Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations

Severe acute respiratory syndrome Coronavirus-2 GenoAnalyzer and mutagenic anomaly detector using FCMFI and NSCE

In order to deepen our understanding of the virus and help guide the creation of efficient therapies, this study uses artificial intelligence tools to thoroughly explore the genetic sequences of the SARS-CoV-2 virus. The process starts by using the Fuzzy Closure Miner for Frequent Itemsets (FCMFI) on a large corpus of SARS-CoV-2 genomic sequences to reveal hidden patterns, including nucleotides base sequences, repeating motifs, and corresponding interchanges. Then, using the Nucleotide Sequence Comprehension Engine (NSCE) technique, we were able to precisely define the genomic areas for mutation analysis. Structured and unstructured proteins are both strongly impacted by virus mutations, with spike proteins that are linked to the severity of COVID-19 pneumonia being particularly affected. Notably, the Mutagenic Anomaly Detector shows a 65 % efficiency boost in computing genome mutation rates compared to conventional point mutation analysis, while GenoAnalyzer offers a remarkable 93.33 % improvement over existing approaches in recognizing common genomic sequence patterns. These results highlight the potential of FCMFI to reveal complex genomic patterns and significant insights in COVID-19 genetic sequences when combined with mutation analysis. The Mutagenic Anomaly Detector and GenoAnalyzer show promise for revealing hidden genomic patterns and precisely estimating the SARS-CoV-2 mutation rate.

Application scenario-oriented molecule generation platform developed for drug discovery

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides

The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein Interactions. Here, we applied a peptide design algorithm based on shape complementarity optimization and electrostatic compatibility and provided the first experimental in vitro proof of the efficacy of the design algorithm. Focusing on the interaction between the SARS-CoV-2 Spike Receptor-Binding Domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) receptor, we extracted a 23-residues long peptide that structurally mimics the major interacting portion of the ACE2 receptor and designed in silico five mutants of such a peptide with a modulated affinity. Remarkably, experimental KD measurements, conducted using biolayer interferometry, matched the in silico predictions. Moreover, we investigated the molecular determinants that govern the variation in binding affinity through molecular dynamics simulation, by identifying the mechanisms driving the different values of binding affinity at a single residue level. Finally, the peptide sequence with the highest affinity, in comparison with the wild type peptide, was expressed as a fusion protein with human H ferritin (HFt) 24-mer. Solution measurements performed on the latter constructs confirmed that peptides still exhibited the expected trend, thereby enhancing their efficacy in RBD binding. Altogether, these results indicate the high potentiality of this general method in developing potent high-affinity vectors for hindering/enhancing protein-protein associations.

Unraveling bioactive metabolites of mangroves as putative inhibitors of SARS-CoV-2 Mpro and RBD proteins: molecular dynamics and ADMET analysis

COVID-19 is a deadly pandemic caused by Corona virus leading to millions of deaths worldwide. Till today no medicine was available to cure this disease. This study selected 262 potential bioactive natural products derived from mangroves to inhibit the main protease (Mpro) and receptor-binding domain (RBD) protein of the COVID-19 virus. All the ligands were subjected to Adsorption Digestion Metabolism Excretion and Toxicity (ADMET) predictions and docking studies using AutodockVina. Among all the ligands, NP_143 (Shearinine A) and NP_242 (Amentoflavone), having the highest docking score of 10.2 and 10.1 Kj/mole, respectively, were picked for 100 ns of Molecular Dynamics using GROMACS. The trajectories generated were used to estimate Root mean square deviation (RMSD), Root mean square fluctuations (RMSF), Radius of Gyrations (RG), Solvent accessible surface area (SASA), and Hydrogen bonds. From the data generated, both the ligands have good binding ability at the active site of Mpro protein and do not deviate much. They have strong interactions with the amino acids during the 100 ns of simulations and can thus be considered potential drug candidates.Communicated by Ramaswamy H. Sarma.

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

Strain wars 5: Gibbs energies of binding of BA.1 through BA.4 variants of SARS-CoV-2

This paper reports, for the first time, standard Gibbs energies of binding of the BA.1, BA.2, BA.3, BA.2.13, BA.2.12.1 and BA.4 Omicron variants of SARS-CoV-2, to the Human ACE2 receptor. Variants BA.1 through BA.3 exhibit a trend of decreasing standard Gibbs energy of binding and hence increased infectivity. The BA.4 variant exhibits a less negative standard Gibbs energy of binding, but also more efficient evasion of the immune response. Therefore, it was concluded that all the analyzed strains evolve in accordance with expectations of the theory of evolution, albeit using different strategies.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

Omicron BA.2.75 Subvariant of SARS-CoV-2 Is Expected to Have the Greatest Infectivity Compared with the Competing BA.2 and BA.5, Due to Most Negative Gibbs Energy of Binding

Omicron BA.2.75 may become the next globally dominant strain of COVID-19 in 2022. The BA.2.75 sub-variant has acquired more mutations (9) in spike protein and other genes of SARS-CoV-2 than any other variant. Thus, its chemical composition and thermodynamic properties have changed compared with earlier variants. In this paper, the Gibbs energy of the binding and antigen-receptor binding rate was reported for the BA.2.75 variant. Gibbs energy of the binding of the Omicron BA.2.75 variant is more negative than that of the competing variants BA.2 and BA.5.

When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein-protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

Computation of the Binding Energies between Human ACE2 and Spike RBDs of the Original Strain, Delta and Omicron Variants of the SARS-CoV-2: A DFT Simulation Approach

The receptor binding domain (RBD) of SARS-CoV-2 binds to human ACE2 leading to infection. In this study, the complexes that are formed by the attachment of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are investigated via density functional theory (DFT) simulations to obtain binding energies. The DFT computations are performed without fragmenting the interfaces to involve longer-range interactions for improved accuracy, which is one of the primary features of the approach used in this study. Basis set superposition error corrections and van der Waals dispersions are also included in the DFT simulations. The binding energies of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are computed as -4.76, -6.68, and -11.77 eV, respectively. These binding energy values indicate that the binding of the omicron variant to the ACE2 is much more favorable than the binding of the original strain and the delta variant, which constitute a molecular reason for the takeover of the omicron variant. The binding energies and the decomposition of these energies found in this study are expected to aid in the development of neutralizing agents.

