The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach

Lessons learnt from broad-spectrum coronavirus antiviral drug discovery

INTRODUCTION

Highly pathogenic coronaviruses (CoVs), such as severe acute respiratory syndrome CoV (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and the most recent SARS-CoV-2 responsible for the COVID-19 pandemic, pose significant threats to human populations over the past two decades. These CoVs have caused a broad spectrum of clinical manifestations ranging from asymptomatic to severe distress syndromes (ARDS), resulting in high morbidity and mortality.

AREAS COVERED

The accelerated advancements in antiviral drug discovery, spurred by the COVID-19 pandemic, have shed new light on the imperative to develop treatments effective against a broad spectrum of CoVs. This perspective discusses strategies and lessons learnt in targeting viral non-structural proteins, structural proteins, drug repurposing, and combinational approaches for the development of antivirals against CoVs.

EXPERT OPINION

Drawing lessons from the pandemic, it becomes evident that the absence of efficient broad-spectrum antiviral drugs increases the vulnerability of public health systems to the potential onslaught by highly pathogenic CoVs. The rapid and sustained spread of novel CoVs can have devastating consequences without effective and specifically targeted treatments. Prioritizing the effective development of broad-spectrum antivirals is imperative for bolstering the resilience of public health systems and mitigating the potential impact of future highly pathogenic CoVs.

Can iron chelators ameliorate viral infections?

The redox reactivity of iron is a double-edged sword for cell functions, being either essential or harmful depending on metal concentration and location. Deregulation of iron homeostasis is associated with several clinical conditions, including viral infections. Clinical studies as well as in silico, in vitro and in vivo models show direct effects of several viruses on iron levels. There is support for the strategy of iron chelation as an alternative therapy to inhibit infection and/or viral replication, on the rationale that iron is required for the synthesis of some viral proteins and genes. In addition, abnormal iron levels can affect signaling immune response. However, other studies report different effects of viral infections on iron homeostasis, depending on the class and genotype of the virus, therefore making it difficult to predict whether iron chelation would have any benefit. This review brings general aspects of the relationship between iron homeostasis and the nonspecific immune response to viral infections, along with its relevance to the progress or inhibition of the inflammatory process, in order to elucidate situations in which the use of iron chelators could be efficient as antivirals.

SARS-CoV-2 mechanisms of cell tropism in various organs considering host factors

A critical step in the drug design for SARS-CoV-2 is to discover its molecular targets. This study comprehensively reviewed the molecular mechanisms of SARS-CoV-2, exploring host cell tropism and interaction targets crucial for cell entry. The findings revealed that beyond ACE2 as the primary entry receptor, alternative receptors, co-receptors, and several proteases such as TMPRSS2, Furin, Cathepsin L, and ADAM play critical roles in virus entry and subsequent pathogenesis. Additionally, SARS-CoV-2 displays tropism in various human organs due to its diverse receptors. This review delves into the intricate details of receptors, host proteases, and the involvement of each organ. Polymorphisms in the ACE2 receptor and mutations in the spike or its RBD region contribute to the emergence of variants like Alpha, Beta, Gamma, Delta, and Omicron, impacting the pathogenicity of SARS-CoV-2. The challenge posed by mutations raises questions about the effectiveness of existing vaccines and drugs, necessitating consideration for updates in their formulations. In the urgency of these critical situations, repurposed drugs such as Camostat Mesylate and Nafamostat Mesylate emerge as viable pharmaceutical options. Numerous drugs are involved in inhibiting receptors and host factors crucial for SARS-CoV-2 entry, with most discussed in this review. In conclusion, this study may provide valuable insights to inform decisions in therapeutic approaches.

Mechanisms of allostery at the viral surface through the eyes of molecular simulation

The outermost surface layer of any virus is formed by either a capsid shell or envelope. Such layers have traditionally been thought of as immovable structures, but it is becoming apparent that they cannot be viewed exclusively as static architectures protecting the viral genome. A limited number of proteins on the virion surface must perform a multitude of functions in order to orchestrate the viral life cycle, and allostery can regulate their structures at multiple levels of organization, spanning individual molecules, protomers, large oligomeric assemblies, or entire viral surfaces. Here, we review recent contributions from the molecular simulation field to viral surface allostery, with a particular focus on the trimeric spike glycoprotein emerging from the coronavirus surface, and the icosahedral flaviviral envelope complex. As emerging viral pathogens continue to pose a global threat, an improved understanding of viral dynamics and allosteric regulation will prove crucial in developing novel therapeutic strategies.

