A phenoxy-bridged trinuclear Ni(ii) complex: synthesis, structural elucidation and molecular docking with viral proteins

A novel phenoxy-bridged trinuclear nickel(ii) complex [Ni3(mu-L)2(bipy)3](1) (where H3L= (E)-2-hydroxy-N-(2-hydroxy-3,5-diiodophenyl)-3,5-diiodobenzohydrazonic acid, bipy = 2,2'-bipyridyl) has been designed and synthesized as a potential antivirus drug candidate. The trinuclear Ni(ii) complex [Ni3(mu-L)2(bipy)3](1) was fully characterized via single crystal X-ray crystallography. The unique structure of the trinuclear nickel(ii) complex crystallized in a trigonal crystal system with P3221 space group and revealed distorted octahedral coordination geometry around each Ni(ii) ion. The X-ray diffraction analysis established the existence of a new kind of trinuclear metal system containing nickel(ii)-nickel(ii) interactions with an overall octahedral-like geometry about the nickel(ii) atoms. The non-bonded Ni-Ni distance seems to be 3.067 and 4.455 A from the nearest nickel atoms. The detailed structural analysis and non-covalent supramolecular interactions are also investigated by single crystal structure analysis and computational approaches. Hirshfeld surfaces (HSs) and 2D fingerprint plots (FPs) have been explored in the crystal structure to investigate the intermolecular interactions. The preliminary analysis of redox and magnetic characterization was conducted using cyclic voltammetry measurements and a vibrating sample magnetometer (VSM), respectively. This unique structure shows good inhibition performance for SARS-CoV-2, Omicron and HIV viruses. For insight into the potential application of the Ni(ii) coordination complex as an effective antivirus drug, we have examined the molecular docking of the trinuclear Ni(ii) complex [Ni3(mu-L)2(bipy)3](1) with the receptor binding domain (RBD) from SARS-CoV-2 (PDB ID: 7MZF), Omicron BA.3 variant spike (PDB ID: 7XIZ), and HIV protease (PDB ID: 7WCQ) viruses. This structure shows good inhibition performance for SARS-CoV-2, Omicron S protein and HIV protease viruses;the binding energies (DELTAG) and the respective Ki/Kd (inhibition/dissociation constants) correlation values are -8.9 (2.373 muM or 2373 nM), -8.1 (1.218 muM or 1218 nM) and -7.9 (0.874 muM or 874 nM), respectively. The results could be used for rational drug design against SARS-CoV-2 Omicron variant and HIV protease viruses.Copyright © 2023 The Royal Society of Chemistry.

Revealing the structural and molecular interaction landscape of the favipiravir-RTP and SARS-CoV-2 RdRp complex through integrative bioinformatics: Insights for developing potent drugs targeting SARS-CoV-2 and other viruses.

BACKGROUND: The global research community has made considerable progress in therapeutic and vaccine research during the COVID-19 pandemic. Several therapeutics have been repurposed for the treatment of COVID-19. One such compound is, favipiravir, which was approved for the treatment of influenza viruses, including drug-resistant influenza. Despite the limited information on its molecular activity, clinical trials have attempted to determine the effectiveness of favipiravir in patients with mild to moderate COVID-19. Here, we report the structural and molecular interaction landscape of the macromolecular complex of favipiravir-RTP and SARS-CoV-2 RdRp with the RNA chain. METHODS: Integrative bioinformatics was used to reveal the structural and molecular interaction landscapes of two macromolecular complexes retrieved from RCSB PDB. RESULTS: We analyzed the interactive residues, H-bonds, and interaction interfaces to evaluate the structural and molecular interaction landscapes of the two macromolecular complexes. We found seven and six H-bonds in the first and second interaction landscapes, respectively. The maximum bond length is 3.79 Å. In the hydrophobic interactions, five residues (Asp618, Asp760, Thr687, Asp623, and Val557) were associated with the first complex and two residues (Lys73 and Tyr217) were associated with the second complex. The mobilities, collective motion, and B-factor of the two macromolecular complexes were analyzed. Finally, we developed different models, including trees, clusters, and heat maps of antiviral molecules, to evaluate the therapeutic status of favipiravir as an antiviral drug. CONCLUSIONS: The results revealed the structural and molecular interaction landscape of the binding mode of favipiravir with the nsp7-nsp8-nsp12-RNA SARS-CoV-2 RdRp complex. Our findings can help future researchers in understanding the mechanism underlying viral action and guide the design of nucleotide analogs that mimic favipiravir and exhibit greater potency as antiviral drugs against SARS-CoV-2 and other infectious viruses. Thus, our work can help in preparing for future epidemics and pandemics.

