Journal of Molecular Graphics and Modelling

The Nipah virus (NiV) poses a pressing global threat to public health due to its high mortality rate, multiple modes of transmission, and lack of effective treatments. NiV glycoprotein G (NiV-G) emerges as a promising target for NiV drug discovery due to its essential role in viral entry and membrane fusion. Therefore, in this study we applied an integrated computational and biophysics approach to identify potential inhibitors of NiV-G within a curated dataset of Peruvian phytochemicals. Our virtual screening results indicated that these compounds could represent a natural source of potential NiV-G inhibitors with {Delta}G values ranging from -8 to -11 kcal/mol. Among them, Procyanidin B2, B3, B7, and C1 exhibited the highest binding affinities and formed the most molecular interactions with NiV-G. Molecular dynamics simulations revealed the induced-fit mechanism of NiV-G pocket interaction with these procyanidins, primarily driven by its hydrophobic nature. Non-equilibrium free energy calculations were employed to determine binding affinities, highlighting Procyanidin B3 and B2 as the ligands with the most substantial interactions. Overall, this work underscores the potential of Peruvian phytochemicals, particularly procyanidins B2, B3, B7, and C1, as lead compounds for developing anti-NiV drugs through an integrated computational biophysics approach. Key pointsO_LINipah Virus (NiV) Threat: NiV is a severe public health risk due to its high mortality rate, broad host range, multiple transmission modes, and lack of effective treatment. Outbreaks have occurred frequently in South and Southeast Asia, particularly in Bangladesh and India, leading to high fatality rates. C_LIO_LICross-Border Concerns: NiVs ability to transmit between humans and domestic animals raises concerns about its potential to cross regional borders and cause pandemics. It has been recognized as a high-priority pathogen by the World Health Organization. C_LIO_LILack of Treatment: Currently, there are no approved specific antiviral treatments or vaccines for NiV. Patients receive supportive care and some drugs used for other viruses, despite their side effects. C_LIO_LITargeting NiV Glycoprotein G: The study focuses on NiV glycoprotein G (NiV-G) as a target for potential anti-Nipah drugs due to its crucial role in viral entry. This glycoprotein mediates viral attachment and entry into host cells. C_LIO_LIComputational Drug Discovery: The research employs computational methods, including virtual screening and molecular dynamics simulations, to identify potential inhibitors of NiV-G from a dataset of Peruvian phytochemicals, particularly procyanidins B2, B3, B7, and C1. These compounds showed promising binding affinities, stable interactions, and favorable binding energies with NiV-G, making them potential lead compounds for drug development. C_LI

The new variant of SARS-CoV-2, Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including spike proteins receptor-binding domain (RBD). We have computationally investigated the interaction between RBD of both wild-type and omicron variants with hACE2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The mode of the interaction between Omicrons RBD to the human ACE2 (hACE2) receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has increased binding affinity for the hACE-2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of the hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g. S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. The pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to binding free energies suggesting this as one of the key interactions stabilizing the complex formation. The structural insights of RBD:hACE2 complex, their binding mode information and residue wise contributions to binding free energy provide insight on the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.

Infections caused by multidrug-resistant Streptococcus pneumoniae remain the leading cause of pneumonia-related deaths in children < 5 years globally, and mutations in penicillin-binding protein (PBP) 2x have been identified as the major cause of resistance in the organism to beta-lactams. Thus, the development of new modulators with enhanced binding of PBP2x is highly encouraged. In this study, phenolics, due to their reported antibacterial activities, were screened against the active site of PBP2x using structure-based pharmacophore and molecular docking techniques, and the ability of the top-hit phenolics to inhibit the active and allosteric sites of PBP2x was refined through 120 ns molecular dynamic simulation. Except for gallocatechin gallate and lysidicichin, respectively, at the active and allosteric sites of PBP2x, the top-hit phenolics had higher negative binding free energy ({Delta}Gbind) than amoxicillin [active site (-19.23 kcal/mol), allosteric site (-33.75 Kcal/mol)]. Although silicristin had the best broad-spectrum effects at the active (-38.41 kcal/mol) and allosteric (-50.54 kcal/mol) sites of PBP2x, the high thermodynamic entropy (4.90 [A]) of the resulting complex might suggest the need for its possible structural refinement for enhanced potency. Interestingly, silicristin had a predicted synthetic feasibility score of < 5 and quantum calculations using the DFT B3LYP/6-31G+ (dp) revealed that silicristin is less stable and more reactive than amoxicillin. These findings point to the possible benefits of the top-hit phenolics, and most especially silicristin, in the direct and synergistic treatment of infections caused by S. pneumoniae. Accordingly, silicristin is currently the subject of further confirmatory in vitro research.