The Search for System's Parameters: Statistical and Dynamical Description from Complex Network Analysis

The integration of physical and biological science styles is the key for facing the deluge of molecular level information that is becoming a real threat for knowledge advancement. In this work, I will indicate a possible integration path based on the network formalization of molecular knowledge by two different (here named flux and dynamical) perspectives. Some theoretical and applicative cases are presented, focusing on the different physical models implicit in the two network analysis approaches.

Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state.

OBJECTIVE

This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state.

METHODS

In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD.

RESULTS

Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state.

CONCLUSION

The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

Elastic network models and molecular dynamic simulations reveal the molecular basis of allosteric regulation in ubiquitin-specific protease 7 (USP7)

Ubiquitin-specific protease 7 (USP7) is one of the most abundant deubiquitinases and plays an important role in various malignant tumors. However, the molecular mechanisms underlying USP7's structures, dynamics, and biological significance are yet to be investigated. In this study, we constructed the full-length models of USP7 in both the extended and compact state, and applied elastic network models (ENM), molecular dynamics (MD) simulations, perturbation response scanning (PRS) analysis, residue interaction networks as well as allosteric pocket prediction to investigate allosteric dynamics in USP7. Our analysis of intrinsic and conformational dynamics revealed that the structural transition between the two states is characterized by global clamp motions, during which the catalytic domain (CD) and UBL4-5 domain exhibit strong negative correlations. The PRS analysis, combined with the analysis of disease mutations and post-translational modifications (PTMs) further highlighted the allosteric potential of the two domains. The residue interaction network based on MD simulations captured an allosteric communication path which starts at CD domain and ends at UBL4-5 domain. Moreover, we identified a pocket at the TRAF-CD interface as a high-potential allosteric site for USP7. Overall, our studies not only provide molecular insights into the conformational changes of USP7, but also aid in the design of allosteric modulators that target USP7.

SARS-CoV-2 protein structure and sequence mutations: Evolutionary analysis and effects on virus variants

The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises. However, there is the need for models and tools to study the links between the evolution of S protein sequence, structure and functions, and virus transmissibility and the effects of vaccination. As studies on S protein have been generated a large amount of relevant information, we propose in this work to use Protein Contact Networks (PCNs) to relate protein structures with biological properties by means of network topology properties. Topological properties are used to compare the structural changes with sequence changes. We find that both node centrality and community extraction analysis can be used to relate protein stability and functionality with sequence mutations. Starting from this we compare structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. Finally by applying our model to the Omicron variant we report a timeline correlation between Omicron and the vaccination campaign.

Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks

The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.

A Protein Co-Conservation Network Model Characterizes Mutation Effects on SARS-CoV-2 Spike Protein

The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors' degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations.

Computational method for aromatase-related proteins using machine learning approach

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics Analysis of Fast-Spreading Severe Acute Respiratory Syndrome Coronavirus 2 Variants and Their Effects on the Interaction with Human Angiotensin-Converting Enzyme 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving with mutations in the spike protein, especially in the receptor-binding domain (RBD). The failure of public health measures in some countries to contain the spread of the disease has given rise to novel viral variants with increased transmissibility. However, key questions about how quickly the variants can spread remain unclear. Herein, we performed a structural investigation using molecular dynamics simulations and determined dissociation constant (K _D) values using surface plasmon resonance assays of three fast-spreading SARS-CoV-2 variants, alpha, beta, and gamma, as well as genetic factors in host cells that may be related to the viral infection. Our results suggest that the SARS-CoV-2 variants facilitate their entry into the host cell by moderately increased binding affinities to the human ACE2 receptor, different torsions in hACE2 mediated by RBD variants, and an increased spike exposure time to proteolytic enzymes. We also found that other host cell aspects, such as gene and isoform expression of key genes for the infection (ACE2, FURIN, and TMPRSS2), may have few contributions to the SARS-CoV-2 variant infectivity. In summary, we concluded that a combination of viral and host cell factors allows SARS-CoV-2 variants to increase their abilities to spread faster than the wild type.

PCN-Miner: an open-source extensible tool for the analysis of Protein Contact Networks

MOTIVATION

Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches.

RESULTS

We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis.

AVAILABILITY AND IMPLEMENTATION

The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.