A new short synthesis route for favipiravir and its analogue: Their tautomerization behaviour

The study of tautomerism in biologically relevant heterocycles is essential, as it directly affects their chemical properties and biological function. Lactam-lactim tautomerization in pyridine/pyrazine derivatives is such a phenomenon. Favipiravir, a pyrazine derivative, is an essential antiviral drug molecule having notable performance against SARS-CoV-2. Along with a better yielding synthetic method for favipiravir, we have also investigated the lactam-lactim tautomerization of favipiravir and its analogous molecules. Most of these molecules were crystalized and studied for various interactions in their lattice. Many interesting supramolecular interactions such as hydrogen bonding, pi-pi stacking and halogen bonding were revealed during the analysis. Some of these structures show interesting F-F halogen bonding and water channels in their solid state.Copyright © 2022 The Royal Society of Chemistry.

Electronic, intermolecular, quantum computational investigations, molecular docking and simulation studies of the potent antiviral drug EIDD-2801

In this work, an analysis has been done to describe the molecular structure, spectroscopic, reduced density gradient, topological properties, atomic charges, Lipinski rule, Natural bond orbital analysis, docking and molecular dynamics simulation of the potent antiviral drug EIDD-2801 in the effective treatment against COVID-19. Intramolecular charge distribution is well understood by three schemes such as AIM, Mulliken and NBO analysis and non-covalent interactions have been understood through reduced density gradient. Topological properties, such as charge density and Laplacian of charge density along with the electron localization function, make it easy to obtain comprehensive information about bond strengths and critical points. The details obtained from the calculation of global reactivity descriptors and Lipinski rule are useful for understanding the nature of molecular reactivity and site selectivity. Electrostatic potentials help to identify potential electrophilic and nucleophilic sites for interaction between EIDD-2801 and target proteins. The molecular docking combined with molecular dynamic simulation studies enables us to get better picture about the ligand-protein interaction.Copyright © 2023 Indian Chemical Society

Dissection of Antibody Responses of Gam-COVID-Vac-Vaccinated Subjects Suggests Involvement of Epitopes Outside RBD in SARS-CoV-2 Neutralization.

Millions of people have been vaccinated with Gam-COVID-Vac but fine specificities of induced antibodies have not been fully studied. Plasma from 12 naïve and 10 coronavirus disease 2019 (COVID-19) convalescent subjects was obtained before and after two immunizations with Gam-COVID-Vac. Antibody reactivity in the plasma samples (n = 44) was studied on a panel of micro-arrayed recombinant folded and unfolded severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins and 46 peptides spanning the spike protein (S) and by immunoglobulin G (IgG) subclass enzyme-linked immunosorbent assay (ELISA). The ability of Gam-COVID-Vac-induced antibodies to inhibit binding of the receptor-binding domain (RBD) to its receptor angiotensin converting enzyme 2 (ACE2) was investigated in a molecular interaction assay (MIA). The virus-neutralizing capacity of antibodies was studied by the pseudo-typed virus neutralization test (pVNT) for Wuhan-Hu-1 and Omicron. We found that Gam-COVID-Vac vaccination induced significant increases of IgG1 but not of other IgG subclasses against folded S, spike protein subunit 1 (S1), spike protein subunit 2 (S2), and RBD in a comparable manner in naïve and convalescent subjects. Virus neutralization was highly correlated with vaccination-induced antibodies specific for folded RBD and a novel peptide (i.e., peptide 12). Peptide 12 was located close to RBD in the N-terminal part of S1 and may potentially be involved in the transition of the pre- to post-fusion conformation of the spike protein. In summary, Gam-COVID-Vac vaccination induced S-specific IgG1 antibodies in naive and convalescent subjects in a comparable manner. Besides the antibodies specific for RBD, the antibodies induced against a peptide close to the N-terminus of RBD were also associated with virus-neutralization.

