In Silico Study on Natural Chemical Compounds from Citric Essential Oils as Potential Inhibitors of an Omicron (BA.1) SARS-CoV-2 Mutants' Spike Glycoprotein.

BACKGROUND: SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest. OBJECTIVE: This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations. METHODS: An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted. RESULTS: Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors. CONCLUSION: The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

Selective Near-Infrared Blood Detection Driven by Ionic Liquid-Dye-Albumin Nanointeractions.

Due to its abundance in blood, a great deal of research has been undertaken to develop efficient biosensors for serum albumin and provide insight into the interactions that take place between these biosensing molecules and the protein. Near-infrared (NIR, >700 nm) organic dyes have been shown to be effective biosensors of serum albumin, but their effectiveness is diminished in whole blood. Herein, it is shown that an NIR sulfonate indolizine-donor-based squaraine dye, SO3SQ, can be strengthened as a biosensor of albumin through the addition of biocompatible ionic liquids (ILs). Specifically, the IL choline glycolate (1:1), at a concentration of 160 mM, results in the enhanced fluorescence emission ("switch-on") of the dye in the presence of blood. The origin of the fluorescence enhancement was investigated via methods, including DLS, ITC, and molecular dynamics. Further, fluorescence measurements were conducted to see the impact the dye-IL system had on the fluorescence of the tryptophan residue of human serum albumin (HSA), as well as to determine its apparent association constants in relation to albumin. Circular dichroism (CD) spectroscopy was used to provide evidence that the dye-IL system does not alter the secondary structures of albumin or DNA. Our results suggest that the enhanced fluorescence of the dye in the presence of IL and blood is due to diversification of binding sites in albumin, controlled by the interaction of the IL-dye-albumin complex.

Investigation of cannabidiol's potential targets in limbic seizures. In-silico approach.

Even though the vast armamentarium of FDA-approved antiepileptic drugs is currently available, over one-third of patients do not respond to medication, which arises a need for alternative medicine. In clinical and preclinical studies, various investigations have shown the advantage of specific plant-based cannabidiol (CBD) products in treating certain groups of people with limbic epilepsy who have failed to respond to conventional therapies. This work aims to investigate possible mechanisms by which CBD possesses its anticonvulsant properties. Molecular targets for CBD's treatment of limbic epilepsy, including hyperpolarization-activated cyclic nucleotide-gated channel 1 (HCN1), gamma-aminobutyric acid aminotransferase (GABA-AT), and gamma-aminobutyric acid type A receptor (GABAA), were used to evaluate its binding affinity. Interactions with the CB1 receptor were initially modeled as a benchmark, which further proved the efficiency of proposed here approach. Considering the successful benchmark, we further used the same concept for in silico investigation, targeting proteins of interest. As a result of molecular docking, molecular mechanics, and molecular dynamics simulations models of CBD-receptor complexes were proposed and evaluated. While CBD possessed decently high affinity and stability within the binding pockets of GABA-AT and some binding sites of GABAA, the most effective binding was observed in the CBD complex with HCN1 receptor. 100 ns molecular dynamics simulation revealed that CBD binds the open pore of HCN1 receptor, forming a similar pattern of interactions as potent Lamotrigine. Therefore, we can propose that HCN1 can serve as a most potent target for cannabinoid antiepileptic treatment. Communicated by Ramaswamy H. Sarma.

Homology Modeling and Molecular Dynamics-Driven Search for Natural Inhibitors That Universally Target Receptor-Binding Domain of Spike Glycoprotein in SARS-CoV-2 Variants.

The rapid spread of SARS-CoV-2 required immediate actions to control the transmission of the virus and minimize its impact on humanity. An extensive mutation rate of this viral genome contributes to the virus' ability to quickly adapt to environmental changes, impacts transmissibility and antigenicity, and may facilitate immune escape. Therefore, it is of great interest for researchers working in vaccine development and drug design to consider the impact of mutations on virus-drug interactions. Here, we propose a multitarget drug discovery pipeline for identifying potential drug candidates which can efficiently inhibit the Receptor Binding Domain (RBD) of spike glycoproteins from different variants of SARS-CoV-2. Eight homology models of RBDs for selected variants were created and validated using reference crystal structures. We then investigated interactions between host receptor ACE2 and RBDs from nine variants of SARS-CoV-2. It led us to conclude that efficient multi-variant targeting drugs should be capable of blocking residues Q(R)493 and N487 in RBDs. Using methods of molecular docking, molecular mechanics, and molecular dynamics, we identified three lead compounds (hesperidin, narirutin, and neohesperidin) suitable for multitarget SARS-CoV-2 inhibition. These compounds are flavanone glycosides found in citrus fruits - an active ingredient of Traditional Chinese Medicines. The developed pipeline can be further used to (1) model mutants for which crystal structures are not yet available and (2) scan a more extensive library of compounds against other mutated viral proteins.

Computational studies on binding, solvent, and pH effects on (S)-propranolol and methacrylic acid complex.

