Structural implications of SARS-CoV-2 Surface Glycoprotein N501Y mutation within receptor-binding domain [499-505]–computational analysis of the most frequent Asn501 polar uncharged amino acid mutations

The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD–hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD–hACE2 solubility over N501T, N501 (wild type): (Formula presented.), (Formula presented.). The N501Y mutation shifts (Formula presented.) -helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from (Formula presented.) -helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY (Formula presented.) 's RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the (Formula presented.) hydrogen bond into permanent polar contact;(Formula presented.);(Formula presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics

The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms. [ FROM AUTHOR] Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Rational Repurposing of Drugs, Clinical Trial Candidates, and Natural Products for SARS-CoV-2 Therapy

In the last 20 years, the world has been threatened with coronavirus (CoV) pandemic threats from severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and finally COVID-19 due to SARS-CoV-2 in 2019. These viruses posed serious global pandemic threats, with estimated case fatality rates of 15% for SARS-CoV, 34% for MERS-CoV, and 1-3% for SARS-CoV-2. With the current pandemic still far from over there is an urgent need to find new drug treatments for COVID-19. We can assume that this will not be the last coronavirus to threaten humanity, so we need better tools to identify drugs active against past but also future coronavirus threats. In this Chapter we describe in silico computer modeling and screening approaches that can rapidly identify drugs from existing drug libraries that could be repurposed to treat COVID-19 infections. We also describe how this computational screening pipeline can be expanded in the future to identify drugs with broad spectrum activity against a wide diversity of coronaviruses. A significant concern is that the protection against CoVs provided by single drugs protection may be short-lived because viruses rapidly mutate to develop drug resistance. We know from other viruses such as HIV that drugs hitting multiple targets within the virus provide better protection against the development of resistance. This Chapter describes the current state of development of in silico CoV drug repurposing, the challenges and pitfalls of these approaches, and our predictions of how these methods could be used to develop drugs for future pandemics before they occur. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

In silico screening of potential compounds from begonia genus as 3CL protease (3Cl pro) SARS-CoV-2 inhibitors

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective(s): This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases "promising" for the anti-SARS-CoV-2 target. Method(s): In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Result(s): Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only beta-sitosterol-3-O-beta-D-glucopyranoside with a lower docking score of -9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 A, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 A and 0.773 A, respectively. Conclusion(s): This result suggests that beta-sitosterol-3-O-beta-Dglucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.Copyright © 2023, Page Press Publications. All rights reserved.

Inhibition of SARS-CoV2 Replication Regulated by NSP3 through Ayurveda Phytoconstituents of Indian Traditional Medicine

With Covid-19, a significant proportion of the population who are already vaccinated have tested positive. Therefore, there is a need for better medicines that act against the virus rigorously without causing any side effects. We aim to achieve the same through molecular docking and further simulations for bioactive phytochemicals of ayurvedic medicinal plants. The target for this study has been considered the NSP3 protein of the viral RNA that actively takes part in both replication and immune evasion pathways of the virus. Ligand libraries consisting of bioactive phytochemicals of aswasgandha and analogues of curcumin and piperine are curated. The libraries, along with the NSP3 protein moiety are docked onto two active sites. With the best-scored complexes further taken up for molecular dynamics simulation, the study resulted in favourable outcomes for three such ligands (compound ID 5469426, 69501714, ZINC000003874317). © 2023 Bharati Vidyapeeth, New Delhi.

SARS-CoV-2 main protease targeting potent fluorescent inhibitors: Repurposing thioxanthones

The coronavirus disease, COVID-19, is the major focus of the whole world due to insufficient treatment options. It has spread all around the world and is responsible for the death of numerous human beings. The future consequences for the disease survivors are still unknown. Hence, all contributions to understand the disease and effectively inhibit the effects of the disease have great importance. In this study, different thioxanthone based molecules, which are known to be fluorescent compounds, were selectively chosen to study if they can inhibit the main protease of SARS-CoV-2 using various computational tools. All candidate ligands were optimized, molecular docking and adsorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were conducted and subsequently, some were subjected to 100 ns molecular dynamics simulations in conjunction with the known antiviral drugs, favipiravir, and hydroxychlo-roquine. It was found that different functional groups containing thioxanthone based molecules are capable of different intermolecular interactions. Even though most of the studied ligands showed stable interactions with the main protease, para-oxygen-di-acetic acid functional group containing thioxanthone was found to be a more effective inhibitor due to the higher number of intermolecular inter-actions and higher stability during the simulations.

