Characterization of the Docking Mechanism of Fruity Aroma Compounds on Olfactory Receptors Using Molecular Docking Simulation and Statistical Physics Modeling

This article suggests that the olfaction process can be simplified to an adsorption mechanism by utilizing the Machilis hrabei olfactory receptor MhOR5 as a biological adsorbent. The odorant molecules such as geosmin, linalool, and o-cresol were used as adsorbates. The aim of the present study is to provide new insights into the docking process of the tested odorants on MhOR5 using numerical simulation via an advanced statistical physics model to fit the corresponding response curves. In the present work, an advanced theory based on statistical physics formalism is applied to understand and analyze the experimental dose-olfactory response curves of three odorant molecules on the Machilis hrabei olfactory receptor. Indeed, a monolayer model with four energy levels developed using the grand canonical ensemble was successfully applied to analyze the adsorption mechanism of geosmin, linalool, and o-cresol on MhOR5 through the interpretation of the different fitted parameters. Stereographically, it was found that geosmin, linalool, and o-cresol molecules were docked on MhOR5 binding pockets with nonparallel orientations (multi-molecular process) since all the numbers of the studied odorants adsorbed on one binding pocket were superior to 1. Energetically, the values of the molar adsorption energies ΔEi (i = 1, 2, 3, and 4) related to the four types of binding pockets (varied between 6.18 and 18.43kJ/mol) demonstrated that the three odorants were exothermically and physically docked on MhOR5 since all values of ΔEi were positive and inferior to 40kJ/mol. The proposed model may also be applied to calculate and interpret two thermodynamic potentials: the internal energy Eint and adsorption entropy Sa. Additionally, the physicochemical parameters may be used to stereographically and energetically characterize the heterogeneity of the insect MhOR5 surface. The docking simulation results demonstrated that the estimated binding affinities or energy score values (varied between 6.27 and 18.40kJ/mol) were slightly similar to molar adsorption energy values and were included in the adsorption energy bands of the three adsorption energy distributions (AEDs).

In this COVID-19 pandemic situation, an appropriate drug is urgent to fight against this infectious disease to save lives and prevent mortality. Repurposed drugs and vaccines are the immediate solutions for this medical emergency until discover a new drug to treat this disease. As of now, no specific drug is available to cure this disease completely. Several drug targets were identified in SARS-CoV-2, in which RdRp protein is one of the potential targets to inhibit this virus infection. In-Silico studies plays a vital role to understand the binding nature of the drugs at the atomic level against the disease targets. The present study explores the binding mechanism of reported 53 nucleoside and non-nucleoside RdRp inhibitors and Ivermectin which are in clinical trials. These molecules were screened by molecular docking simulation; in which, the molecules are showing high binding affinity and forming interactions with the key amino acids of active site of RdRp protein are chosen for molecular dynamics simulation (MD) and binding free energy analysis. The results of molecular docking and MD simulation studies reveal that IDX184 is a stable molecule and forms strong interactions with the key amino acids and shows high binding affinity towards RdRp. Hence, IDX184 may also be considered as a potential inhibitor of RdRp after clinical study. Communicated by Ramaswamy H. Sarma