In-Silico Characterization of von Willebrand Factor Bound to FVIII

Factor VIII belongs to the coagulation cascade and is expressed as a long pre-protein (mature form, 2351 amino acids long). FVIII is deficient or defective in hemophilic A patients, who need to be treated with hemoderivatives or recombinant FVIII substitutes, i.e., biologic drugs. The interaction between FVIII and von Willebrand factor (VWF) influences the pharmacokinetics of FVIII medications. In vivo, full-length FVIII (FL-FVIII) is secreted in a plasma-inactive form, which includes the B domain, which is then proteolyzed by thrombin protease activity, leading to an inactive plasma intermediate. In this work, we analyzed through a computational approach the binding of VWF with two structure models of FVIII (secreted full-length with B domain, and B domain-deleted FVIII). We included in our analysis the atomic model of efanesoctocog alfa, a novel and investigational recombinant FVIII medication, in which the VWF is covalently linked to FVIII. We carried out a structural analysis of VWF/FVIII interfaces by means of protein–protein docking, PISA (Proteins, Interfaces, Structures and Assemblies), and protein contact networks (PCN) analyses. Accordingly, our computational approaches to previously published experimental data demonstrated that the domains A3-C1 of B domain-deleted FVIII (BDD-FVIII) is the preferential binding site for VWF. Overall, our computational approach applied to topological analysis of protein–protein interface can be aimed at the rational design of biologic drugs other than FVIII medications.

Decoding molecular factors shaping human angiotensin converting enzyme 2 receptor usage by spike glycoprotein in lineage B beta-coronaviruses

Acquiring the human ACE2 receptor usage trait enables the coronaviruses to spill over to humans. However, the origin of the ACE2 usage trait in coronaviruses is poorly understood. Using a multi-disciplinary approach combining evolutionary bioinformatics and molecular dynamics simulation, we decode the principal driving force behind human ACE2 receptor recognition in coronaviruses. Genomic content, evolutionary divergence, and codon usage bias analysis reveal that SARS-CoV2 is evolutionarily divergent from other human ACE2-user CoVs, indicating that SARS-CoV2 originates from a different lineage. Sequence analysis shows that all the human ACE2-user CoVs contain two insertions in the receptor-binding motif (RBM) that directly interact with ACE2. However, the insertion sequences in SARS-CoV2 are divergent from other ACE2-user CoVs, implicating their different recombination origins. The potential of mean force calculations reveals that the high binding affinity of SARS-CoV2 RBD to human ACE2 is primarily attributed to its ability to form a higher number of hydrogen bonds than the other ACE2-user CoVs. The adaptive branch-site random effects likelihood method identifies positive selection bias across the ACE2 user CoVs lineages. Recombination and selection forces shape the spike evolution in human ACE2-using beta-CoVs to optimize the interfacial hydrogen bonds between RBD and ACE2. However, these evolutionary forces work within the constraints of nucleotide composition, ensuring optimum codon adaptation of the spike (S) gene within the host cell.

A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the β2 Adrenergic Receptor (β2-AR) was examined and allosteric switches were identified in the molecular structure through the reorganization of energy transport networks during activation. In this work, we further investigated the allosteric properties of β2-AR, using Protein Contact Networks (PCNs). In this paper, we report an extensive statistical analysis of the topological and structural properties of β2-AR along its molecular dynamics trajectory to identify the activation pattern of this molecular system. The results show a distinct character to the activation that both helps to understand the allosteric switching previously identified and confirms the relevance of the network formalism to uncover relevant functional features of protein molecules.

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.

The beneficial role of Hepcidin peptide inhibitor in improved the symptoms of COVID-19 in diabetics: anti-inflammatory and potential therapeutic effects

Coronavirus Disease 2019 (COVID-19) is a recent public health issue worldwide. Also, diabetes is a frequent condition with high mortality. There is a strong relationship between COVID-19 and diabetes. This article analyses the intricate relationship between COVID-19 and hepcidin. Hepcidin increases in aged non-insulin diabetic patients. Hepcidin is the last target treatment of several medications commonly used. Viral diseases, especially SARS-CoV19, can activate the hepcidin pathway leading to an elevation in the iron load. This increased iron is released into the bloodstream and results in cell death through ferroptosis, like free iron. Excess iron has pro-coagulative and toxic effects. Hepcidin overexpression and iron overload are associated with COVID-19 infection and can be considered potential targets for treatment. Several studies have shown dalteparin (anti-Hepcidin) could improve the symptoms of COVID-19 in diabetics by appropriately modulating and decreasing oxidative stress and inflammation. This finding can be leading to enhancing the existing knowledge about Therapeutic measures for reducing Covid-19 impairments in diabetics and is suggested as a possible therapeutic agent in diabetes.