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation.

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using 'PaDEL' and 'ChemDes' software, and then redundant and non-significant descriptors were eliminated by a module in 'QSARINS ver. 2.2.2'. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.

Identification of key mutations responsible for the enhancement of receptor-binding affinity and immune escape of SARS-CoV-2 Omicron variant.

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised concerns worldwide due to its enhanced transmissibility and immune escapability. The first dominant Omicron BA.1 subvariant harbors more than 30 mutations in the spike protein from the prototype virus, of which 15 mutations are located at the receptor binding domain (RBD). These mutations in the RBD region attracted significant attention, which potentially enhance the binding of the receptor human angiotensin-converting enzyme 2 (hACE2) and decrease the potency of neutralizing antibodies/nanobodies. This study applied the molecular dynamics simulations combined with the molecular mechanics-generalized Born surface area (MMGBSA) method, to investigate the molecular mechanism behind the impact of the mutations acquired by Omicron on the binding affinity between RBD and hACE2. Our results indicate that five key mutations, i.e., N440K, T478K, E484A, Q493R, and G496S, contributed significantly to the enhancement of the binding affinity by increasing the electrostatic interactions of the RBD-hACE2 complex. Moreover, fourteen neutralizing antibodies/nanobodies complexed with RBD were used to explore the effects of the mutations in Omicron RBD on their binding affinities. The calculation results indicate that the key mutations E484A and Y505H reduce the binding affinities to RBD for most of the studied neutralizing antibodies/nanobodies, mainly attributed to the elimination of the original favorable gas-phase electrostatic and hydrophobic interactions between them, respectively. Our results provide valuable information for developing effective vaccines and antibody/nanobody drugs.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes.

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase.

BACKGROUND: Human neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. METHOD: AutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85-8501 and the highly active BAY-8040 were calculated with sc and tcHNE. RESULTS: The DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1'-S2' subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85-8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85-8501 is likely to be inactive. CONCLUSION: SAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

Nanovectorization of Ivermectin to avoid overdose of drugs.

Ivermectin is an antiparasitic drug that results in the death of the targeted parasites using several mechanical actions. While very well supported, it can induce in rare cases, adverse effects including coma and respiratory failure in case of overdose. This problem should be solved especially in an emergency situation. For instance, the first pandemic of the 21th century was officially declared in early 2020, and while several vaccines around the worlds have been used, an effective treatment against this new strain of coronavirus, better known as SARS-CoV-2, should also be considered, especially given the massive appearance of variants. From all the tested therapies, Ivermectin showed a potential reduction of the viral portability, but sparked significant debate around the dose needed to achieve these positive results. To answer this general question, we propose, using simulations, to show that the nanovectorization of Ivermectin on BN oxide nanosheets can increase the transfer of the drug to its target and thus decrease the quantity of drug necessary to cope with the disease. This first application could help science to develop such nanocargo to avoid adverse effects.Communicated by Ramaswamy H. Sarma.

Promising antivirals for PLpro of SARS-CoV-2 using virtual screening, molecular docking, dynamics, and MMPBSA.

The recent pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) is a viral respiratory disease that has been spread all over the globe. Therefore, it is an urgent requirement to identify and develop drugs for this contagious infection. The papain-like protease (PLpro) of SARS-CoV-2 performs critical functions in virus replication and immune evasion, making it an enticing therapeutic target. SARS-CoV-2 and SARS-CoV PLpro proteases have significant similarities, and an inhibitor discovered for SARS-CoV PLpro is an exciting first step toward therapeutic development. Here, a set of antiviral molecules were screened at the catalytic and S-binding allosteric sites of papain-like protease (PLpro). Molecular docking results suggested that five molecules (44560613, 136277567, S5652, SC75741, and S3833) had good binding affinities at both sites of PLpro. Molecular dynamics analysis like root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), and hydrogen bond results showed that identified molecules with PLpro tend to form stable PLpro-inhibitor(s) complexes. Molecular Mechanics/Position-Boltzmann Surface Area (MMPBSA) analysis confirmed that antiviral molecules bound PLpro complex had lower energy (-184.72 ± 7.81 to -215.67 ± 6.73 kJ/mol) complexes. Noticeably, computational approaches revealed promising antivirals candidates for PLpro, which may be further tested by biochemical and cell-based assays to assess their potential for SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach.

