguments are presented in support of this hypothesis and based on the current knowledge of both beneficial/harmful effects and cost/effectiveness, the use of chelating agents in the prevention and therapy of COVID-19 is proposed. In this regard, clinical trials (currently absent) employing citrate/EDTA in COVID-19 are urgently needed in order to shed more light on the efficacy of zinc chelators against SARS-CoV-2 infection in vivo.

Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

Molecular docking simulation reveals ACE2 polymorphisms that may increase the affinity of ACE2 with the SARS-CoV-2 Spike protein

There is increasing evidence that ACE2 gene polymorphism can modulate the interaction between ACE2 and the SARS-CoV-2 spike protein affecting the viral entry into the host cell, and/or contribute to lung and systemic damage in COVID-19. Here we used in silico molecular docking to predict the effects of ACE2 missense variants on the interaction with the spike protein of SARS-CoV-2. HDOCK and FireDock simulations identified 6 ACE2 missense variants (I21T, A25T, K26R, E37K, T55A, E75G) with higher affinity for SARS-CoV-2 Spike protein receptor binding domain (RBD) with respect to wild type ACE2, and 11 variants (I21V, E23K, K26E, T27A, E35K, S43R, Y50F, N51D, N58H, K68E, M82I) with lower affinity. This result supports the hypothesis that ACE2 genetic background may represent the first "genetic gateway" during the disease progression.

COVID-19: The Effect of Host Genetic Variations on Host-Virus Interactions

Spurred into action by the COVID-19 pandemic, the global scientific community has, in a short of period of time, made astonishing progress in understanding and combating COVID-19. Given the known human protein machinery for (a) SARS-CoV-2 entry, (b) the host innate immune response, and (c) virus-host interactions (protein-protein and RNA-protein), the potential effects of human genetic variation in this machinery, which may contribute to clinical differences in SARS-CoV-2 pathogenesis and help determine individual risk for COVID-19 infection, are explored. The Genome Aggregation Database (gnomAD) was used to show that several rare germline exome variants of proteins in these pathways occur in the human population, suggesting that carriers of these rare variants (especially for proteins of innate immunity pathways) are at risk for severe symptoms (like the severe symptoms in patients who are known to be rare variant carriers), whereas carriers of other variants could have a protective advantage against infection. The occurrence of genetic variation is thus expected to motivate the experimental probing of natural variants to understand the mechanistic differences in SARS-CoV-2 pathogenesis from one individual to another.