Variants of SARS-CoV-2 have been identified rapidly after the beginning of pandemic. One of them, involving the spike protein and called D614G, represents a substantial percentage of currently isolated strains. While research on this variant was ongoing worldwide, on December 20th 2020 the European Centre for Disease Prevention and Control reported a Threat Assessment Brief describing the emergence of a new variant of SARS-CoV-2, named B.1.1.7, harboring multiple mutations mostly affecting the Spike protein. This viral variant has been recently associated with a rapid increase in COVID-19 cases in South East England, with alarming implications for future virus transmission rates. Specifically, of the nine amino acid replacements that characterize the Spike in the emerging variant, four are found in the region between the Fusion Peptide and the RBD domain (namely the already known D614G, together with A570D, P681H, T716I), and one, N501Y, is found in the Spike Receptor Binding Domain - Receptor Binding Motif (RBD-RBM). In this study, by using in silico biology, we provide evidence that these amino acid replacements have dramatic effects on the interactions between SARS-CoV-2 Spike and the host ACE2 receptor or TMPRSS2, the protease that induces the fusogenic activity of Spike. Mostly, we show that these effects are strongly dependent on ACE2 and TMPRSS2 polymorphism, suggesting that dynamics of pandemics are strongly influenced not only by virus variation but also by host genetic background.

Accurate prediction of biologically relevant binding poses is crucial for the success of computer-aided drug development. In this study, we describe a general strategy to enhance the precision of pose prediction in molecular docking by incorporating symmetry-related molecules (SRMs). Our objective was to demonstrate the significant impact of SRMs on the accuracy of pose prediction. To achieve this, we evaluated our method on high-quality protein-ligand complex structures, focusing on the presence and absence of SRMs during molecular docking studies. We have extracted the co-crystal ligands from the selected crystal structure and were redocked in presence and absence of SRM to assess their influence. Additionally, we calculated the free energy of the docked poses using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method, comparing the results in the presence and absence of SRMs. The findings revealed that redocking performed in the presence of SRMs significantly improved the prediction of biologically significant/crystallographically relevant poses. Consequently, our proposed strategy offers a robust method for enhancing pose prediction in current molecular docking programs, potentially leading to more effective and reliable drug development processes.

CagriSema is a fixed-dose combination of cagrilintide (an amylin analogue) and semaglutide (a GLP-1 receptor agonist), and is currently an experimental obesity drug developed by Novo Nordisk. In March, 2025, CagriSema underperformed expectations in a Phase III trial, achieving 15.7% weight loss instead of the anticipated 25%, raising concerns about its efficacy and clinical value. Given its chemical composition, the weight-loss efficacy of CagriSema is inextricably linked to the activations of GLP-1R and amylin receptors (AMYRs). With GLP-1R as an example target here, this study employs a structural biophysics-guided computational approach for the design of semaglutide analogues to enhance the activation of its receptor GLP-1R. To fully harness the therapeutic potential of GLP-1R activation, an experimental structural basis (PDB entry 4ZGM) of the GLP-1-GLP-1R interaction is essential for the design of semaglutide analogues, where site-specific missense mutations are engineered into its peptide backbone to establish additional stabilizing interactions with the extracellular domain (ECD) of GLP-1R. Specifically, this study puts forward an automated systemic natural amino acid scanning of the peptide backbone of semaglutide, where PDB entry 4ZGM was used as the structural template for high-throughput structural modeling by Modeller and ligand-receptor binding affinity (Kd) calculations by Prodigy. To sum up, this article reports a total of 564 computationally designed semaglutide analogues with improved GLP-1R ECD binding affinity. Moreover, this study proposes a concept of interfacial electrostatic scaffold comprising four salt bridges at the binding interface of GLP-1R ECD and semaglutide analogues. Drawing parallels with the continued optimization in the past century history of insulin, this article argues that the interfacial electrostatic scaffold here constitutes a robust framework for continued development of next-generation GLP-1R agonists, enabling more effective therapies for patients with diabetes and/or obesity.

