METHOD: The calculations are performed, at concentrations x=0.0, 0.25, 0.5, 0.75, and 1.0 by using the "full potential (FP) linearized (L) augmented plane wave plus local orbital (APW+lo) method framed within density functional theory (DFT)" as recognized in the "WIEN2k computational code". The "quasi-harmonic Debye model" approach is employed to determine the thermal properties of the title alloys.
METHODS: As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site.
RESULTS: The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔGbinding of - 75.7304 ± 0.98324 and - 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study.
METHODS: The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were then explored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which include OPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition, in-house scripts written in Matlab were used to control fragments connection and automation of the tools.
METHODS: The ATGL-predicted protein structure, verified by PROCHECK, was determined using AlphaFold. Molecular docking, molecular dynamics simulation, and prime molecular mechanic-generalized born surface area were performed using LigPrep, Desmond, and prime MM-GBSA modules of Schrödinger software release 2021-2, respectively. For pharmacological validation, immunoblotting was performed to assess ATGL protein expression. The fluorescence intensity and glycerol concentration were quantified to evaluate the efficiency of phillyrin in inhibiting ATGL.
METHODS: The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions.
METHOD: This research commenced with retrieving protein structures from the RCSB database, generating the formation of ligands using the ChemDraw Professional software, conducting molecular docking with the Autodock Vina software, and performing molecular dynamics simulations using the Amber software, along with the evaluation of RMSD values and intermolecular hydrogen bonds. Free energy, electrostatic interactions, and Van der Waals interaction were calculated using MM/GBSA. Acarbose, used as a positive control, and maltose are simulated together with test compound that has the best free energy. The forcefields used for molecular dynamics simulations are ff19SB, gaff2, and tip3p.
METHODS: Docking between ligand and FOXO1 receptor was carried out with Autodock4.2. For molecular dynamics simulations, the force fields of DNA.OL15, protein.ff14SB, gaff2, and tip3p were used.
METHODS: Initially, IR and NMR spectroscopic methods were used. Standard procedures were followed. For the computations, a hybrid DFT method with empirical dispersion, ωB97X-D, was used. The basis set, 6-311++G**, is of triple-ζ quality, in which polarization functions and diffuse functions were added for all atoms.
METHODS: The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30 ns production simulations using GROMACS 2018 at 300 K and 1 atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.
METHODS: The computational techniques involved dispersion-corrected density functional theory (DFT) with the B3LYP functional and the 6-311G** basis set. Grimme's D3 corrections were included to account for dispersion interactions. The calculations were performed via GAMESS-US software. Quantum descriptors of reactivity, such as ionization potential, electron affinity, chemical potential, and electrophilicity index, were derived from the HOMO and LUMO energies. Molecular docking studies were conducted via the CB-Dock server via AutoDock Vina software to predict binding affinities to cancer-related proteins. Petra/Osiris/Molinspiration (POM) analysis was used to predict the drug likeness and other pharmaceutical properties of the synthesized ILs.