Electrostatic Features for the Receptor Binding Domain of SARS-COV-2 Wildtype and Its Variants. Compass to the Severity of the Future Variants with the Charge-Rule

Electrostatic intermolecular interactions are important in many aspects of biology. We have studied the main electrostatic features involved in the interaction of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with the human receptor Angiotensin-converting enzyme 2 (ACE2). As the principal computational tool, we have used the FORTE approach, capable to model proton fluctuations and computing free energies for a very large number of protein-protein systems under different physical-chemical conditions, here focusing on the RBD-ACE2 interactions. Both the wild-type and all critical variants are included in this study. From our large ensemble of extensive simulations, we obtain, as a function of pH, the binding affinities, charges of the proteins, their charge regulation capacities, and their dipole moments. In addition, we have calculated the pK_as for all ionizable residues and mapped the electrostatic coupling between them. We are able to present a simple predictor for the RBD-ACE2 binding based on the data obtained for Alpha, Beta, Gamma, Delta, and Omicron variants, as a linear correlation between the total charge of the RBD and the corresponding binding affinity. This "RBD charge rule" should work as a quick test of the degree of severity of the coming SARS-CoV-2 variants in the future.

Strain Wars: Competitive interactions between SARS-CoV-2 strains are explained by Gibbs energy of antigen-receptor binding

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

All-Atom Simulations of Human ACE2-Spike Protein RBD Complexes for SARS-CoV-2 and Some of its Variants: Nature of Interactions and Free Energy Diagrams for Dissociation of the Protein Complexes

The spike protein of SARS-CoV-2 is known to interact with the human ACE2 protein via its receptor binding domain (RBD). We have investigated the molecular nature of this interprotein interaction and the associated free energy diagrams for the unbinding of the two proteins for SARS-CoV-2 and some of its known variants through all-atom simulations. The present work involves generation and analysis of 2.5 μs of unbiased and 4.2 μs of biased molecular dynamics trajectories in total for five explicitly solvated RBD-ACE2 systems at full atomic level. First, we have made a comparative analysis of the details of residue-wise specific interactions of the spike protein with ACE2 for SARS-CoV-1 and SARS-CoV-2. It is found that the average numbers of both direct interprotein and water-bridged hydrogen bonds between the RBD and ACE2 are higher for SARS-CoV-2 than SARS-CoV-1. These higher hydrogen bonded interactions are further aided by enhanced nonspecific electrostatic attractions between the two protein surfaces for SARS-CoV-2. The free energy calculations reveal that there is an increase in the free energy barrier by ∼1.5 kcal/mol for the unbinding of RBD from ACE2 for SARS-CoV-2 compared to that for SARS-CoV-1. Subsequently, we considered the RBDs of three variants of SARS-CoV-2, namely N501Y, E484Q/L452R, and N440K. The free energy barrier of protein unbinding for the N501Y variant is found to be ∼4 kcal/mol higher than the wild type SARS-CoV-2 which can be attributed to additional specific interactions involving Tyr501 of RBD and Lys353 and Tyr42 of ACE2 and also enhanced nonspecific electrostatic interaction between the protein surfaces. For the other two mutant variants of E484Q/L452R and N440K, the free energy barrier for protein unbinding increases by ∼2 and ∼1 kcal/mol, respectively, compared with the wild type SARS-CoV-2, which can be attributed to an increase in the number of interprotein hydrogen bonds for the former and also to enhanced positive electrostatic potential on the RBD surfaces for both of the variants. The successive breaking of interprotein hydrogen bonds along the free energy pathway of the unbinding process is also found out for all five systems studied here.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

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.

Clinical and Genetic Characteristics of Coronaviruses with Particular Emphasis on SARS-CoV-2 Virus

The rapidly spreading Coronavirus Disease 2019 (COVID-19) pandemic has led to a global health crisis and has left a deep mark on society, culture, and the global economy. Despite considerable efforts made to contain the disease, SARS-CoV-2 still poses a threat on a global scale. The current epidemiological situation caused an urgent need to understand the basic mechanisms of the virus transmission and COVID-19 severe course. This review summarizes current knowledge on clinical courses, diagnostics, treatment, and prevention of COVID-19. Moreover, we have included the latest research results on the genetic characterization of SARS-CoV-2 and genetic determinants of susceptibility and severity to infection.

Exploring the Spike-hACE 2 Residue-Residue Interaction in Human Coronaviruses SARS-CoV-2, SARS-CoV, and HCoV-NL63

Coronaviruses (CoVs) have been responsible for three major outbreaks since the beginning of the 21st century, and the emergence of the recent COVID-19 pandemic has resulted in considerable efforts to design new therapies against coronaviruses. Thus, it is crucial to understand the structural features of their major proteins related to the virus-host interaction. Several studies have shown that from the seven known CoV human pathogens, three of them use the human Angiotensin-Converting Enzyme 2 (hACE-2) to mediate their host's cell entry: SARS-CoV-2, SARS-CoV, and HCoV-NL63. Therefore, we employed quantum biochemistry techniques within the density function theory (DFT) framework and the molecular fragmentation with conjugate caps (MFCC) approach to analyze the interactions between the hACE-2 and the spike protein-RBD of the three CoVs in order to map the hot-spot residues that form the recognition surface for these complexes and define the similarities and differences in the interaction scenario. The total interaction energy evaluated showed a good agreement with the experimental binding affinity order: SARS-2 > SARS > NL63. A detailed investigation revealed the energetically most relevant regions of hACE-2 and the spike protein for each complex, as well as the key residue-residue interactions. Our results provide valuable information to deeply understand the structural behavior and binding site characteristics that could help to develop antiviral therapeutics that inhibit protein-protein interactions between CoVs S protein and hACE-2.