Density functional theory methods have been applied to understand binding of (s)-propranolol, a template, to a methacrylic acid molecule acting as a functional monomer using basic 1:1 model. The model has been expanded to study the effect of various pH by adding hydronium and hydroxide ions solvated by water molecules to the template-monomer system, to mimic acidic and basic environments, respectively. This could be considered a model study towards a potential use of molecular imprinting method for the design of a transdermal patch for a topical and direct delivery of (s)-propranolol to hemangiomas. In addition, this study provides detailed binding site analysis of the template and functional monomer verified by the theoretical IR spectra analysis, as well as solvent and pH effects on template-monomer binding energy.

N-arylnaphthylamines as inhibitors of human immunodeficiency virus integrase - lens epithelium-derived growth factor interactions: theoretical studies.

Presented work reports a comprehensive theoretical study on the inhibitory nature of N-arylnaphthylamines in Human Immunodeficiency Virus Integrase (HIV IN) - Lens Epithelium-Derived Growth Factor (LEDGF/p75) complexes. Factors influencing the inhibition efficiency in AlphaScreen% assay are evaluated and explained through the structure- and ligand-based studies; including molecular docking, molecular dynamics calculations, and quantitative structure-activity relationship (QSAR) approach. It has been shown that N-arylnaphthylamines possess a wide variety of binding poses. Three QSAR models have been developed using structural descriptors and descriptors derived from docking calculations. The activity of untested N-arylnaphthylamines have been predicted using the most successful model. Proposed here technique could become a useful tool for ligand selection, accelerating the development of a new generation of anti-HIV medications. [Formula: see text] Communicated by Ramaswamy H. Sarma.

Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (Mpro) through docking, molecular mechanic & dynamic, and ADMET profiling.

Due to an outbreak of COVID-19, the number of research papers devoted to in-silico drug discovery of potential antiviral drugs is increasing every day exponentially. Still, there is no specific drug to prevent or treat this novel coronavirus (SARS-CoV-2) disease. Thus, the screening for a potential remedy presents a global challenge for scientists. Up to date over a hundred crystallographic structures of SARS-CoV-2 Mpro have been deposited to Protein Data Bank. With many known proteins, the demand for a reliable target has become higher than ever, so as the choice of an efficient computational methods. Therefore, in this study comparative methods have been used for receptor-based virtual screening, targeting 9 selected structures of viral Mpro. Reliability analyses followed by re-docking of the specific co-crystallized ligand provided the best reproductivity for structures with PDB ID 6LU7, 6Y2G and 6Y2F. The influence of crystallographic water on an outcome of a virtual screening against selected targets was also investigated. Once the most reliable targets were selected, the library of easy purchasable natural compounds were retrieved from the MolPort database (10,305 compounds) and docked against the selected Mpro proteins. To ensure the efficiency of the selected compounds, binding energies for top-15 hit ligands were calculated using Molecular Mechanics as well as their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were predicted. Based on predicted binding energies and toxicities, top-5 compounds were selected and subjected to Molecular Dynamics simulation and found to be stable in complex to act as possible inhibitors for SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Antioxidant Activity of Selected Phenolic Acids-Ferric Reducing Antioxidant Power Assay and QSAR Analysis of the Structural Features.

Phenolic acids are naturally occurring compounds that are known for their antioxidant and antiradical activity. We present experimental and theoretical studies on the antioxidant potential of the set of 22 phenolic acids with different models of hydroxylation and methoxylation of aromatic rings. Ferric reducing antioxidant power assay was used to evaluate this property. 2,3-dihydroxybenzoic acid was found to be the strongest antioxidant, while mono hydroxylated and methoxylated structures had the lowest activities. A comprehensive structure-activity investigation with density functional theory methods elucidated the influence of compounds topology, resonance stabilization, and intramolecular hydrogen bonding on the exhibited activity. The key factor was found to be a presence of two or more hydroxyl groups being located in ortho or para position to each other. Finally, the quantitative structure-activity relationship approach was used to build a multiple linear regression model describing the dependence of antioxidant activity on structure of compounds, using features exclusively related to their topology. Coefficients of determination for training set and for the test set equaled 0.9918 and 0.9993 respectively, and Q2 value for leave-one-out was 0.9716. In addition, the presented model was used to predict activities of phenolic acids that haven't been tested here experimentally.

QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors.

Many chemical phenomena occur in solution. Different solvents can change the optical activity of chiral molecules. The optical rotation angles of solutes of 75 amino acids in dimethylformamide, water and methanol were analyzed using the quantitative structure-activity relationships approach. For an accurate description of chirality, we used specific quantum chemical descriptors, which reflect the properties of a chiral center, and continuous symmetry measures. The set of specific quantum chemical descriptors for atoms located near the chiral center, such as Mulliken charges, the sum of Mulliken charges on an atom (with the hydrogen charges summed up with the adjacent non-hydrogen atoms), and nuclear magnetic resoncance tensors was applied. To represent solvent effects, we used mixture-like structural simplex descriptors and quantum chemical descriptors obtained for structures optimized for specified solvent using PBE1PBE/6-31G** level of theory with the polarizable continuum model. Multiple linear regression, M5P, and locally weighted learning techniques were used to achieve accurate predictions. The specific quantum chemical descriptors proposed here demonstrated high specificity in the majority of the developed models and established direct quantitative structure-property relationships.