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3 and one CXC receptor CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier, but as an efficient multi-class classifier that can discard not only inactive compounds but also low or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 were among the top predicted compounds and thus benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors available at: http://db-gpcr-chem.uw.edu.pl. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-spike protein-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds have previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

Tetracyclines are indispensable antibiotics employed to treat a broad spectrum of bacterial infections. However, the emergence of clinical pathogens exhibiting significant resistance to these drugs has posed a formidable challenge in managing bacterial diseases effectively. This resistance is attributed to the proliferation and diversity of resistance genes and various mechanisms that render tetracyclines inactive. A prevalent mechanism of resistance is enzymatic inactivation by tetracycline destructases, which compromises the efficacy of tetracyclines. Consequently, there is a pressing need to discover novel molecules capable of inhibiting tetracycline destructases activity and thereby enhancing the drugs potency. In this study, we identify novel inhibitors for tetracycline destructase (TDase) in Acinetobacter Baumannii, utilizing compounds derived from marine environments. Through a high-throughput virtual screening approach, we have investigated the potent natural marine compounds interaction and dynamic behaviour of tetracycline destructase through molecular simulations. These findings hold promise for the development of novel and efficacious therapeutics against bacterial infections.

BackgroundThe emergence of multi-resistant pathogens have increased dramatically in recent years, becoming a major public-health concern. Among other promising antimicrobial molecules with potential to assist in this worldwide struggle, cysteine-stabilized {beta} (CS-{beta}) defensins are attracting attention due their efficacy, stability, and broad spectrum against viruses, bacteria, fungi, and protists, including many known human pathogens. ResultsHere, 23 genomes of ciliated protists were screened and three CS-{beta} defensins with a likely antifungal activity were identified and characterized using bioinformatics from two freshwater and culturable species Laurentiella sp. (LsAMP-1 and LsAMP-2) and Euplotes focardii (EfAMP-1). Although any potential cellular ligand could be predicted for LsAMP-2 and EfAMP-1; evidences from structural, molecular dynamics, and docking analyses suggest that LsAMP-1 may form stably associations with phosphatidylinositol 4,5-bisphosphates (PIP2), a phospholipid found on many eukaryotic cells, which could, in turn, represent an anchorage mechanism within plasma membrane of targeted cells. ConclusionThese data stress that more biotechnology-oriented studies should be conducted on neglected protists, such ciliates, which could become valuable sources of novel bioactive molecules for therapeutic uses.

In silico analysis provides valuable insights into studying macromolecules, particularly proteins. Protein structure prediction models, like AlphaFold (AF), offer a cost-effective and time-efficient alternative to traditional methods like X-ray crystallography, NMR spectroscopy, and cryo-EM for determining protein structures. These models are increasingly used in protein-ligand interaction studies, a key aspect of drug discovery. Docking and molecular dynamics simulations facilitate this process, and researchers are continuously developing open-access tools for cavity detection and docking to accelerate protein-ligand interaction studies. However, while many of these tools perform well in specific cases, their strengths and weaknesses in analyzing predicted protein structures remain largely unknown. Therefore, it is crucial to compare docking analyses using experimentally determined protein structures and deep learning-based models. In this study, two well-characterized proteins, dopamine D3 receptor with its ligand ETQ and neprilysin with its ligand sacubitrilat, are used to evaluate docking predictions. The docking tools CB-Dock 2 and COACH-D are applied to both X-ray crystallography-derived structures and five different AF-generated models. The objective is to assess the accuracy of these docking approaches and determine whether this strategy can effectively simulate macromolecular behavior in their microenvironment. By doing so, this study aims to generate new insights and contribute to accelerating research in protein-ligand interactions.

