Empirical Hydrogen-Bond Potential Functions-An Old Hat Reconditioned

A computational investigation of potential plant-based bioactive compounds against drug-resistant Staphylococcus aureus of multiple target proteins

Drug-resistant Staphylococcus aureus (DRSA) poses a significant global health threat, like bacteremia, endocarditis, skin, soft tissue, bone, and joint infections. Nowadays, the resistance against conventional drugs has been a prompt and focused medical concern. The present study aimed to explore the inhibitory potential of plant-based bioactive compounds (PBBCs) against effective target proteins using a computational approach. We retrieved and verified 22 target proteins associated with DRSA and conducted a screening process that involved testing 87 PBBCs. Molecular docking was performed between screened PBBCs and reference drugs with selected target proteins via AutoDock. Subsequently, we filtered the target proteins and top PBBCs based on their binding affinity scores. Furthermore, molecular dynamic simulation was carried out through GROMACS for a duration of 100 ns, and the binding free energy was calculated using the gmx_MMPBSA. The result showed consistent hydrogen bonding interactions among the amino acid residues Ser 149, Arg 151, Thr 165, Thr 216, Glu 239, Ser 240, Ile 14, as well as Asn 18, Gln 19, Lys 45, Thr 46, Tyr 109, with their respective target proteins of the penicillin-binding protein and dihydrofolate reductase complex. Additionally, we assessed the pharmacokinetic properties of screened PBBCs via SwissADME and AdmetSAR. The findings suggest that β-amyrin, oleanolic acid, kaempferol, quercetin, and friedelin have the potential to inhibit the selected target proteins. In future research, both in vitro and in vivo, experiments will be needed to establish these PBBCs as potent antimicrobial drugs for DRSA.Communicated by Ramaswamy H. Sarma.

In silico discovery of food-derived phytochemicals against asialoglycoprotein receptor 1 for treatment of hypercholesterolemia: Pharmacophore modeling, molecular docking and molecular dynamics simulation approach

Hypercholesterolemia is a significant risk factor for atherosclerotic cardiovascular disease (ASCVD). Successful management of cholesterol metabolism disorders can prevent these ASCVD effectively. Asialoglycoprotein receptor 1 (ASGR1) is the main subtype of sialoglycoprotein receptor, which is specifically expressed in the liver and mediates the endocytosis of blood asialoglycoprotein to lysosome degradation. Recently, ASGR1 has been reported as a new therapeutic target for the treatment of hypercholesterolemia. In this study, the main aim was to identify natural ASGR1 inhibitors from plant food chemicals library through pharmacophore and docking based virtual screening. Total 14 phytochemicals of potential ASGR1 inhibitors were identified, which presented docking affinity higher than control compound through docking based virtual screening. The docking pose showed the top three hits interacted residues were located at active pocket of ASGR1 with hydrogen bonds, hydrophobic interactions and electrostatic interactions. The top three hits (ZINC85664954, ZINC169372863, and ZINC195764535) were then subjected to 200 ns molecular dynamics simulation to evaluate the stability of docked complexes. These results showed that selected phytochemicals bound to ASGR1 with higher stability than control compound. Binding free energy of each docked complex was calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method. The binding free energy of ZINC85664954, ZINC169372863, ZINC195764535, and control-ASGR1 docked complexes were -18.359, -13.303, -14.389, and -6.229 kcal/mol, respectively. This indicated that selected hits bound to ASGR1 with higher affinity than control compound. Network pharmacology analysis shows that these phytochemicals have obvious multiple-effects and can regulate various biochemical pathways related to hypercholesterolemia. Besides, selected phytochemicals have suitable pharmacokinetics properties, suggesting that these compounds may be potential drug candidates. This study may be contributed to rational design of novel ASGR1 inhibitors for treatment of hypercholesterolemia.