Probing structural basis for enhanced binding of SARS-CoV-2 P.1 variant spike protein with the human ACE2 receptor

The initial step of infection by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) involves the binding of receptor binding domain (RBD) of the spike protein to the angiotensin converting enzyme 2 (ACE2) receptor. Each successive wave of SARS‐CoV‐2 reports emergence of many new variants, which is associated with mutations in the RBD as well as other parts of the spike protein. These mutations are reported to have enhanced affinity towards the ACE2 receptor as well as are also crucial for the virus transmission. Many computational and experimental studies have demonstrated the effect of individual mutation on the RBD‐ACE2 binding. However, the cumulative effect of mutations on the RBD and away from the RBD was not investigated in detail. We report here a comparative analysis on the structural communication and dynamics of the RBD and truncated S1 domain of spike protein in complex with the ACE2 receptor from SARS‐CoV‐2 wild type and its P.1 variant. Our integrative network and dynamics approaches highlighted a subtle conformational changes in the RBD as well as truncated S1 domain of spike protein at the protein contact level, responsible for the increased affinity with the ACE2 receptor. Moreover, our study also identified the commonalities and differences in the dynamics of the interactions between spike protein of SARS‐CoV‐2 wild type and its P.1 variant with the ACE2 receptor. Further, our investigation yielded an understanding towards identification of the unique RBD residues crucial for the interaction with the ACE2 host receptor. Overall, the study provides an insight for designing better therapeutics against the circulating P.1 variants as well as other future variants.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Energy Bilocalization Effect and the Emergence of Molecular Functions in Proteins

Proteins are among the most complex molecular structures, which have evolved to develop broad functions, such as energy conversion and transport, information storage and processing, communication, and regulation of chemical reactions. However, the mechanisms by which these dynamical entities coordinate themselves to perform biological tasks remain hotly debated. Here, a physical theory is presented to explain how functional dynamical behavior possibly emerge in complex/macro molecules, thanks to the effect that we term bilocalization of thermal vibrations. More specifically, our approach allows us to understand how structural irregularities lead to a partitioning of the energy of the vibrations into two distinct sets of molecular domains, corresponding to slow and fast motions. This shape-encoded spectral allocation, associated to the genetic sequence, provides a close access to a wide reservoir of dynamical patterns, and eventually allows the emergence of biological functions by natural selection. To illustrate our approach, the SPIKE protein structure of SARS-COV2 is considered.

Computational Study of SARS-CoV-2 RNA Dependent RNA Polymerase Allosteric Site Inhibition

The COVID-19 pandemic has caused millions of fatalities since 2019. Despite the availability of vaccines for this disease, new strains are causing rapid ailment and are a continuous threat to vaccine efficacy. Here, molecular docking and simulations identify strong inhibitors of the allosteric site of the SARS-CoV-2 virus RNA dependent RNA polymerase (RdRp). More than one hundred different flavonoids were docked with the SARS-CoV-2 RdRp allosteric site through computational screening. The three top hits were Naringoside, Myricetin and Aureusidin 4,6-diglucoside. Simulation analyses confirmed that they are in constant contact during the simulation time course and have strong association with the enzyme's allosteric site. Absorption, distribution, metabolism, excretion and toxicity (ADMET) data provided medicinal information of these top three hits. They had good human intestinal absorption (HIA) concentrations and were non-toxic. Due to high mutation rates in the active sites of the viral enzyme, these new allosteric site inhibitors offer opportunities to drug SARS-CoV-2 RdRp. These results provide new information for the design of novel allosteric inhibitors against SARS-CoV-2 RdRp.

Mutation-Induced Long-Range Allosteric Interactions in the Spike Protein Determine the Infectivity of SARS-CoV-2 Emerging Variants

The emergence of a variety of highly transmissible SARS-CoV-2 variants, the causative agent of COVID-19, with multiple spike mutations poses serious challenges in overcoming the ongoing deadly pandemic. It is, therefore, essential to understand how these variants gain enhanced ability to evade immune responses with a higher rate of spreading infection. To address this question, here we have individually assessed the effects of SARS-CoV-2 variant-specific spike (S) protein receptor-binding domain (RBD) mutations E484K, K417N, L452Q, L452R, N501Y, and T478K that characterize and differentiate several emerging variants. Despite the hundreds of apparently neutral mutations observed in the domains other than the RBD, we have shown that each RBD mutation site is differentially engaged in an interdomain allosteric network involving mutation sites from a distant domain, affecting interactions with the human receptor angiotensin-converting enzyme-2 (ACE2). This allosteric network couples the residues of the N-terminal domain (NTD) and the RBD, which are modulated by the RBD-specific mutations and are capable of propagating mutation-induced perturbations between these domains through a combination of structural changes and effector-dependent modulations of dynamics. One key feature of this network is the inclusion of compensatory mutations segregated into three characteristically different clusters, where each cluster residue site is allosterically coupled with specific RBD mutation sites. Notably, each RBD mutation acted like a positive allosteric modulator; nevertheless, K417N was shown to have the largest effects among all of the mutations on the allostery and thereby holds the highest binding affinity with ACE2. This result will be useful for designing the targeted control measure and therapeutic efforts aiming at allosteric modulators.