The transmembrane protease serine subfamily (TMPRSS) has at least eight members with known protein sequence: TMPRSS2, TMPRRS3, TMPRSS4, TMPRSS5, TMPRSS6, TMPRSS7, TMPRSS9, TMPRSS11, TMPRSS12 and TMPRSS13. A majority of these TMPRSS proteins have key roles in human hemostasis as well as promoting certain pathologies, including several types of cancer. In addition, TMPRSS proteins have been shown to facilitate the entrance of respiratory viruses into human cells, most notably TMPRSS2 and TMPRSS4 activate the spike protein of the SARS-CoV-2 virus. Despite the wide range of functions that these proteins have in the human body, none of them have been successfully crystallized. The lack of structural data has significantly hindered any efforts to identify potential drug candidates with high selectivity to these proteins. In this study, we present homology models for all members of the TMPRSS family including any known isoform (the homology model of TMPRSS2 is not included in this study as it has been previously published). The atomic coordinates for all homology models have been refined and equilibrated through molecular dynamic simulations. The structural data revealed potential binding sites for all TMPRSS as well as key amino acids that can be targeted for drug selectivity.

HIV-1 protease (PR) is a critical enzyme for viral maturation and a major antiretroviral target. Here, a structure-based pharmacophore modeling, drug repurposing, docking, and molecular dynamics (MD) was applied to discover new PR inhibitors. A pharmacophore model was generated from the HIV-1 PR-3TL complex (PDB 3KFP) and used to screen the DrugBank library. The identified hit and reference 3TL were docked into PR, and each complex was simulated for 100 ns. Key metrics, including binding energy, RMSD, RMSF, SASA, hydrogen bonds, salt bridges, PCA were compared. Docking predicted BANs binding energy (-7.51 kcal/mol) slightly better than 3TL (-7.05 kcal/mol). MD showed BAN established a dense H-bond network, including Asp25, and a highly favorable total interaction energy (-69 kJ/mol). However, BAN binding significantly increased protease flexibility. BAN-bound PR had higher backbone RMSD (0.34 nm) and RMSF (0.215 nm) than 3TL-bound (0.22 nm, 0.121 nm), and disrupted salt bridges that remained stable with 3TL. PCA revealed BAN-bound PR sampled a larger conformational space. SASA changes were minor in all systems. BAN binds HIV-1 PR with affinity comparable to 3TL but via a distinct mechanism, stronger polar interactions accompanied by greater protein flexibility. These results, supported by recent literature, suggest BAN as a novel scaffold for PR inhibition. Experimental validation of BANs inhibitory activity is warranted.

The PD-1/PD-L1/PD-L2 immune checkpoint plays a critical role in regulating immune responses, and its dysfunction is implicated in immune evasion by cancer cells. Cold atmospheric plasma (CAP) has emerged as a promising cancer therapeutic modality with the potential to modulate immune checkpoints. This study employs molecular dynamics (MD) simulations to investigate the impact of CAP-induced oxidation on the interactions between PD-1 and its ligands, PD-L1 and PD-L2. We simulated the PD-1/PD-L1 and PD-1/PD-L2 complexes under different oxidation levels. Key residues within the interaction site of the ligands are modified using Vienna PTM 2.0 online server. Umbrella sampling and other MD analyses revealed that increasing oxidation levels leads to weaken the binding affinity between PD-1 and both PD-L1 and PD-L2. These findings suggest that CAP may offer a novel strategy for enhancing anti-tumor immunity. This computational study provides valuable insights into the molecular mechanisms underlying CAPs effects on immune regulation and highlights its potential for cancer immunotherapy.