BackgroundFactor C (FC) is widely used as a standard material for endotoxin testing. It functions as a zymogenic serine protease and serve as a biosensor that detects lipopolysaccharides. Prior investigations involving molecular docking and molecular dynamics simulations of FC demonstrated an interaction between the C-type lectin domain (CLECT) and the ligand lipopolysaccharide (lipid A). In this study, our aim was to assess the stability of the interaction between fragment FC and the lipid A ligand using protein modeling approaches, molecular docking, molecular dynamics simulation, and gene construction into the pPIC9K expression vector. Methods and resultsThe FC structure was modelled by online tools. In this case, both molecular docking and MD simulations were applied to identify the interaction between protein and ligand (lipid A) including its complex stability. The FC structure model using three modeling websites has varied values, according to a Ramachandran plot study. When compared to other models, AlphaFold server modeling produced the best Ramachandran findings, with residues in the most advantageous area at 88.3%, followed by ERRAT values at 89.83% and 3D Verify at 71.93%. From the docking simulation of FC fragments with three ligands including diphosphoryl lipid A, FC-Core lipid A, and Kdo2 lipid A can be an activator of FC protein by binding to receptor regions to form ligand-receptor complexes. MD simulations were performed on all three complexes to assess their stability in water solvents showing that all complexes were stable during the simulation. The optimization of recombinant protein expression in Pichia pastoris was conducted by assessing the OD value and protease activity. Induction was carried out using 1% (v/v) methanol in BMMY media at 30°C for 72 h. ConclusionsProtein fragments of Factor C has been proven to detect endotoxins and serve as a potential biomarker. Molecular docking simulation and MD simulation were employed to study the complex formation of protein fragments FC with ligands. The expression of FC fragments was successfully achieved through heterologous expression. We propose optimizing the expression of FC fragments by inducing them with 1% methanol at 30°C and incubating them for 72 h. These optimized conditions are well-suited for upscaling the production of recombinant FC fragments using a bioreactor.

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

Elucidating the binding mechanism between bovine serum albumin and TiO2 nanoparticles with diverse properties: Insights from spectroscopic methods and molecular docking simulation

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.

Advanced investigation of a putative adsorption process of nine non key food odorants (non-KFOs) on the broadly tuned human olfactory receptor OR2W1: Statistical physics modeling and molecular docking study

This study presents a comprehensive analysis of six isoxazolidine and isoxazoline derivatives, employing a multifaceted approach that integrates Density Functional Theory (DFT), AdmetSAR analysis, and molecular docking simulations to explore their electronic, pharmacokinetic, and anticancer properties. Utilizing DFT analysis with the B3LYP-D3BJ functional and the 6-311++G(d,p) basis set, molecular geometries were optimized, and vibrational frequencies in the IR spectrum were evaluated, offering insights into the molecular structure and stability of the pharmaceutical compounds. Electrostatic potential maps were analyzed to predict functional group reactivity and protein-substrate interactions. Frontier Molecular Orbital (FMO) analysis and Density of States (DOS) plots revealed varying stability levels among the compounds, with 1b, 2b, and 3b exhibiting slightly higher stability. Chemical potential and hardness analyses highlighted stronger binding affinity for compounds 1b and 2b, suggesting stronger potential interactions. AdmetSAR analysis predicted favorable human intestinal absorption (HIA) rates for all compounds, with compound 3b showing superior oral effectiveness. Molecular docking and dynamics simulations were conducted targeting the receptor (PDB: 1JU6). Molecular docking simulations confirmed the high affinity of these compounds towards the target protein 1JU6, particularly compound 3b, which exhibited the most favorable binding energy of -8.50 kcal/mol. Molecular dynamics simulations demonstrated the superior stability of ligand 3b over 1b and 5-FU over 100 ns, suggesting its potential for further study. The 3b-protein complex exhibited stability through hydrophobic and hydrogen bond interactions, with 3b demonstrating reduced solvent exposure compared to 1b and 5-FU. This study underscores the promising role of compound 3b in anticancer treatments, providing a solid foundation for future drug development and optimization efforts.