Distant residues modulate conformational opening in SARS-CoV-2 spike protein

The novel coronavirus (SARS-CoV-2) pandemic resulted in the largest public health crisis in recent times. Significant drug design effort against SARS-CoV-2 is focused on the receptor-binding domain (RBD) of the spike protein, although this region is highly prone to mutations causing therapeutic resistance. We applied deep data analysis methods on all-atom molecular dynamics simulations to identify key non-RBD residues that play a crucial role in spike−receptor binding and infection. Because the non-RBD residues are typically conserved across multiple coronaviruses, they can be targeted by broad-spectrum antibodies and drugs to treat infections from new strains that might appear during future epidemics.

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the attachment of the receptor-binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down-to-up conformational change in the spike protein, the change that presents the RBD to the receptor. To date, computational and experimental studies that search for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and is therefore a hotspot for drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. This allows for a deeper understanding of the underlying molecular recognition events and prediction of the highest-effect key mutations in distant, allosteric sites, with implications for therapeutics. Also, these sites can appear in emerging mutants with possibly higher transmissibility and virulence, and preidentifying them can give clues for designing pan-coronavirus vaccines against future outbreaks. Our model, based on time-lagged independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Residues involved in the most ubiquitous D614G mutation and the A570D mutation of the highly contagious UK SARS-CoV-2 variant are predicted ab initio from our model. Conversely, broad-spectrum therapeutics like drugs and monoclonal antibodies can target these key distant-but-conserved regions of the spike protein.

Insights into Conformational Dynamics and Allostery in DNMT1-H3Ub/USP7 Interactions

DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design.

Identification of Potential Binding Sites of Sialic Acids on the RBD Domain of SARS-CoV-2 Spike Protein

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still an emergent pandemic for humans. The virus infection is achieved by penetrating its spike protein to host cells via binding with ACE2. Moreover, recent studies show that SARS-CoV-2 may have multiple receptors that need to be further revealed. SARS-CoV-2 shares similar sequences of the spike protein with the Middle East Respiratory Syndrome Coronavirus (MERS-CoV), which can invade host cells by binding to either DPP4 or sialic acids. Sialic acids can be linked to the terminal of glycoproteins and gangliosides are used as one of the receptors of many types of viruses. Therefore, it is very interesting to determine whether sialic acid is a potential receptor of SARS-CoV-2. To address this question, we took N-Acetylneuraminic acid (Neu5Ac), a type of predominant sialic acid found in human cells, as the molecular probe to computationally search the surface of the spike protein to locate the potential binding sites of Neu5Ac. SPR analysis and mass spectrum analysis confirmed the interaction between Neu5Ac and spike protein. This study shows that sialic acids can moderately interact with the spike protein of SARS-CoV-2 by binding between the two RBDs of the spike protein, indicating it could be a potential secondary or auxiliary receptor of SARS-CoV-2.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

Dynamic Profiling of Binding and Allosteric Propensities of the SARS-CoV-2 Spike Protein with Different Classes of Antibodies: Mutational and Perturbation-Based Scanning Reveals the Allosteric Duality of Functionally Adaptable Hotspots

The functional adaptability and conformational plasticity of SARS-CoV-2 spike proteins allow for the efficient modulation of complex phenotypic responses to the host receptor and antibodies. In this study, we combined atomistic simulations with mutational and perturbation-based scanning approaches to examine binding mechanisms of the SARS-CoV-2 spike proteins with three different classes of antibodies. The ensemble-based profiling of binding and allosteric propensities of the SARS-CoV-2 spike protein residues showed that these proteins can work as functionally adaptable and allosterically regulated machines. Conformational dynamics analysis revealed that binding-induced modulation of soft modes can elicit the unique protein response to different classes of antibodies. Mutational scanning heatmaps and sensitivity analysis revealed the binding energy hotspots for different classes of antibodies that are consistent with the experimental deep mutagenesis, showing that differences in the binding affinity caused by global circulating variants in spike positions K417, E484, and N501 are relatively moderate and may not fully account for the observed antibody resistance effects. Through functional dynamics analysis and perturbation-response scanning of the SARS-CoV-2 spike protein residues in the unbound form and antibody-bound forms, we examine how antibody binding can modulate allosteric propensities of spike protein residues and determine allosteric hotspots that control signal transmission and global conformational changes. These results show that residues K417, E484, and N501 targeted by circulating mutations correspond to a group of versatile allosteric centers in which small perturbations can modulate collective motions, alter the global allosteric response, and elicit binding resistance. We suggest that the SARS-CoV-2 S protein may exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter the response to antibody binding without compromising the activity of the spike protein.