The zymogen protease Plasminogen (Plg) and its active form plasmin (Plm) carry out important functions in the blood clot disintegration (breakdown of fibrin fibres) process. Inhibition of plasmin effectively reduces fibrinolysis to circumvent heavy bleeding. Currently, available Plm inhibitor tranexamic acid (TXA) that is used to treat severe hemorrhages is associated with an increased incidence of seizures which in turn were traced to gamma-aminobutyric acid antagonistic activity (GABAa) in addition to having multiple side effects. Fibrinolysis can be suppressed by targeting the three important protein domains: kringle-1 and serine protease domain of plasminogen and kringle-2 domain of tissue plasminogen activator. In the present study, combined approaches of structure-based virtual screening and molecular docking using Schrodinger Glide, AutoDock Vina, and ParDock/BAPPL+ were employed to identify potential hits from the ZINC database. Thereafter, the drug-likeness properties of the top three leads for each protein target were evaluated using Discovery Studio. Subsequently, a molecular dynamics simulation of 200ns for each protein-ligand complex was performed in GROMACS. The identified ligands are found to impart higher rigidity and stability to the protein-ligand complexes. Furthermore, the results were validated by performing the principal component analysis (PCA), and calculation of binding free energy using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach. The identified ligands occupy smaller phase space, form stable clusters and exhibit stronger non-bonded interactions. Thus, our findings can be useful for the development of promising anti-fibrinolytic agents. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/512028v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@b762fforg.highwire.dtl.DTLVardef@1c47591org.highwire.dtl.DTLVardef@1028dc1org.highwire.dtl.DTLVardef@a884ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

The coronavirus disease of 2019 (COVID-19) is a highly contagious respiratory illness that has become a global health crisis with new variants, an unprecedented number of infections, and deaths and demands urgent manufacturing of potent therapeutics. Despite the success of vaccination campaigns around the globe, there is no particular therapeutics approved to date for efficiently treating infected individuals. Repositioning or repurposing previously effective antivirals against RNA viruses to treat COVID-19 patients is a feasible option. Remdesivir is a broad-spectrum antiviral drug that the Food and Drug Administration (FDA) licenses for treating COVID-19 patients who are critically ill patients. Remdesivirs low efficacy, which has been shown in some clinical trials, possible adverse effects, and dose-related toxicities are issues with its use in clinical use. Our study aimed to design potent derivatives of remdesivir through the functional group modification of the parent drug targeting RNA-dependent RNA polymerase (RdRp) and main protease (MPro) of SARS-CoV-2. The efficacy and stability of the proposed derivatives were assessed by molecular docking and extended molecular dynamics simulation analyses. Furthermore, the pharmacokinetic activity was measured to ensure the safety and drug potential of the designed derivatives. The derivatives were non-carcinogenic, chemically reactive, highly interactive, and stable with the target proteins. D-CF3 is one of the designed derivatives that finally showed stronger interaction than the parent drug, according to the docking and dynamics simulation analyses, with both target proteins. However, in vitro and in vivo investigations are guaranteed to validate the findings in the future.

The current SARS covid-19 epidemic spread appears to be influenced by ethnical, geographical and sex-related factors that may involve genetic susceptibility to diseases. Similar to SARS-CoV, SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells, notably type II alveolar epithelial cells. Importantly, ACE2 gene is highly polymorphic. Here we have used in silico tools to analyze the possible impact of ACE2 single-nucleotide polymorphisms (SNPs) on the interaction with SARS-CoV-2 spike glycoprotein. We found that S19P (common in African people) and K26R (common in European people) were, among the most diffused SNPs worldwide, the only two SNPs that were able to potentially affect the interaction of ACE2 with SARS-CoV-2 spike. FireDock simulations demonstrated that while S19P may decrease, K26R might increase the ACE2 affinity for SARS-CoV-2 Spike. This finding suggests that the S19P may genetically protect, and K26R may predispose to more severe SARS-CoV-2 disease.