The irrational use of antibiotics has favored the emergence of resistant bacteria, posing a serious threat to global health. To counteract antibiotic resistance, this research seeks to identify novel antimicrobials derived from essential oils that operate through several mechanisms. It aims to evaluate the quality and composition of essential oils from Origanum compactum and Origanum elongatum; test their antimicrobial activity against various strains; explore their synergies with commercial antibiotics; predict the efficacy, toxicity, and stability of compounds; and understand their molecular interactions through docking and dynamic simulations. The essential oils were extracted via hydrodistillation from the flowering tops of oregano in the Middle Atlas Mountains in Morocco. Gas chromatography combined with mass spectrometry (GC-MS) was used to examine their composition. Nine common antibiotics were chosen and tested alone or in combination with essential oils to discover synergistic effects against clinically important and resistant bacterial strains. A comprehensive in silico study was conducted, involving molecular docking and molecular dynamics simulations (MD). O. elongatum oil includes borneol (8.58%), p-cymene (42.56%), thymol (28.43%), and carvacrol (30.89%), whereas O. compactum oil is mostly composed of γ-terpinene (22.89%), p-cymene (15.84%), thymol (10.21%), and (E)-caryophyllene (3.63%). With O. compactum proving to be the most potent, these essential oils showed antibacterial action against both Gram-positive and Gram-negative bacteria. Certain antibiotics, including ciprofloxacin, ceftriaxone, amoxicillin, and ampicillin, have been shown to elicit synergistic effects. To fight resistant bacteria, the essential oils of O. compactum and O. elongatum, particularly those high in thymol and (E)-caryophyllene, seem promising when combined with antibiotics. These synergistic effects could result from their ability to target the same bacterial proteins or facilitate access to target sites, as suggested by molecular docking simulations. Molecular dynamics simulations validated the stability of the examined protein–ligand complexes, emphasizing the propensity of substances like thymol and (E)-caryophyllene for particular target proteins, opening the door to potentially effective new therapeutic approaches against pathogens resistant to multiple drugs.

The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in regulating excitatory glutamatergic neurotransmission and synaptic plasticity. However, excessive NMDAR activation can lead to increased calcium ion influx, resulting in excitotoxicity- a key contributor to neurodegenerative diseases. Although current NMDAR inhibitors exist, their clinical use is limited due to adverse effects. This study employed computational screening of Bacillus-derived macrolactins to identify potential NMDAR antagonists. Molecular docking simulations were performed using AMDock v1.5.2 with the AutoDock Vina engine to assess binding affinities to NMDAR (PDB:7SAD). Docked complexes were analyzed for chemical interactions, including polar contacts, using PyMol v2 and Discovery Studio Visualizer v4.5. Pharmacokinetic properties of macrolactins were predicted using Deep-PK. Molecular dynamics simulations via GROMACS assessed complex stability through RMSD, RMSF, radius of gyration (Rg), hydrogen bond count, and solvent-accessible surface area (SASA). Network pharmacology analysis of macrolactins in Alzheimer's disease (AD) involved mapping target interactions in STRING, importing into Cytoscape, and identifying hub genes using CytoHubba for KEGG pathway enrichment. Macrolactin F emerged as a promising candidate, exhibiting strong binding affinity (-6.8 kcal/mol) and an estimated Ki of 10.37 μM, outperforming commercial memantine and other macrolactins. Molecular dynamics simulations confirmed the stability and conformational integrity of the Macrolactin F-NMDAR complex. KEGG pathway enrichment analysis highlighted key hub pathways associated with AD, including hsa05010, hsa04725, hsa04722, hsa04071, hsa04068, hsa04150, and hsa04910. The findings suggest that Macrolactin F possesses superior antagonistic activity against NMDAR compared to memantine, supported by molecular docking and dynamic simulations. Network pharmacology analyses indicate that Macrolactin F can modulate critical signaling pathways implicated in AD, including PI3K/Akt/mTOR and MAPK cascades. Computational analyses identify Macrolactin F as a promising preclinical candidate for developing allosteric NMDAR inhibitors. This aligns with SDG 3 by contributing to potential therapeutics for neurodegenerative diseases such as Alzheimer's disease and supports SDG 10 by promoting accessible interventions to reduce global health disparities.

This study was designed to explore the underlying mechanisms of Asarum in treating oral ulcers (OU) by integrating network pharmacological analysis, the gene expression omnibus database, molecular docking, and dynamics simulation techniques. Network pharmacology was used to identify core targets of the active components of Asarum for OU treatment. Subsequently, single-cell genomic analysis was performed to investigate the distribution and expression of these core targets in oral mucosal tissue cells. Molecular docking was employed to assess the binding affinity between Asarum's active ingredients and the identified core targets. Transcriptomic data were used to validate the differential expression of these core targets in OU. Finally, molecular dynamics simulations were conducted on promising binding systems to evaluate the stability of their interactions. Eight active pharmacological ingredients of Asarum were identified, along with 135 corresponding gene targets. Intersection analysis of OU-related gene targets resulted in 92 drug-disease interaction genes. The CytoNCA plugin was used to select 14 core targets. Molecular docking simulations indicated moderate-to-strong binding affinities between these core targets and the active ingredients of Asarum. Differential expression analysis of OU data from the gene expression omnibus database revealed that CYP3A4 and AKT1 were differentially expressed in the disease group, providing an effective diagnostic model. Molecular dynamics simulations further demonstrated that the CYP3A4-kaempferol complex exhibited superior stability. Our study successfully predicted the potential targets of Asarum in the treatment of OU and provided a comprehensive exploration of their mechanisms of action. Among the active ingredients, kaempferol and the target gene CYP3A4 appear to hold the greatest promise for therapeutic applications in OU treatment. This study lays a strong foundation for future studies on the efficacy of Asarum in treating OU.