Computational Analysis of the SARS-CoV-2 RBD-ACE2-Binding Process Based on MD and the 3D-RISM Theory

The binding process of angiotensin-converting enzyme 2 (ACE2) to the receptor-binding domain (RBD) of the severe acute respiratory syndrome-like coronavirus 2 spike protein was investigated using molecular dynamics simulation and the three-dimensional reference interaction-site model theory. The results suggested that the protein-binding process consists of a protein-protein approaching step, followed by a local structural rearrangement step. In the approaching step, the interprotein interaction energy decreased as the proteins approached each other, whereas the solvation free energy increased. As the proteins approached, the glycan of ACE2 first established a hydrogen bond with the RBD. Thereafter, the number of interprotein hydrogen bonds increased rapidly. The solvation free energy increased because of the desolvation of the protein as it approached its partner. The spatial distribution function of the solvent revealed the presence of hydrogen bonds bridged by water molecules on the RBD-ACE2 interface. Finally, principal component analysis revealed that ACE2 showed a pronounced conformational change, whereas there was no significant change in RBD.

Characterization of mutations modulating enhanced transmissibility of SARS-CoV-2 B.1.617+ (Delta) variant using In Silico tools

Since the beginning of the of SARS-CoV-2 (Covid-19) pandemic, variants of concern (VOC) have emerged taxing health systems worldwide. In October 2020, a new variant of SARS-CoV-2 (B.1.617+/Delta variant) emerged in India, triggering a deadly wave of Covid-19. Epidemiological data strongly suggests that B.1.617+ is more transmissible and previous reports have revealed that B.1.617+ has numerous mutations compared to wild type (WT), including several changes in the spike protein (SP). The main goal of this study was to use In Silico (computer simulation) techniques to examine mutations in the SP, specifically L452R and E484Q (part of the receptor binding domain (RBD) for human angiotensin-converting enzyme 2 (hACE2)) and P681R (upstream of the Furin cleavage motif), for effects in modulating the transmissibility of the B.1.617+ variant. Using computational models, the binding free energy (BFE) and H-bond lengths were calculated for SP-hACE2 and SP-Furin complexes. Comparison of the SP-hACE2 complex in the WT and B.1.617+ revealed both complexes have identical receptor-binding modes but the total BFE of B.1.617+ binding was more favorable for complex formation than WT, suggesting L452R and E484Q have a moderate impact on binding affinity. In contrast, the SP-Furin complex of B.1.617+ substantially lowered the BFE and revealed changes in molecular interactions compared to the WT complex, implying stronger complex formation between the variant and Furin. This study provides an insight into mutations that modulate transmissibility of the B.1.617+ variant, specifically the P681R mutation which appears to enhance transmissibility of the B.1.617+ variant by rendering it more receptive to Furin.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Unbinding of hACE2 and inhibitors from the receptor binding domain of SARS-CoV-2 spike protein

The first direful biomolecular event leading to COVID-19 disease is the SARS-CoV-2 virus surface spike (S) protein-mediated interaction with the human transmembrane protein, angiotensin-converting enzyme 2 (hACE2). Prevention of this interaction presents an attractive alternative to thwart SARS-CoV-2 replications. The development of monoclonal antibodies (mAbs) in the convalescent plasma treatment, nanobody, and designer peptides, which recognizes epitopes that overlap with hACE2 binding sites in the receptor-binding domain (RBD) of S protein (S/RBD) and thereby blocking the infection has been the center stage of therapeutic research. Here we report atomistic and reliable in silico structure-energetic features of the S/RBD interactions with hACE2 and its two inhibitors (convalescent mAb, B38, and an alpaca nanobody, Ty1). The discovered potential of mean forces exhibits free energy basin and barriers along the interaction pathways, providing sufficient molecular insights to design a B38 mutant and a Ty1-based peptide with higher binding capacity. While the mutated B38 forms a 60-fold deeper free energy minimum, the designer peptide (Ty1-based) constitutes 38 amino acids and is found to form a 100-fold deeper free energy minimum in the first binding basin than their wild-type variants in complex with S/RBD. Our strategy may help to design more efficacious biologics towards therapeutic intervention against the current raging pandemic.Communicated by Ramaswamy H. Sarma.

Insight into prognostics, diagnostics, and management strategies for SARS CoV-2

The foremost challenge in countering infectious diseases is the shortage of effective therapeutics. The emergence of coronavirus disease (COVID-19) outbreak has posed a great menace to the public health system globally, prompting unprecedented endeavors to contain the virus. Many countries have organized research programs for therapeutics and management development. However, the longstanding process has forced authorities to implement widespread infrastructures for detailed prognostic and diagnostics study of severe acute respiratory syndrome (SARS CoV-2). This review discussed nearly all the globally developed diagnostic methodologies reported for SARS CoV-2 detection. We have highlighted in detail the approaches for evaluating COVID-19 biomarkers along with the most employed nucleic acid- and protein-based detection methodologies and the causes of their severe downfall and rejection. As the variable variants of SARS CoV-2 came into the picture, we captured the breadth of newly integrated digital sensing prototypes comprised of plasmonic and field-effect transistor-based sensors along with commercially available food and drug administration (FDA) approved detection kits. However, more efforts are required to exploit the available resources to manufacture cheap and robust diagnostic methodologies. Likewise, the visualization and characterization tools along with the current challenges associated with waste-water surveillance, food security, contact tracing, and their role during this intense period of the pandemic have also been discussed. We expect that the integrated data will be supportive and aid in the evaluation of sensing technologies not only in current but also future pandemics.

Atom counting method for determining elemental composition of viruses and its applications in biothermodynamics and environmental science

Quantitative physicochemical perspective on life processes has been a great asset, in bioengineering and biotechnology. The quantitative physicochemical approach can be applied to practically all organisms, including viruses, if their chemical composition and thermodynamic properties are known. In this paper, a new method is suggested for determining elemental composition of viruses, based on atom counting. The atom counting method requires knowledge of genetic sequence, protein sequences and protein copy numbers. An algorithm was suggested for a program that finds elemental composition of various viruses (DNA or RNA, enveloped or non-enveloped). Except for the nucleic acid, capsid proteins, lipid bilayer and carbohydrates, this method includes membrane proteins, as well as spike proteins. The atom counting method has been compared with the existing molecular composition and geometric methods on 5 viruses of different morphology, as well as experimentally determined composition of the poliovirus. The atom counting method was found to be more accurate in most cases. The three methods were found to be complementary, since they require different kind of input information. Moreover, since the 3 methods rest on different assumptions, results of one model can be compared to those of the other two.