Further quantitative in silico analysis of SARS-CoV-2 S-RBD Omicron BA.4, BA.5, BA.2.75, BQ.1, and BQ.1.1 transmissibility.

The Covid-19 variants' transmissibility was further quantitatively analyzed in silico to study the binding strength with ACE-2 and find the binding inhibitors. The molecular interaction energy values of their optimized complex structures (MIFS) demonstrated that Omicron BA.4 and 5's MIFS value (344.6 kcal mol-1) was equivalent to wild-type MIFS (346.1 kcal mol-1), that of Omicron BQ.1 and BQ. 1.1's MIFS value (309.9 and 364.6 kcal mol-1). Furthermore, the MIFS value of Omicron BA.2.75 (515.1 kcal mol-1) was about Delta-plus (511.3 kcal mol-1). The binding strength of Omicron BA.4, BA. 5, and BQ.1.1 may be neglectable, but that of Omicron BA.2.75 was urging. Furthermore, the 79 medicine candidates were analyzed as the binding inhibitors from binding strength with ACE-2. Only carboxy compounds were repulsed from the ACE-2 binding site indicating that further modification of medical treatment candidates may produce an effective binding inhibitor.

Molecular simulation of compounds from n-hexane fraction of Sonchus arvensis L. leaves as SARS-CoV-2 antiviral through inhibitor activity targeting strategic viral protein

Context: COVID-19 was caused by the spread and transmission of SARS-CoV-2 at the end of 2019 until now. The problem comes when antiviral drugs have not yet been found and patients infected with SARS-CoV-2 can trigger a cytokine storm condition due to the effects of viral replication. Indonesia has various kinds of medicinal plants, such as Sonchus arvensis L., which are used as medicinal plants. Aim(s): To analyze the activity of the inhibitor as SARS-CoV-2 antiviral agents from n-hexane fractions of S. arvensis leaves. Method(s): The sample was collected from GC-MS analysis, PubChem, and Protein Databank database, then drug-likeness identification using Lipinski Rule of Five server and bioactive prediction of bioactive compounds as inhibitor activity was conducted by Molinspiration server. Furthermore, the docking simulation was performed using PyRx 0.9.9 software to determine the binding activity, molecular interaction by Discovery Studio software to identify position and interaction type, 3D molecular visualization by PyMol 2.5. software, and dynamic by CABS-flex 2.0 server to predict interaction stability. Result(s): alpha-Amyrin and beta-amyrin from n-hexane fractions of S. arvensis leaves had activity as SARS-CoV-2 inhibitors through interactions on helicase, RdRp, Mpro, and RBD-Spike, both compounds had more negative binding affinity than control drug and can produce stable chemical bond interactions in the ligand-protein complexes. However, the results were merely computational, so they must be validated through an in vivo and in vitro research approach. Conclusion(s): Sonchus arvensis L. leaves were predicted to have SARS-CoV-2 antiviral through inhibitor activity by alpha-amyrin and beta-amyrin. Copyright © 2022 Journal of Pharmacy & Pharmacognosy Research.