The Odd Faces of Oligomers: The Case of TRAF2-C, A Trimeric C-Terminal Domain of TNF Receptor-Associated Factor

TNF Receptor Associated Factor 2 (TRAF2) is a trimeric protein that belongs to the TNF receptor associated factor family (TRAFs). The TRAF2 oligomeric state is crucial for receptor binding and for its interaction with other proteins involved in the TNFR signaling. The monomer-trimer equilibrium of a C- terminal domain truncated form of TRAF2 (TRAF2-C), plays also a relevant role in binding the membrane, causing inward vesiculation. In this study, we have investigated the conformational dynamics of TRAF2-C through circular dichroism, fluorescence, and dynamic light scattering, performing temperature-dependent measurements. The data indicate that the protein retains its oligomeric state and most of its secondary structure, while displaying a significative increase in the heterogeneity of the tyrosines signal, increasing the temperature from ≈15 to ≈35 °C. The peculiar crowding of tyrosine residues (12 out of 18) at the three subunit interfaces and the strong dependence on the trimer concentration indicate that such conformational changes mainly involve the contact areas between each pair of monomers, affecting the oligomeric state. Molecular dynamic simulations in this temperature range suggest that the interfaces heterogeneity is an intrinsic property of the trimer that arises from the continuous, asymmetric approaching and distancing of its subunits. Such dynamics affect the results of molecular docking on the external protein surface using receptor peptides, indicating that the TRAF2-receptor interaction in the solution might not involve three subunits at the same time, as suggested by the static analysis obtainable from the crystal structure. These findings shed new light on the role that the TRAF2 oligomeric state might have in regulating the protein binding activity in vivo.

Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies: Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations

In this study, we used an integrative computational approach to examine molecular mechanisms and determine functional signatures underlying the role of functional residues in the SARS-CoV-2 spike protein that are targeted by novel mutational variants and antibody-escaping mutations. Atomistic simulations and functional dynamics analysis are combined with alanine scanning and mutational sensitivity profiling of the SARS-CoV-2 spike protein complexes with the ACE2 host receptor and the REGN-COV2 antibody cocktail(REG10987+REG10933). Using alanine scanning and mutational sensitivity analysis, we have shown that K417, E484, and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 spike protein complexes with ACE2, we demonstrate that E406, N439, K417, and N501 residues serve as effector centers of allosteric interactions and anchor major intermolecular communities that mediate long-range communication in the complexes. The results provide support to a model according to which mutational variants and antibody-escaping mutations constrained by the requirements for host receptor binding and preservation of stability may preferentially select structurally plastic and energetically adaptable allosteric centers to differentially modulate collective motions and allosteric interactions in the complexes with the ACE2 enzyme and REGN-COV2 antibody combination. This study suggests that the SARS-CoV-2 spike protein may function as a versatile and functionally adaptable allosteric machine that exploits the plasticity of allosteric regulatory centers to fine-tune response to antibody binding without compromising the activity of the spike protein.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

Integrated Biophysical Modeling of the SARS-CoV-2 Spike Protein Binding and Allosteric Interactions with Antibodies

Structural and biochemical studies of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoproteins and complexes with highly potent antibodies have revealed multiple conformation-dependent epitopes highlighting conformational plasticity of spike proteins and capacity for eliciting specific binding and broad neutralization responses. In this study, we used coevolutionary analysis, molecular simulations, and perturbation-based hierarchical network modeling of the SARS-CoV-2 spike protein complexes with a panel of antibodies targeting distinct epitopes to explore molecular mechanisms underlying binding-induced modulation of dynamics and allosteric signaling in the spike proteins. Through coevolutionary analysis of the SARS-CoV-2 spike proteins, we identified highly coevolving hotspots and functional clusters that enable a functional cross-talk between distant allosteric regions in the SARS-CoV-2 spike complexes with antibodies. Coarse-grained and all-atom molecular dynamics simulations combined with mutational sensitivity mapping and perturbation-based profiling of the SARS-CoV-2 receptor-binding domain (RBD) complexes with CR3022 and CB6 antibodies enabled a detailed validation of the proposed approach and an extensive quantitative comparison with the experimental structural and deep mutagenesis scanning data. By combining in silico mutational scanning, perturbation-based modeling, and network analysis of the SARS-CoV-2 spike trimer complexes with H014, S309, S2M11, and S2E12 antibodies, we demonstrated that antibodies can incur specific and functionally relevant changes by modulating allosteric propensities and collective dynamics of the SARS-CoV-2 spike proteins. The results provide a novel insight into regulatory mechanisms of SARS-CoV-2 S proteins showing that antibody-escaping mutations can preferentially target structurally adaptable energy hotspots and allosteric effector centers that control functional movements and allosteric communication in the complexes.