Monkeypox virus (mpox virus) outbreak has rapidly spread to 82 non-endemic countries. Although it primarily causes skin lesions, secondary complications and high mortality (1-10%) in vulnerable populations have made it an emerging threat. Since there is no specific vaccine/antiviral, it is desirable to repurpose existing drugs against mpox virus. With little knowledge about the lifecycle of mpox virus, identifying potential inhibitors is a challenge. Nevertheless, the available genomes of mpox virus in public databases represent a goldmine of untapped possibilities to identify druggable targets for the structure-based identification of inhibitors. Leveraging this resource, we combined genomics and subtractive proteomics to identify highly druggable core proteins of mpox virus. This was followed by virtual screening to identify inhibitors with affinities for multiple targets. 125 publicly available genomes of mpox virus were mined to identify 69 highly conserved proteins. These proteins were then curated manually. These curated proteins were funnelled through a subtractive proteomics pipeline to identify 4 highly druggable, non-host homologous targets namely; A20R, I7L, Top1B and VETFS. High-throughput virtual screening of 5893 highly curated approved/investigational drugs led to the identification of common as well as unique potential inhibitors with high binding affinities. The common inhibitors, i.e., batefenterol, burixafor and eluxadoline were further validated by molecular dynamics simulation to identify their best potential binding modes. The affinity of these inhibitors suggests their repurposing potential. This work can encourage further experimental validation for possible therapeutic management of mpox.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Immunoinformatics Approaches in Designing Vaccines Against COVID-19.

Since the onset of the COVID-19 pandemic, a number of approaches have been adopted by the scientific communities for developing efficient vaccine candidate against SARS-CoV-2. Conventional approaches of developing a vaccine require a long time and a series of trials and errors which indeed limit the feasibility of such approaches for developing a dependable vaccine in an emergency situation like the COVID-19 pandemic. Hitherto, most of the available vaccines have been developed against a particular antigen of SARS-CoV, spike protein in most of the cases, and intriguingly, these vaccines are not effective against all the pathogenic coronaviruses. In this context, immunoinformatics-based reverse vaccinology approaches enable a robust design of efficacious peptide-based vaccines against all the infectious strains of coronaviruses within a short frame of time. In this chapter, we enumerate the methodological trajectory of developing a universal anti-SARS-CoV-2 vaccine, namely, "AbhiSCoVac," through advanced computational biology-based immunoinformatics approach and its in-silico validation using molecular dynamics simulations.

In-silico study for the identification of potential destabilizers between the spike protein of SARS-CoV-2 and human ACE-2.

The emergence of the new SARS-CoV-2 virus, which causes the disease known as COVID-19, has generated a pandemic that has plunged the world into a health crisis. The infection process is triggered by the direct binding of the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) of the host cell. In the present study, virtual screening techniques such as molecular docking, molecular dynamics, calculation of free energy using the GBSA method, prediction of drug similarity, pharmacokinetic, and toxicological properties of various ligands interacting with the RBD-ACE2 complex were applied. The ligands radotinib, hinokiflavone, and ginkgetin were identified as potential destabilizers of the RBD-ACE2 interaction, which could produce their pharmacological effect by interacting at an allosteric site of ACE2, with affinity energy values of -10.2 ± 0.1, -9.8 ± 0.0, and -9.4 ± 0.0 kcal/mol, indicating strong receptor affinity. The complex with hinokiflavone showed the highest conformational stability and rigidity of the dynamic simulation and also obtained the best binding free energy of the three molecules, with an energy of -215.86 kcal/mol.

Identification and characterization of 7-azaindole derivatives as inhibitors of the SARS-CoV-2 spike-hACE2 protein interaction.