Characterization of adenovirus interactions with PF4 using a novel ELISA-qPCR technology

The COVID vaccines Janssen and AstraZeneca, based respectively on adenovirus (AdV) serotypes AdV26 and ChAdOx1, have been associated with rare cases of vaccine-induced thrombotic thrombocytopenia (VITT). It was recently demonstrated that the AdVs of the vaccines can bind to the blood protein platelet factor 4 (PF4), an interaction very likely to be involved in VITT. Since there are hundreds of known AdV serotypes, we hypothesized that certain serotypes have a lower affinity for PF4. We therefore aimed to screen a library comprising dozens of serotypes from different AdV species. For this purpose, we established the ELISA-qPCR technology. Like in standard ELISA, AdV viral particles are allowed to specifically interact with PF4 proteins coated on a plate. However, the revelation is not performed by antibody staining, but by qPCR after the genomes of bound AdVs are released through alkaline heat lysis. This technology enables fast, accurate and unbiased assessment of virus molecular interactions. Unlike most tested serotypes, the species D AdV37, AdV69 and AdV70 did not bind to PF4. Even though the ELISA-qPCR technique is not sensitive enough to detect potential low-affinity interactions, these serotypes may avoid or decrease the risk of VITT and represent safer candidates for vaccine or gene therapy vector development. In order to gain deeper insights into the mechanism of virion binding to PF4, we tested how AdV5 affinity for PF4 was affected by genetic removal or PEGylation of different hypervariable regions (HVR) of the hexon protein of the capsid.

Protein-ligand binding affinity prediction with edge awareness and supervised attention.

Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

A Spotlight on the Development, Pharmaceutical Trends, Innovations and Patents of Nirmatrelvir (Paxlovid™)

Paxlovid™ is a combination of Nirmatrelvir and Ritonavir antiviral pills with good oral bioavailability. In clinical studies, treatment of the patients infected with SARS-CoV-2 with Paxlovid™ within three to five days of the appearance of symptoms significantly reduced the hospitalization rate as well as mortality. It is the first oral antiviral treatment for the COVID-19 which received USFDA approval for EUA on 22nd December, 2021. Nirmatrelvir inhibits the replication of SARS-CoV-2 while another antiviral drug, Ritonavir, is given in combination to enhance the bioavailability of Nirmatrelvir. Molecular interaction studies have shown that Nirmatrelvir binds covalently with the catalytic triad of the active site of the viral protease enzyme (3CLPRO). It, therefore, acts by stopping the SARS-CoV-2 replication by its ability to block the translation of the viral genetic materials. Research studies conducted have proven the efficacy of this oral anti-viral drug in mild to moderate COVID-19 patients beside its ease of oral administration and good oral bioavailability. Alternative synthetic methods to scale up the synthesis of this potent molecule are needed to reduce the treatment cost of the COVID-19. Extensive clinical research on a larger group population is also underway for ensuring the safety and efficacy of this medication in the battle against the COVID-19 pandemic.

Mutation Pattern in the Receptor Binding Motif of SARS-Cov-2 Variants and the Effect on Molecular Interactions in Docked Ligand Complexes

The spike glycoprotein of SARS-Cov-2 is a therapeutic target for Covid-19 and mutations in the Receptor Binding Motif (RBM) may alter the binding properties of ligands proposed to inhibit viral entry. This study aimed to identify the existence of a mutation pattern in the RBMs of SARS-Cov-2 variants and study the effect on ligand binding interactions. RBM sequences were obtained using NCBI BLASTP and subjected to multiple and pairwise sequence alignment analysis. Hypothetical generations were drawn from the phylogenetic tree. The effect of mutation on ligand binding was studied by docking zafirlukast on selected RBMs. Molecular dynamics simulations were conducted to explain molecular interactions. The sequences at the same phylogenetic level showed higher similarity with the observed differences defined by the crystallized chain length. 6XDG_E, a leaf node sequence was 76% similar to 7NXA_E, a branch from the root, and had the highest mutation. Differences in sequence similarity across successive generations were based on mutations and crystallized chain length and the amino acid substitution is not predictable. Different bond types and binding affinities were observed as well as varying Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Region of Gyration (RoG) values for the RBMs in different variants. The RMSD, RMSF, and RoG did not differ significantly in the bound and free states of RBM from specific variants suggesting that the observed differences are attributable to amino acid substitutions. This information is crucial for drug development intended to block SARS-Cov-2 entry.