N501Y and K417N Mutations in the Spike Protein of SARS-CoV-2 Alter the Interactions with Both hACE2 and Human-Derived Antibody: A Free Energy of Perturbation Retrospective Study

The N501Y and K417N mutations in the spike protein of SARS-CoV-2 and their combination gave rise to questions, but the data on their mechanism of action at the molecular level were limited. In this study, we present free energy perturbation (FEP) calculations, performed at the end of December 2020, for the interactions of the spike S1 receptor-binding domain (RBD) with both the ACE2 receptor and an antibody derived from COVID-19 patients. Our results showed that the S1 RBD-ACE2 interactions were significantly increased whereas those with the STE90-C11 antibody dramatically decreased. The K417N mutation in a combination with N501Y fully abolished the antibody effect. However, Lys417Asn seems to have a compensatory mechanism of action increasing the S1 RBD-ACE2 free energy of binding. This may explain the increased spread of the virus observed in the U.K. and South Africa and also gives rise to an important question regarding the possible human immune response and the success of the already available vaccines. Notably, when the experimental data became available confirming our calculations, it was demonstrated that protein-protein FEP can be a useful tool for providing urgent data to the scientific community.

Rapid Assessment of Binding Affinity of SARS-COV-2 Spike Protein to the Human Angiotensin-Converting Enzyme 2 Receptor and to Neutralizing Biomolecules Based on Computer Simulations

SARS-CoV-2 infects humans and causes Coronavirus disease 2019 (COVID-19). The S1 domain of the spike glycoprotein of SARS-CoV-2 binds to human angiotensin-converting enzyme 2 (hACE2) via its receptor-binding domain, while the S2 domain facilitates fusion between the virus and the host cell membrane for entry. The spike glycoprotein of circulating SARS-CoV-2 genomes is a mutation hotspot. Some mutations may affect the binding affinity for hACE2, while others may modulate S-glycoprotein expression, or they could result in a virus that can escape from antibodies generated by infection with the original variant or by vaccination. Since a large number of variants are emerging, it is of vital importance to be able to rapidly assess their characteristics: while changes of binding affinity alone do not always cause direct advantages for the virus, they still can provide important insights on where the evolutionary pressure is directed. Here, we propose a simple and cost-effective computational protocol based on Molecular Dynamics simulations to rapidly screen the ability of mutated spike protein to bind to the hACE2 receptor and selected neutralizing biomolecules. Our results show that it is possible to achieve rapid and reliable predictions of binding affinities. A similar approach can be used to perform preliminary screenings of the potential effects of S-RBD mutations, helping to prioritize the more time-consuming and expensive experimental work.

Identification of Hotspot Residues in Binding of SARS-CoV-2 Spike and Human ACE2 Proteins

Coronaviruses are a large family of viruses that can cause respiratory infections with varying severity from common cold to severe diseases such as novel coronavirus disease (COVID-19). COVID-19 has been declared as a global pandemic by the World Health Organization on March 11, 2020 and with the development of vaccines it slowed down as of this date. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its spike glycoprotein (Sgp) to bind human angiotensin-converting enzyme 2 (hACE2) receptor, and mediates membrane fusion and virus entry. The recognition of Sgp to human ACE2 and its high affinity for it has been of great importance since this provides the first step in viral entry to human cells. Therefore, it is crucial to identify key residues (hotspots) in this process. In this study, computational alanine scanning has been performed for Sgp and hACE2. The residues identified with significance in binding and other residues in close proximity were studied further through molecular mechanics-based protein binding free energy change prediction methods. Moreover, the interfacial residues in both proteins were investigated for their cooperative binding. Additionally, folding free energy changes upon mutation to Ala were calculated to assess their effect on stability of Sgp and hACE2. Our results taken together with findings from previous studies revealed the residues that are most significant and are relatively significant in binding of Sgp to human ACE2 protein.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.

In silico study of intrinsic dynamics of full-length apo-ACE2 and RBD-ACE2 complex

The key step for SARS-CoV-2 to infect human cells is the membrane fusion triggered by the binding of the viral extracellular Spike protein to the human extracellular receptor, the angiotensin-converting enzyme 2 (ACE2). Although the Cryo-electron microscopy (Cryo-EM) uncovered the static atomic details of ACE2 homodimers, there is still a lack of research on the kinetic and thermodynamic properties of these full-length structures. This information is helpful to understand and interpret the role of ACE2 in the cell entry of SARS-CoV-2. In order to obtain this information, we performed microsecond-scale conventional and accelerated molecular dynamics (MD) simulations of full-length all-atomic systems of the RBD-ACE2 complex, the normal and torsional conformations of the apo-ACE2 homodimer. The comparative analysis of these systems showed that there were differences in their allosteric signal pathways and motion trends. These results may be helpful to further explore the cell entry mechanism of SARS-CoV-2. Moreover, the binding free energy and hydrogen bond distribution analysis of RBD-ACE2 binding interface provided the binding motifs that may be critical to allosteric signal transmission and RBD binding. These multi-conformational binding motifs can be used as targets or templates for the inhibitor design of the cell entry of SARS-CoV-2.

Coinfection and Interference Phenomena Are the Results of Multiple Thermodynamic Competitive Interactions

Biological, physical and chemical interaction between one (or more) microorganisms and a host organism, causing host cell damage, represents an infection. Infection of a plant, animal or microorganism with a virus can prevent infection with another virus. This phenomenon is known as viral interference. Viral interference is shown to result from two types of interactions, one taking place at the cell surface and the other intracellularly. Various viruses use different receptors to enter the same host cell, but various strains of one virus use the same receptor. The rate of virus–receptor binding can vary between different viruses attacking the same host, allowing interference or coinfection. The outcome of the virus–virus–host competition is determined by the Gibbs energies of binding and growth of the competing viruses and host. The virus with a more negative Gibbs energy of binding to the host cell receptor will enter the host first, while the virus characterized by a more negative Gibbs energy of growth will overtake the host metabolic machine and dominate. Once in the host cell, the multiplication machinery is shared by the competing viruses. Their potential to utilize it depends on the Gibbs energy of growth. Thus, the virus with a more negative Gibbs energy of growth will dominate. Therefore, the outcome can be interference or coinfection, depending on both the attachment kinetics (susceptibility) and the intracellular multiplication machinery (permittivity). The ratios of the Gibbs energies of binding and growth of the competing viruses determine the outcome of the competition. Based on this, a predictive model of virus–virus competition is proposed.