Polymorphism on human aromatase affects protein dynamics and substrate binding: spectroscopic evidence

Human aromatase is a member of the cytochrome P450 superfamily, involved in steroid hormones biosynthesis. In particular, it converts androgen into estrogens being therefore responsible for the correct sex steroids balance. Due to its capacity in producing estrogens it has also been considered as a promising target for breast cancer therapy. Two single-nucleotide polymorphisms (R264C and R264H) have been shown to alter aromatase activity and they have been associated to an increased or decreased risk for estrogen-dependent pathologies. Here, the effect of these mutations on the protein dynamics is investigated by UV/FTIR and time resolved fluorescence spectroscopy. H/D exchange rates were measured by FTIR for the three proteins in the ligand-free, substrate- and inhibitor-bound forms and the data indicate that the wild-type enzyme undergoes a conformational change leading to a more compact tertiary structure upon substrate or inhibitor binding. Indeed, the H/D exchange rates are decreased when a ligand is present. In the variants, the exchange rates in the ligand-free and -bound forms are similar, indicating that a structural change is lacking, despite the single amino acid substitution is located in the peripheral shell of the protein molecule. Moreover, the fluorescence lifetimes data show that the quenching effect on tryptophan-224 observed upon ligand binding in the wild-type, is absent in both variants. Since this residue is located in the catalytic pocket, these findings suggest that substrate entrance and/or retention in the active site is partially compromised in both mutants. A contact network analysis demonstrates that the protein structure is organized in two main clusters, whose connectivity is altered by ligand binding, especially in correspondence of helix-G, where the amino acid substitutions occur. Our findings demonstrate that SNPs resulting in mutations on aromatase surface modify the protein flexibility that is required for substrate binding and catalysis. The cluster analysis provides a rationale for such effect, suggesting helix G as a possible target for aromatase inhibition.

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.

Analysis of the SARS-CoV-2-host protein interaction network reveals new biology and drug candidates: focus on the spike surface glycoprotein and RNA polymerase

Introduction: The COVID-19 pandemic originated from the emergence of anovel coronavirus, SARS-CoV-2, which has been intensively studied since its discovery in order to generate the knowledge necessary to accelerate the development of vaccines and antivirals. Of note, many researchers believe there is great potential in systematically identifying host interactors of viral factors already targeted by existing drugs.Areas Covered: Herein, the authors discuss in detail the only available large-scale systematic study of the SARS-CoV-2-host protein-protein interaction network. More specifically, the authors review the literature on two key SARS-CoV-2 drug targets, the Spike surface glycoprotein, and the RNA polymerase. The authors also provide the reader with their expert opinion and future perspectives.Expert opinion: Interactions made by viral proteins with host factors reveal key functions that are likely usurped by the virus and, as aconsequence, points to known drugs that can be repurposed to fight viral infection and collateral damages that can exacerbate various disease conditions in COVID-19.

Insight into Shared Properties and Differential Dynamics and Specificity of Secretory Phospholipase A2 Family Members

Understanding generic mechanisms of functions shared by the secretory phospholipase A₂ (sPLA₂) family involved in the lipid metabolism and cell signaling and the molecular basis of function specificity for family members is an intriguing but challenging problem for biologists. Here, we explore the issue through extensive analyses using a combination of structure-based methods and bioinformatics tools on130 sPLA₂ family members. The principal component analysis of the structure ensemble reveals that the enzyme has an open-close motion which helps widen the substrate binding channel, facilitating its binding to phospholipid. Performing elastic network model and sequence analyses found that the residues critical for family functions, such as cysteine and catalytic residues, are highly conserved and undergo minimal movements, which is evolutionarily essential as their perturbation would impact the function, while the four residue regions involved in the association with the calcium ion/membrane are lowly conserved and of high mobility and large variations in low-to-intermediate frequency modes, which reflects the specificity of members. The analyses from perturbation response scanning also reveal that the above four regions with high sensitivity to an external perturbation are member-specific, suggesting their different roles in allosteric modulation, while the minimal sensitive residues are the shared characteristics across family members, which play an important role in maintaining structural stability as the folding core. This study is helpful for understanding how sequences, structures, and dynamics of sPLA₂ family members evolve to ensure their common and specific functions and can provide a guide for accurate design of proteins with finely tuned activities.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches

The rapidly growing body of structural and biochemical studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examined molecular mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic analysis of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained molecular simulations, protein stability analysis, and perturbation-based modeling of residue interaction networks, we examined how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the experimentally confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and determine topography of signal communication pathways operating through state-specific cascades of control switch points. This analysis provides a plausible strategy for allosteric probing of the conformational equilibrium and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.

Clinical HDAC Inhibitors Are Effective Drugs to Prevent the Entry of SARS-CoV2

The outbreak of COVID-19 by the end of 2019 has posed serious health threats to humanity and jeopardized the global economy. However, no effective drugs are available to treat COVID-19 currently and there is a great demand to fight against it. Here, we combined computational screening and an efficient cellular pseudotyped virus system, confirming that clinical HDAC inhibitors can efficiently prevent SARS-CoV-2 and potentially be used to fight against COVID-19.