A computational evaluation of structural stability of omicron and delta mutations of SARS-CoV-2 spike proteins and human ACE-2 interactions.

Several more infectious SARS-CoV-2 variants have emerged globally since SARS-CoV-2 pandemic and the discovery of the first D614G variant of SARS-CoV-2 spike proteins in 2020. Delta (B.1.617.2) and Omicron (B.1.1.529) variants have proven to be of major concern out of all the reported variants, considering their influence on the virus' transmissibility and severity. This study aimed at evaluating the impact of mutations on these two variants on stability and molecular interactions between the viral Spike protein and human angiotensin converting enzyme-2 (hACE-2). The spike proteins receptor binding domain (RBD) was docked with the hACE-2 using HADDOCK servers. To understand and establish the effects of the mutations on the structural stability and flexibility of the RBD-hACE-2 complex, molecular dynamic (MD) simulation of the docked complex was performed and evaluated. The findings from both molecular docking analysis and binding free energy showed that the Omicron (OM) variant has high receptiveness towards hACE-2 versus Delta variant (DT), thereby, responsible for its increase in transmission. The structural stability and flexibility evaluation of variants' systems showed that mutations on DT and OM variants disturbed the stability of either the spike protein or the RBD-hACE-2 complex, with DT variant having greater instability impact. This study, therefore, assumed this obvious instability observed in DT variant might be associated or responsible for the reported severity in DT variant disease over the OM variant disease. This study provides molecular insight into the effects of OM and DT variants on stability and interactions between SARS-CoV-2 protein and hACE-2.

Some Selected Phytoconstituents from Rhus succedanea as SARS CoV-2 Main Protease and Spike protein (COVID-19) Inhibitors

Rhus succedanea (Anacardiaceae) was used to treat multiple human afflictions. Literary works demonstrate that it has many biological activities. Today's research aims to recognize Rhus succedanea Phyto-derived anti-viral compounds against the main protease and spike protein of the viral agent of COVID-19 (SARS-CoV-2) gain insight into the molecular interactions. In the current study, ten molecules taken from R. succedanea are analyzed through docking, derived from the PubChem database. Docking experiments with Autodock vina and PyRx tools were conducted. AdmetSAR and DruLito servers were eventually used for drug-like prediction. Our research shows that the phytoconstituents from R. succedanea, namely, Amentoflavone, Rhoifolin, and Agathisflavone acts against SARS CoV-2 main protease with the binding affinity of-9.3,-8.6 and-8.4 Kcal/mol;Hinokiflavone Robustaflavone and Amentoflavone acts against the SARS-CoV-2 receptor-binding domain of spike protein with a binding affinity of-10.5,-10.4 and-10.1 Kcal/mol respectively. These phyto-compounds can use contemporary strategies to develop effective medicines from natural origins. The substances identified potential anti-viral as likely. However, In-vitro studies are even more necessary to assess their effectiveness versus SARS CoV-2.

Macroalgae Bioactive Compounds for the Potential Antiviral of SARS-CoV-2: An In Silico Study