Theoretical study of a putative adsorption mechanism of arginine and glutamate on goldfish 5.24 and zebrafish Z06: Statistical physics modeling, thermodynamic study, and docking simulation

Cancer remains one of the leading causes of death worldwide, with the rising incidence of breast cancer being a significant public health concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as a promising therapeutic target for breast cancer treatment due to its crucial role in DNA repair. This study aimed to discover novel, targeted, and non-toxic PARP-1 inhibitors using an integrated approach that combines machine learning-based screening, molecular docking simulations, and quantum mechanical calculations. We trained a widely used machine learning models, Random Forest, using bioactivity data from known PARP-1 inhibitors. After evaluating the performance, it was used to screen an FDA-approved drug library, successfully identifying Atazanavir, Brexpiprazole, Raltegravir, and Nisoldipine as potential PARP-1 inhibitors. These compounds were further validated through molecular docking and all-atom molecular dynamics simulations, highlighting their potential for breast cancer therapy. The binding free energies indicated that Atazanavir at -41.86kJ/mol and Brexpiprazole at -45.44kJ/mol exhibited superior binding affinity compared to the control drug at -30.42kJ/mol, highlighting their promise as candidates for breast cancer therapy. Subsequent optimized geometries and electron density mappings of the two molecular structures revealed a Gibbs free energy of -2334.610 Ha for the first molecule and -1682.278316 Ha for the second, confirming enhanced stability compared to the standard drug. This study not only highlights the efficacy of machine learning in drug discovery but also underscores the importance of quantum mechanics in validating molecular stability, setting a robust foundation for future pharmacological explorations. Additionally, this approach could revolutionize the drug repurposing process by significantly reducing the time and cost associated with traditional drug development methods. Our results establish a promising basis for subsequent research aimed at optimizing these PARP-1 inhibitors for clinical use, potentially offering more effective treatment options for breast cancer patients.

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors. Communicated by Ramaswamy H. Sarma

Heart failure (HF) is a life-threatening condition that occurs when the heart cannot pump enough blood and oxygen to meet the body's needs. It affects mostly the elderly, commonly from the male population, especially those with obesity, diabetes, or some other chronic condition. It can be treated with different medications, and promising results were shown by a relatively new medicament called Entresto. Results obtained from molecular docking and molecular dynamics simulations to examine the inhibitory capacity of Entresto are presented in this study. Parameters obtained by the molecular docking simulations show that both parts of Entresto (sacubitril (SAC) and valsartan (VAL)) interact with targeted proteins, and inhibit their physiological function. Simulations of molecular dynamics revealed some interesting inhibitory patterns. SAC was discovered to produce structural alterations in neprilysin by binding to it, reducing neprilysin's physiological activity. In addition to blocking the active site, SAC binding causes the enzyme's structure to become less compact over time, causing changes in its biochemical characteristics and preventing the enzyme from performing its biological function. Similar to SAC, VAL also causes deviations in the structure of angiotensin receptors. The angiotensin receptor GPCR (G-protein-coupled receptors) is immersed in the lipid bilayer, and changes in the tertiary structure are only visible through RMSD and RMSF, not by examining Rg. In this regard, MD simulations validated the results of molecular docking simulations, demonstrating that both SAC and VAL had inhibitory potential towards the neprilysin and angiotensin receptors, respectively.