Key Interacting Residues between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamics Simulation and Density Functional Calculation

The spike protein of SARS-CoV-2 binds to the ACE2 receptor via its receptor-binding domain (RBD), with the RBD-ACE2 complex presenting an essential molecular target for vaccine development to stall the virus infection proliferation. The computational analyses at molecular, amino acid (AA), and atomic levels have been performed systematically to identify the key interacting AAs in the formation of the RBD-ACE2 complex for SARS-CoV and SARS-CoV-2 with its Alpha and Beta variants. Our study uses the molecular dynamics (MD) simulations with the molecular mechanics generalized Born surface area (MM-GBSA) method to predict the binding free energy (BFE) and to determine the actual interacting AAs, as well as two ab initio quantum chemical protocols based on the density functional theory (DFT) implementation. Based on MD results, Q493, Y505, Q498, N501, T500, N487, Y449, F486, K417, Y489, F456, Y495, and L455 have been identified as hotspots in SARS-CoV-2 RBD, while those in ACE2 are K353, K31, D30, D355, H34, D38, Q24, T27, Y83, Y41, and E35. RBD with Alpha and Beta variants has slightly different interacting AAs due to N501Y mutation. Both the electrostatic and hydrophobic interactions are the main driving force to form the AA-AA binding pairs. We confirm that Q493, Q498, N501, F486, K417, and F456 in RBD are the key residues responsible for the tight binding of SARS-CoV-2 with ACE2 compared to SARS-CoV. RBD with the Alpha variant binds with ACE2 stronger than the wild-type RBD or Beta. In the Beta variant, K417N reduces the binding, E484K slightly enhances it, and N501Y significantly increases it as in Alpha. The DFT results reveal that N487, Q493, Y449, T500, G496, G446, and G502 in RBD of SARS2 form pairs via specific hydrogen bonding with Q24, H34, E35, D38, Y41, Q42, and K353 in ACE2.

Molecular Dynamics Studies on the Structural Characteristics for the Stability Prediction of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the COVID-19 pandemic in the world. The spike protein of the various proteins encoded in SARS-CoV-2 binds to human ACE2, fuses, and enters human cells in the respiratory system. Spike protein, however, is highly variable, and many variants were identified continuously. In this study, Korean mutants for spike protein (D614G and D614A-C terminal domain, L455F and F456L-RBD, and Q787H-S2 domain) were investigated in patients. Because RBD in spike protein is related to direct interaction with ACE2, almost all researches were focused on the RBD region or ACE2-free whole domain region. The 3D structure for spike protein complexed with ACE2 was recently released. The stability analysis through RBD distance among each spike protein chain and the binding free energy calculation between spike protein and ACE2 were performed using MD simulation depending on mutant types in 1-, 2-, and 3-open-complex forms. D614G mutant of CT2 domain, showing to be the most prevalent in the global pandemic, showed higher stability in all open-complex forms than the wild type and other mutants. We hope this study will provide an insight into the importance of conformational fluctuation in the whole domain, although RBD is involved in the direct interaction with ACE2.

Peptide-Based Inhibitors for SARS-CoV-2 and SARS-CoV

The COVID‐19 (coronavirus disease) global pandemic, caused by the spread of the SARS‐CoV‐2 (severe acute respiratory syndrome coronavirus 2) virus, currently has limited treatment options which include vaccines, anti‐virals, and repurposed therapeutics. With their high specificity, tunability, and biocompatibility, small molecules like peptides are positioned to act as key players in combating SARS‐CoV‐2, and can be readily modified to match viral mutation rate. A recent expansion of the understanding of the viral structure and entry mechanisms has led to the proliferation of therapeutic viral entry inhibitors. In this comprehensive review, inhibitors of SARS and SARS‐CoV‐2 are investigated and discussed based on therapeutic design, inhibitory mechanistic approaches, and common targets. Peptide therapeutics are highlighted, which have demonstrated in vitro or in vivo efficacy, discuss advantages of peptide therapeutics, and common strategies in identifying targets for viral inhibition.

Free Energy Landscapes from SARS-CoV-2 Spike Glycoprotein Simulations Suggest that RBD Opening Can Be Modulated via Interactions in an Allosteric Pocket

The SARS-CoV-2 coronavirus is an enveloped, positive-sense single-stranded RNA virus that is responsible for the COVID-19 pandemic. The spike is a class I viral fusion glycoprotein that extends from the viral surface and is responsible for viral entry into the host cell and is the primary target of neutralizing antibodies. The receptor binding domain (RBD) of the spike samples multiple conformations in a compromise between evading immune recognition and searching for the host-cell surface receptor. Using atomistic simulations of the glycosylated wild-type spike in the closed and 1-up RBD conformations, we map the free energy landscape for RBD opening and identify interactions in an allosteric pocket that influence RBD dynamics. The results provide an explanation for experimental observation of increased antibody binding for a clinical variant with a substitution in this pocket. Our results also suggest the possibility of allosteric targeting of the RBD equilibrium to favor open states via binding of small molecules to the hinge pocket. In addition to potential value as experimental probes to quantify RBD conformational heterogeneity, small molecules that modulate the RBD equilibrium could help explore the relationship between RBD opening and S1 shedding.