The COVID-19 pandemic, caused by SARS-CoV-2, has become a global public health crisis. The entry of SARS-CoV-2 into host cells is facilitated by the binding of its spike protein (S1-RBD) to the host receptor hACE2. Small molecule compounds targeting S1-RBD-hACE2 interaction could provide an alternative therapeutic strategy sensitive to viral mutations. In this study, we identified G7a as a hit compound that targets the S1-RBD-hACE2 interaction, using high-throughput screening in the SARS2-S pseudovirus model. To enhance the antiviral activity of G7a, we designed and synthesized a series of novel 7-azaindole derivatives that bind to the S1-RBD-hACE2 interface. Surprisingly, ASM-7 showed excellent antiviral activity and low cytotoxicity, as confirmed by pseudovirus and native virus assays. Molecular docking and molecular dynamics simulations revealed that ASM-7 could stably bind to the binding interface of S1-RBD-hACE2, forming strong non-covalent interactions with key residues. Furthermore, the binding of ASM-7 caused alterations in the structural dynamics of both S1-RBD and hACE2, resulting in a decrease in their binding affinity and ultimately impeding the viral invasion of host cells. Our findings demonstrate that ASM-7 is a promising lead compound for developing novel therapeutics against SARS-CoV-2.

Exploiting the potential of natural polyphenols as antivirals against monkeypox envelope protein F13 using machine learning and all-atoms MD simulations.

The re-emergence of monkeypox (MPX), in the era of COVID-19 pandemic is a new global menace. Regardless of its leniency, there are chances of MPX expediting severe health deterioration. The role of envelope protein, F13 as a critical component for production of extracellular viral particles makes it a crucial drug target. Polyphenols, exhibiting antiviral properties have been acclaimed as an effective alternative to the traditional treatment methods for management of viral diseases. To facilitate the development of potent MPX specific therapeutics, herein, we have employed state-of-the-art machine learning techniques to predict a highly accurate 3-dimensional structure of F13 as well as identify binding hotspots on the protein surface. Additionally, we have effectuated high-throughput virtual screening methodology on 57 potent natural polyphenols having antiviral activities followed by all-atoms molecular dynamics (MD) simulations, to substantiate the mode of interaction of F13 protein and polyphenol complexes. The structure-based virtual screening based on Glide SP, XP and MM/GBSA scores enables the selection of six potent polyphenols having higher binding affinity towards F13. Non-bonded contact analysis, of pre- and post- MD complexes propound the critical role of Glu143, Asp134, Asn345, Ser321 and Tyr320 residues in polyphenol recognition, which is well supported by per-residue decomposition analysis. Close-observation of the structural ensembles from MD suggests that the binding groove of F13 is mostly hydrophobic in nature. Taken together, this structure-based analysis from our study provides a lead on Myricetin, and Demethoxycurcumin, which may act as potent inhibitors of F13. In conclusion, our study provides new insights into the molecular recognition and dynamics of F13-polyphenol bound states, offering new promises for development of antivirals to combat monkeypox. However, further in vitro and in vivo experiments are necessary to validate these results.

Monomeric and dimeric states of human ZO1-PDZ2 are functional partners of the SARS-CoV-2 E protein.

The Envelope (E) protein of SARS-CoV-2 plays a key role in virus maturation, assembly, and virulence mechanisms. The E protein is characterized by the presence of a PDZ-binding motif (PBM) at its C-terminus that allows it to interact with several PDZ-containing proteins in the intracellular environment. One of the main binding partners of the SARS-CoV-2 E protein is the PDZ2 domain of ZO1, a protein with a crucial role in the formation of epithelial and endothelial tight junctions (TJs). In this work, through a combination of analytical ultracentrifugation analysis and equilibrium and kinetic folding experiments, we show that ZO1-PDZ2 domain is able to fold in a monomeric state, an alternative form to the dimeric conformation that is reported to be functional in the cell for TJs assembly. Importantly, surface plasmon resonance (SPR) data indicate that the PDZ2 monomer is fully functional and capable of binding the C-terminal portion of the E protein of SARS-CoV-2, with a measured affinity in the micromolar range. Moreover, we present a detailed computational analysis of the complex between the C-terminal portion of E protein with ZO1-PDZ2, both in its monomeric conformation (computed as a high confidence AlphaFold2 model) and dimeric conformation (obtained from the Protein Data Bank), by using both polarizable and nonpolarizable simulations. Together, our results indicate both the monomeric and dimeric states of PDZ2 to be functional partners of the E protein, with similar binding mechanisms, and provide mechanistic and structural information about a fundamental interaction required for the replication of SARS-CoV-2.