The machinery involved in cytotoxic T-cell activation requires three main characters such as: the major histocompatibility complex class I (MHC I) bound to the peptide (p), the T-cell receptor (TCR), and the CD3-complex which is a multidimer interfaced with the intracellular side. The pMHC:TCR interaction has been largely studied both in experimental and computational models, giving a contribution in understanding the complexity of the TCR triggering process. Nevertheless, a detailed study of the structural and dynamical characterization of the full complex (pMHC:TCR:CD3-complex) is still missing, due to insufficient data available on the CD3-chains arrangement around the TCR. The recent determination of the TCR:CD3-complex structure by means of Cryo-EM technique has given a chance to build the entire proteins system essential in the activation of T-cell, and thus in the adaptive immune response. Here, we present the first full model of the pMHC interacting with the TCR:CD3-complex, built in a lipid environment. To describe the conformational behaviour associated with the unbound and the bound states, all atoms Molecular Dynamics simulations were performed for the TCR:CD3-complex and for two pMHC:TCR:CD3-complex systems, bound to two different peptides. Our data point out that a conformational change affecting the TCR Constant {beta} (C{beta}) region occurs after the binding to the pMHC, revealing a key role of such a region in the propagation of the signal. Moreover, we found that the TCR reduces the flexibility of the MHC I binding groove, confirming our previous results.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to mutate and evolve with the emergence of omicron (B.1.1.529) as the new variant of concern. The rapid spread of this variant regionally and globally could be an allusion to increased infectivity, transmissibility, and antibody resistance. The omicron variant has a large set of mutations in its spike protein, specifically in the receptor binding domain (RBD), reflecting their significance in ACE2 interaction and antibody recognition. We have carried out the present study to understand how these mutations structurally impact the binding of the antibodies to their target epitope. We have computationally evaluated the binding of different classes of RBD targeted antibodies, namely, CB6 (etesevimab), REGN10933 (casirivimab), S309 (sotrovimab), and S2X259 to the omicron mutation-induced RBD. Molecular dynamics simulations and binding free energy calculations unveil the binding affinity and stability of the antibody-RBD complexes. All the four antibodies show reduced binding affinity towards the omicron RBD. The therapeutic antibody CB6 aka etesevimab was substantially affected due to numerous omicron mutations occurring in its target epitope. This study provides a structural insight into the reduced efficacy of RBD targeting antibodies against the SARS-CoV-2 omicron variant.

Timely control of the cytokine release syndrome (CRS) at the severe stage of COVID-19 is key to improving the treatment success and reducing the mortality rate. The inhibition of the activity of the two key cytokines, IFN{gamma} and IL-6, can significantly reduce or even reverse the development of the cytokine storm. The objective of our investigations is to reveal the anti-inflammatory potential of heparin for prevention and suppression of the development of CRS in acute COVID-19 patients. The effect of low-molecular-weight heparin (LMWH) on IFN{gamma} signalling inside the stimulated WISH cells was investigated by measuring its antiproliferative activity and the translocation of phosphorylated STAT1 in the nucleus. The mechanism of heparin binding to IFN{gamma} and IL-6 and therefore inhibition of their activity was studied by means of extensive molecular-dynamics simulations. We find that LMWH binds with high affinity to IFN{gamma} and is able to inhibit fully the interaction with its cellular receptor. It also influences the biological activity of IL-6 by binding to either IL-6 or IL-6/IL-6R thus preventing the formation of the IL-6/IL-6R/gp130 signaling complex. Our conclusion is that heparin is a potent anti-inflammatory agent that can be used in acute inflammatory conditions, due to its potential to inhibit both IFN {gamma} and IL-6 signalling pathways. Based on our results and available clinical observations, we suggest the administration of LMWH to COVID-19 patients in the initial stages of the acute phase. The beginning of the treatment and the dosage should be based on a careful follow-up of the platelet count and the D-dimer, IL-6, IFN, T-cells, and B-cells levels.