Free Energy Landscapes from SARS-CoV-2 Spike Glycoprotein Simulations Suggest that RBD Opening Can Be Modulated via Interactions in an Allosteric Pocket

The SARS-CoV-2 coronavirus is an enveloped, positive-sense single-stranded RNA virus that is responsible for the COVID-19 pandemic. The spike is a class I viral fusion glycoprotein that extends from the viral surface and is responsible for viral entry into the host cell and is the primary target of neutralizing antibodies. The receptor binding domain (RBD) of the spike samples multiple conformations in a compromise between evading immune recognition and searching for the host-cell surface receptor. Using atomistic simulations of the glycosylated wild-type spike in the closed and 1-up RBD conformations, we map the free energy landscape for RBD opening and identify interactions in an allosteric pocket that influence RBD dynamics. The results provide an explanation for experimental observation of increased antibody binding for a clinical variant with a substitution in this pocket. Our results also suggest the possibility of allosteric targeting of the RBD equilibrium to favor open states via binding of small molecules to the hinge pocket. In addition to potential value as experimental probes to quantify RBD conformational heterogeneity, small molecules that modulate the RBD equilibrium could help explore the relationship between RBD opening and S1 shedding.

Small molecule therapeutics to destabilize the ACE2-RBD complex: A molecular dynamics study

The ongoing coronavirus disease 19 (COVID-19) pandemic has infected millions of people, claimed hundreds of thousands of lives, and made a worldwide health emergency. Understanding the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mechanism of infection is crucial in the development of potential therapeutics and vaccines. The infection process is triggered by direct binding of the SARS-CoV-2 receptor-binding domain (RBD) to the host-cell receptor angiotensin-converting enzyme 2 (ACE2). Many efforts have been made to design or repurpose therapeutics to deactivate the RBD or ACE2 and prevent the initial binding. In addition to direct inhibition strategies, small chemical compounds might be able to interfere and destabilize the metastable, prefusion complex of ACE2-RBD. This approach can be employed to prevent the further progress of virus infection at its early stages. In this study, molecular docking was employed to analyze the binding of two chemical compounds, SSAA09E2 and Nilotinib, with the druggable pocket of the ACE2-RBD complex. The structural changes as a result of the interference with the ACE2-RBD complex were analyzed by molecular dynamics simulations. Results show that both Nilotinib and SSAA09E2 can induce significant conformational changes in the ACE2-RBD complex, intervene with the hydrogen bonds, and influence the flexibility of proteins. Moreover, essential dynamics analysis suggests that the presence of small molecules can trigger large-scale conformational changes that may destabilize the ACE2-RBD complex.

Quantitative analysis of ACE2 binding to coronavirus spike proteins: SARS-CoV-2 vs. SARS-CoV and RaTG13

The global outbreak of the COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bat virus RaTG13 and SARS-CoV are also members of the coronavirus family and SARS-CoV caused a world-wide pandemic in 2003. SARS-CoV-2, SARS-CoV and RaTG13 bind to angiotensin-converting enzyme 2 (ACE2) through their receptor-binding domain (RBD) of the spike protein. SARS-CoV-2 binds ACE2 with a higher binding affinity than SARS-CoV and RaTG13. Here we performed molecular dynamics simulation of these binding complexes and calculated their binding free energies using a computational alanine scanning method. Our MD simulation and hotspot residue analysis showed that the lower binding affinity of SARS-CoV to ACE2 vs. SARS-CoV-2 to ACE2 can be explained by different hotspot interactions in these two systems. We also found that the lower binding affinity of RaTG13 to ACE2 is mainly due to a mutated residue (D501) which resulted in a less favorable complex formation for binding. We also calculated an important mutation of N501Y in SARS-CoV-2 using both alanine scanning calculation and a thermodynamic integration (TI) method. Both calculations confirmed a significant increase of the binding affinity of the N501Y mutant to ACE2 and explained its molecular mechanism. The present work provides an important theoretical basis for understanding the molecular mechanism in coronavirus spike protein binding to human ACE2.

Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2

SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

Critical Interactions Between the SARS-CoV-2 Spike Glycoprotein and the Human ACE2 Receptor

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects human cells by binding its spike (S) glycoproteins to angiotensin-converting enzyme 2 (ACE2) receptors and causes the coronavirus disease 2019 (COVID-19). Therapeutic approaches to prevent SARS-CoV-2 infection are mostly focused on blocking S-ACE2 binding, but critical residues that stabilize this interaction are not well understood. By performing all-atom molecular dynamics (MD) simulations, we identified an extended network of salt bridges, hydrophobic and electrostatic interactions, and hydrogen bonds between the receptor-binding domain (RBD) of the S protein and ACE2. Mutagenesis of these residues on the RBD was not sufficient to destabilize binding but reduced the average work to unbind the S protein from ACE2. In particular, the hydrophobic end of RBD serves as the main anchor site and is the last to unbind from ACE2 under force. We propose that blocking the hydrophobic surface of RBD via neutralizing antibodies could prove to be an effective strategy to inhibit S-ACE2 interactions.

A Bioluminescent Biosensor for Quantifying the Interaction of SARS-CoV-2 and Its Receptor ACE2 in Cells and In Vitro

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is currently spreading and mutating with increasing speed worldwide. Therefore, there is an urgent need for a simple, sensitive, and high-throughput (HTP) assay to quantify virus–host interactions in order to quickly evaluate the infectious ability of mutant viruses and to develop or validate virus-inhibiting drugs. Here, we developed an ultrasensitive bioluminescent biosensor to evaluate virus–cell interactions by quantifying the interaction between the SARS-CoV-2 receptor binding domain (RBD) and its cellular receptor angiotensin-converting enzyme 2 (ACE2) both in living cells and in vitro. We have successfully used this novel biosensor to analyze SARS-CoV-2 RBD mutants and evaluated candidate small molecules (SMs), antibodies, and peptides that may block RBD:ACE2 interaction. This simple, rapid, and HTP biosensor tool will significantly expedite the detection of viral mutants and the anti-COVID-19 drug discovery process.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor

Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) has caused substantially more infections, deaths, and economic disruptions than the 2002‐2003 SARS‐CoV. The key to understanding SARS‐CoV‐2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV‐2's spike protein stronger than SARS‐CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X‐ray structures. To go beyond insights gained from X‐ray structures and investigate the role of thermal fluctuations in structure, we employ all‐atom molecular dynamics simulations. Microseconds‐long simulations reveal that while CoV and CoV‐2 spike‐ACE2 interfaces have similar conformational binding modes, CoV‐2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non‐interfacial residues. These results recommend that ongoing efforts to design spike‐ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

Mutation Signatures and In Silico Docking of Novel SARS-CoV-2 Variants of Concern