Coronavirus disease (COVID-19), which was due to novel coronavirus was detected in December 2019 in Wuhan, China for the first time and spread rapidly became a global pandemic. This study aimed to predict the potential of macroalgae compounds as SARS-CoV-2 antiviral by inhibiting of ACE2 receptor through in silico approach. Twenty-seven macroalgae compounds were obtained from PubChem (NCBI, USA), while target protein ACE2 receptor was collected from Protein Data Bank (PDB). Then the initial screening study drug-likeness conducted by Lipinski rule of five web server and prediction of bioactive probability carried out by PASS (Prediction of activity spectra for biologically active substances) Online web server. After those compounds were approved by Lipinski's rule of five and PASS online prediction web server, the blind docking simulation was performed using PyRx 0.8 software to show binding energy value. Molecular interaction analysis was done using BIOVIA Discovery Studio 2016 v16.1.0 and PyMOL v2.4.1 software. There are six macroalgae compounds approved by Lipinski's rule of five and PASS Online Analysis. The result is that macroalgae compound siphonaxanthin among 27 macroalgae compound showed strong binding energy to bind ACE2 receptor with -8.8 kcal/mol. This study also used the SARS-CoV-2 drugs as positive control: remdesivir, molnupiravir, baricitinib, lopinavir, oseltamivir, and favipiravir. The result shows that siphonaxanthin has lowest binding energy than the common SARS-CoV-2 drug. Macroalgae compounds are predicted to have potential as SARS-CoV-2 antiviral. Thus, extension studies need to investigate by in vitro and in vivo analysis for confirmation the siphonaxanthin's inhibitory activity in combat SARS-CoV-2.

Shape Complementarity Optimization of Antibody-Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein.

Many factors influence biomolecule binding, and its assessment constitutes an elusive challenge in computational structural biology. In this aspect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody-antigen complexes to quantify the complementarities occurring at molecular interfaces. We relied on a method we recently developed, which employs the 2D Zernike descriptors, to characterize the investigated regions with an ordered set of numbers summarizing the local shape properties. Collecting a structural dataset of antibody-antigen complexes, we applied this method and we statistically distinguished, in terms of shape complementarity, pairs of the interacting regions from the non-interacting ones. Thus, we set up a novel computational strategy based on in silico mutagenesis of antibody-binding site residues. We developed a Monte Carlo procedure to increase the shape complementarity between the antibody paratope and a given epitope on a target protein surface. We applied our protocol against several molecular targets in SARS-CoV-2 spike protein, known to be indispensable for viral cell invasion. We, therefore, optimized the shape of template antibodies for the interaction with such regions. As the last step of our procedure, we performed an independent molecular docking validation of the results of our Monte Carlo simulations.

The Potential Effect of Nigericin from Streptomyces hygroscopicus subsp. Hygroscopicus Against the Syndemic of Malaria and COVID-19 through Molecular Docking Perspective

Background: Malaria is a constantly challenging problem, notably in the Coronavirus Disease-19 (COVID-19) pandemic. The syndemic condition, malaria-COVID-19 co-infections, had been reported. Our previous study successfully revealed several compounds from Streptomyces hygroscopicus subsp. Hygroscopicus, including nigericin that has both antimalarial and antiviral effects. In malaria infection, Plasmodium falciparum Chloroquine Resistance Transporter (PfCRT) is the potential target for eliminating Plasmodium. Meanwhile, for SARS-CoV-2 infection, MPro is an essential protein for SARS-CoV-2 survival. This research aims to examine the potential effect of nigericin towards Plasmodium and SARS-CoV-2 by assessing its molecular interaction with PfCRT and MPro through molecular docking study. Methods: The protein target PfCRT and MPro were obtained from Protein Data Bank. Nigericin and the control ligand (chloroquine and N3) were obtained from PubChem. The pharmacokinetic analysis was done using SwissADME. Specific molecular docking was conducted using PyRx 0.9 and was visualized using LigPlot and PyMOL. Results: Nigericin has a large molecular weight, leading to the non-fulfillment of the Lipinski rule for oral administration. Through molecular docking study, the binding affinity of the Nigericin-PfCRT complex was -8.1 kcal/mol, and Nigericin-MPro was -8.6 kcal/mol. These binding affinities were stronger than the control ligand. The interaction between Nigericin-PfCRT and Nigericin-MPro share a similar pocket-site and amino acid residues as the control ligands. Conclusion: Nigericin has potential antimalarial and anti-coronavirus effects through molecular docking perspective by assessing the binding affinity and similarity of amino acid residues compared to control. Administration of systemic route can be an option in giving nigericin.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses.

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.