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

A highly infectious coronavirus, SARS-CoV-2, has spread in many countries. This virus recognizes its receptor, angiotensin-converting enzyme 2 (ACE2), using the receptor binding domain of its spike protein subunit S1. Many missense mutations are reported in various human populations for the ACE2 gene. In the current study, we predict the affinity of many ACE2 variants for binding to S1 protein using different computational approaches. The dissociation process of S1 from some variants of ACE2 is studied in the current work by molecular dynamics approaches. We study the relation between structural dynamics of ACE2 in closed and open states and its affinity for S1 protein of SARS-CoV-2.

Molecular Simulations and Network Modeling Reveal an Allosteric Signaling in the SARS-CoV-2 Spike Proteins

The development of computational strategies for the quantitative characterization of the functional mechanisms of SARS-CoV-2 spike proteins is of paramount importance in efforts to accelerate the discovery of novel therapeutic agents and vaccines combating the COVID-19 pandemic. Structural and biophysical studies have recently characterized the conformational landscapes of the SARS-CoV-2 spike glycoproteins in the prefusion form, revealing a spectrum of stable and more dynamic states. By employing molecular simulations and network modeling approaches, this study systematically examined functional dynamics and identified the regulatory centers of allosteric interactions for distinct functional states of the wild-type and mutant variants of the SARS-CoV-2 prefusion spike trimer. This study presents evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. Perturbation-based modeling of the interaction networks revealed a key role of the cross-talk between the effector hotspots in the receptor binding domain and the fusion peptide proximal region of the SARS-CoV-2 spike protein. The results have shown that the allosteric hotspots of the interaction networks in the SARS-CoV-2 spike protein can control the dynamic switching between functional conformational states that are associated with virus entry to the host receptor. This study offers a useful and novel perspective on the underlying mechanisms of the SARS-CoV-2 spike protein through the lens of allosteric signaling as a regulatory apparatus of virus transmission that could open up opportunities for targeted allosteric drug discovery against SARS-CoV-2 proteins and contribute to the rapid response to the current and potential future pandemic scenarios.

Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of the SARS-CoV and SARS-CoV-2 spike protein receptor binding domains with the ACE2 host receptor. Different from other computational studies, we systematically examine the molecular and energetic determinants of the binding mechanisms between SARS-CoV-2 and ACE2 proteins through the lens of coevolution, conformational dynamics, and allosteric interactions that conspire to drive binding interactions and signal transmission. Conformational dynamics analysis revealed the important differences in mobility of the binding interfaces for the SARS-CoV-2 spike protein that are not confined to several binding hotspots, but instead are broadly distributed across many interface residues. Through coevolutionary network analysis and dynamics-based alanine scanning, we established linkages between the binding energy hotspots and potential regulators and carriers of signal communication in the virus-host receptor complexes. The results of this study detailed a binding mechanism in which the energetics of the SARS-CoV-2 association with ACE2 may be determined by cumulative changes of a number of residues distributed across the entire binding interface. The central findings of this study are consistent with structural and biochemical data and highlight drug discovery challenges of inhibiting large and adaptive protein-protein interfaces responsible for virus entry and infection transmission.

Molecular Insights into Human Transmembrane Protease Serine-2 (TMPS2) Inhibitors against SARS-CoV2: Homology Modelling, Molecular Dynamics, and Docking Studies

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), which caused novel corona virus disease-2019 (COVID-19) pandemic, necessitated a global demand for studies related to genes and enzymes of SARS-CoV2. SARS-CoV2 infection depends on the host cell Angiotensin-Converting Enzyme-2 (ACE2) and Transmembrane Serine Protease-2 (TMPRSS2), where the virus uses ACE2 for entry and TMPRSS2 for S protein priming. The TMPRSS2 gene encodes a Transmembrane Protease Serine-2 protein (TMPS2) that belongs to the serine protease family. There is no crystal structure available for TMPS2, therefore, a homology model was required to establish a putative 3D structure for the enzyme. A homology model was constructed using SWISS-MODEL and evaluations were performed through Ramachandran plots, Verify 3D and Protein Statistical Analysis (ProSA). Molecular dynamics simulations were employed to investigate the stability of the constructed model. Docking of TMPS2 inhibitors, camostat, nafamostat, gabexate, and sivelestat, using Molecular Operating Environment (MOE) software, into the constructed model was performed and the protein-ligand complexes were subjected to MD simulations and computational binding affinity calculations. These in silico studies determined the tertiary structure of TMPS2 amino acid sequence and predicted how ligands bind to the model, which is important for drug development for the prevention and treatment of COVID-19.