One year since the first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in China, several variants of concern (VOC) have appeared around the world, with some variants seeming to pose a greater thread to public health due to enhanced transmissibility or infectivity. This study provides a framework for molecular characterization of novel VOC and investigates the effect of mutations on the binding affinity of the receptor-binding domain (RBD) to human angiotensin-converting enzyme 2 (hACE2) using in silico approach. Notable nonsynonymous mutations in RBD of VOC include the E484K and K417N/T that can be seen in South African and Brazilian variants, and N501Y and D614G that can be seen in all VOC. Phylogenetic analyses demonstrated that although the UK-VOC and the BR-VOC fell in the clade GR, they have different mutation signatures, implying an independent evolutionary pathway. The same is true about SA-VOC and COH-VOC felling in clade GH, but different mutation signatures. Combining molecular interaction modeling and the free energy of binding (FEB) calculations for VOC, it can be assumed that the mutation N501Y has the highest binding affinity in RBD for all VOC, followed by E484K (only for BR-VOC), which favors the formation of a stable complex. However, mutations at the residue K417N/T are shown to reduce the binding affinity. Once vaccination has started, there will be selective pressure that would be in favor of the emergence of novel variants capable of escaping the immune system. Therefore, genomic surveillance should be enhanced to find and monitor new emerging SARS-CoV-2 variants before they become a public health concern.

Nano-size dependence in the adsorption by the SARS-CoV-2 spike protein over gold colloid

Gold nano-particles were coated with the spike protein (S protein) of SARS-CoV-2 and exposed to increasingly acidic conditions. Their responses were investigated by monitoring the surface plasmon resonance (SPR) band shift. As the external pH was gradually changed from neutral pH to pH ∼2 the peak of the SPR band showed a significant red-shift, with a sigmoidal feature implying the formation of the gold-protein aggregates. The coating of S protein changed the surface property of the gold enough to extract the coverage fraction of protein over nano particles, Θ, which did not exhibit clear nano-size dependence. The geometrical simulation to explain Θ showed the average axial length to be a = 7. 25 nm and b =8.00 nm when the S-protein was hypothesized as a prolate shape with spiking-out orientation. As the pH value externally hopped between pH∼3 and pH∼10, a behavior of reversible protein folding was observed for particles with diameters >30 nm. It was concluded that S protein adsorption conformation was impacted by the size (diameter, d) of a core nano-gold, where head-to-head dimerized S protein was estimated for d ≤ 80 nm and a parallel in opposite directions formation for d = 100 nm.

Experimental Animal Models of Coronavirus Infections: Strengths and Limitations

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the emergence of SARS-CoV-2 in the human population in late 2019, it has spread on an unprecedented scale worldwide leading to the first coronavirus pandemic. SARS-CoV-2 infection results in a wide range of clinical manifestations from asymptomatic to fatal cases. Although intensive research has been undertaken to increase understanding of the complex biology of SARS-CoV-2 infection, the detailed mechanisms underpinning the severe pathogenesis and interactions between the virus and the host immune response are not well understood. Thus, the development of appropriate animal models that recapitulate human clinical manifestations and immune responses against SARS-CoV-2 is crucial. Although many animal models are currently available for the study of SARS-CoV-2 infection, each has distinct advantages and disadvantages, and some models show variable results between and within species. Thus, we aim to discuss the different animal models, including mice, hamsters, ferrets, and non-human primates, employed for SARS-CoV-2 infection studies and outline their individual strengths and limitations for use in studies aimed at increasing understanding of coronavirus pathogenesis. Moreover, a significant advantage of these animal models is that they can be tailored, providing unique options specific to the scientific goals of each researcher.

An Active Site Inhibitor Induces Conformational Penalties for ACE2 Recognition by the Spike Protein of SARS-CoV-2

The novel RNA virus, severe acute respiratory syndrome coronavirus II (SARS-CoV-2), is currently the leading cause of mortality in 2020, having led to over 1.6 million deaths and infecting over 75 million people worldwide by December 2020. While vaccination has started and several clinical trials for a number of vaccines are currently underway, there is a pressing need for a cure for those already infected with the virus. Of particular interest in the design of anti-SARS-CoV-2 therapeutics is the human protein angiotensin converting enzyme II (ACE2) to which this virus adheres before entry into the host cell. The SARS-CoV-2 virion binds to cell-surface bound ACE2 via interactions of the spike protein (s-protein) on the viral surface with ACE2. In this paper, we use all-atom molecular dynamics simulations and binding enthalpy calculations to determine the effect that a bound ACE2 active site inhibitor (MLN-4760) would have on the binding affinity of SARS-CoV-2 s-protein with ACE2. Our analysis indicates that the binding enthalpy could be reduced for s-protein adherence to the active site inhibitor-bound ACE2 protein by as much as 1.48-fold as an upper limit. This weakening of binding strength was observed to be due to the destabilization of the interactions between ACE2 residues Glu-35, Glu-37, Tyr-83, Lys-353, and Arg-393 and the SARS-CoV-2 s-protein receptor binding domain (RBD). The conformational changes were shown to lead to weakening of ACE2 interactions with SARS-CoV-2 s-protein, therefore reducing s-protein binding strength. Further, we observed increased conformational lability of the N-terminal helix and a conformational shift of a significant portion of the ACE2 motifs involved in s-protein binding, which may affect the kinetics of the s-protein binding when the small molecule inhibitor is bound to the ACE2 active site. These observations suggest potential new ways for interfering with the SARS-CoV-2 adhesion by modulating ACE2 conformation through distal active site inhibitor binding.

Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein

The ongoing COVID-19 pandemic caused by the new coronavirus, SARS-CoV-2, calls for urgent developments of vaccines and antiviral drugs. The spike protein of SARS-CoV-2 (S-protein), which consists of trimeric polypeptide chains with glycosylated residues on the surface, triggers the virus entry into a host cell. Extensive structural and functional studies on this protein have rapidly advanced our understanding of the S-protein structure at atomic resolutions, although most of these structural studies overlook the effect of glycans attached to the S-protein on the conformational stability and functional motions between the inactive down and active up forms. Here, we performed all-atom molecular dynamics simulations of both down and up forms of a fully glycosylated S-protein in solution as well as targeted molecular dynamics simulations between them to elucidate key interdomain interactions for stabilizing each form and inducing the large-scale conformational transitions. The residue-level interaction analysis of the simulation trajectories detects distinct amino acid residues and N-glycans as determinants on conformational stability of each form. During the conformational transitions between them, interdomain interactions mediated by glycosylated residues are switched to play key roles on the stabilization of another form. Electrostatic interactions, as well as hydrogen bonds between the three receptor binding domains, work as driving forces to initiate the conformational transitions toward the active form. This study sheds light on the mechanisms underlying conformational stability and functional motions of the S-protein, which are relevant for vaccine and antiviral drug developments.

Tracing the driving forces responsible for the remarkable infectivity of 2019-nCoV: 1. Receptor binding domain in its bound and unbound states

The pandemic of COVID-19 has posed an urgent need to learn the dynamics of the virus and the mechanism of its contagion of host cells. By means of molecular dynamics simulations, this work addressed the behavior of 2019-nCoV in two aspects: the binding affinity of its receptor binding domain (RBD) with ACE2, and its potential conformation preferences in its unbound state. The results showed that the RBD of 2019-nCov bound much stronger with ACE2 than that of SARS-CoV due to a better organized hydrogen bond network between the former pair with most of the residues at the contact interface sharing the responsibility to hold the pair tightly. This is in contrast to the case of SARS-CoV, which strongly relied on the residues at the ends of the cleft. In its unbound state, the RBD of 2019-nCoV was found to fold part of its receptor binding motif (RBM) into a helical conformation and flip into a concave to minimize its contact with the external environment. This has the biological implication that the virus may achieve higher translational motion in the condensed phase and have a higher chance of survival by avoiding capture by the immune system before reaching its target receptor.

Role of Long-range Allosteric Communication in Determining the Stability and Disassembly of SARS-COV-2 in Complex with ACE2

Severe acute respiratory syndrome (SARS) and novel coronavirus disease (COVID-19) are caused by two closely related beta-coronaviruses, SARS-CoV and SARS-CoV-2, respectively. The envelopes surrounding these viruses are decorated with spike proteins, whose receptor binding domains (RBDs) initiate invasion by binding to the human angiotensin-converting enzyme 2 (ACE2). Subtle changes at the interface with ACE2 seem to be responsible for the enhanced affinity for the receptor of the SARS-CoV-2 RBD compared to SARS-CoV RBD. Here, we use Elastic Network Models (ENMs) to study the response of the viral RBDs and ACE2 upon dissassembly of the complexes. We identify a dominant detachment mode, in which the RBD rotates away from the surface of ACE2, while the receptor undergoes a conformational transition which stretches the active-site cleft. Using the Structural Perturbation Method, we determine the network of residues, referred to as the Allostery Wiring Diagram (AWD), which drives the large-scale motion activated by the detachment of the complex. The AWD for SARS-CoV and SARS-CoV-2 are remarkably similar, showing a network that spans the interface of the complex and reaches the active site of ACE2, thus establishing an allosteric connection between RBD binding and receptor catalytic function. Informed in part by the AWD, we used Molecular Dynamics simulations to probe the effect of interfacial mutations in which SARS-CoV-2 residues are replaced by their SARS-CoV counterparts. We focused on a conserved glycine (G502 in SARS-CoV-2, G488 in SARS-CoV) because it belongs to a region that initiates the dissociation of the complex along the dominant detachment mode, and is prominent in the AWD. Molecular Dynamics simulations of SARS-CoV-2 wild-type and G502P mutant show that the affinity for the human receptor of the mutant is drastically diminished. Our results suggest that in addition to residues that are in direct contact with the interface those involved in long range allosteric communication are also a determinant of the stability of the RBD-ACE2 complex.

Virtual screening and molecular dynamics study of approved drugs as inhibitors of spike protein S1 domain and ACE2 interaction in SARS-CoV-2

BACKGROUND

The receptor binding domain (RBD) of spike protein S1 domain SARS-CoV-2 plays a key role in the interaction with ACE2, which leads to subsequent S2 domain mediated membrane fusion and incorporation of viral RNA into host cells. In this study we tend to repurpose already approved drugs as inhibitors of the interaction between S1-RBD and the ACE2 receptor.

METHODS

2456 approved drugs were screened against the RBD of S1 protein of SARS-CoV-2 (target PDB ID: 6M17). As the interacting surface between S1-RBD and ACE2 comprises of bigger region, the interacting surface was divided into 3 sites on the basis of interactions (site 1, 2 and 3) and a total of 5 grids were generated (site 1, site 2, site 3, site 1+site 2 and site 2+site 3). A virtual screening was performed using GLIDE implementing HTVS, SP and XP screening. The top hits (on the basis of docking score) were further screened for MM-GBSA. All the top hits were further evaluated in molecular dynamics studies. Performance of the virtual screening protocol was evaluated using enrichment studies.

RESULT

and discussion: We performed 5 virtual screening against 5 grids generated. A total of 42 compounds were identified after virtual screening. These drugs were further assessed for their interaction dynamics in molecular dynamics simulation. On the basis of molecular dynamics studies, we come up with 10 molecules with favourable interaction profile, which also interacted with physiologically important residues (residues taking part in the interaction between S1-RBD and ACE2. These are antidiabetic (acarbose), vitamins (riboflavin and levomefolic acid), anti-platelet agents (cangrelor), aminoglycoside antibiotics (Kanamycin, amikacin) bronchodilator (fenoterol), immunomodulator (lamivudine), and anti-neoplastic agents (mitoxantrone and vidarabine). However, while considering the relative side chain fluctuations when compared to the S1-RBD: ACE2 complex riboflavin, fenoterol, cangrelor and vidarabine emerged out as molecules with prolonged relative stability.

CONCLUSION

We identified 4 already approved drugs (riboflavin, fenoterol, cangrelor and vidarabine) as possible agents for repurposing as inhibitors of S1:ACE2 interaction. In-vitro validation of these findings are necessary for identification of a safe and effective inhibitor of S1: ACE2 mediated entry of SARS-CoV-2 into the host cell.