In silico discovery of potential sodium-glucose cotransporter-2 inhibitors from natural products for treatment of heart failure via molecular docking and molecular dynamics simulation approach

Heart failure (HF) is the end stage of cardiovascular disease. Because of its complex condition and poor prognosis, HF has become an important public health problem in the world. Sodium-glucose cotransporter-2 (SGLT2) is a member of the glucose transporter family. Recently, SGLT2 inhibitors have been applied to treat HF. In this study, the main aim was to discover natural SGLT2 inhibitor from Chinese herbs through docking-based virtual screening. Totally 113 natural compounds of potential SGLT2 inhibitor were identified, which displayed docking affinity higher than six approved inhibitors (dapagliflozin (IC50 = 4.9 nM), canagliflozin (IC50 = 4.4 nM 6.7), ipragliflozin (IC50 = 7.4 nM), empagliflozin (IC50 = 3.1 nM), tofogliflozin (IC50 = 4 nM) and luseogliflozin (IC50 = 2.3 nM)) through docking-based virtual screening. Then, the top three hits (ZINC70455591, ZINC85594065 and ZINC14588133) and six known inhibitors were selected for molecular dynamics simulation and the binding free energy calculation using molecular mechanics Poisson-Boltzmann surface area to demonstrate the stability and affinity of docked complexes. These results pointed out that the three docked complexes were stabilized and the chosen compounds were tightly adhering to the binding site of SGLT2. Besides, pharmacokinetic properties of the selected compounds showed those natural compounds may be potential drug candidates. This study may be contributed to further in vitro and in vivo validation and the development of novel SGLT2 inhibitor for treating HF.Communicated by Ramaswamy H. Sarma.

Semi-empirical and Linear-Scaling DFT Methods to Characterize duplex DNA and G-quadruplexes in Presence of Interacting Small Molecules

The computational study of DNA and its interaction with ligands is a highly relevant area of research, with significant consequences for developing new therapeutic strategies. However, the computational description of...

Explicit consideration of spatial hydrogen bonding direction for activity coefficient prediction based on implicit solvation calculations

Directional hydrogen bonding is introduced to implicit solvation calculations for improved prediction of solvation properties and phase equilibria of associating fluids.

Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods

We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.

Assessing Molecular Dynamics Simulations with Solvatochromism Modeling

For the modeling of solvatochromism with an explicit representation of the solvent molecules, the quality of preceding molecular dynamics simulations is crucial. Therefore, the possibility to apply force fields which are derived with as little empiricism as possible seems desirable. Such an approach is tested here by exploiting the sensitive solvatochromism of p-nitroaniline, and the use of reliable excitation energies based on approximate second-order coupled cluster results within a polarizable embedding scheme. The quality of the various MD settings for four different solvents, water, methanol, ethanol, and dichloromethane, is assessed. In general, good agreement with the experiment is observed when polarizable force fields and special treatment of hydrogen bonding are applied.

Benchmark of electronic structure methods for protein–ligand interactions based on high-level reference data

The accurate prediction of the strength of protein–ligand interactions is a very difficult problem despite impressive advances in the field of biomolecular modeling. There are good reasons to believe that quantum mechanical methods can help with this task, but the application of such methods in the context of scoring is still in its infancy. Here we benchmark several wave function theory (WFT), density functional theory (DFT) and semiempirical quantum mechanical (SQM) approaches against high-level theoretical references for realistic test cases. Based on our findings for systematically generated model systems of real protein/ligand complexes from the PDB-bind database, we can recommend SCS-MP2 and B2-PLYP-D3 as reference methods, TPSS-D3+Dabc/def-TZVPP as the best DFT approach and PM6-DH+ as a fast and accurate alternative to full ab initio treatments.

Enhanced semiempirical QM methods for biomolecular interactions

Recent successes and failures of the application of 'enhanced' semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.

Large-scale virtual high-throughput screening for the identification of new battery electrolyte solvents: computing infrastructure and collective properties

A volunteer computing approach is presented for the purpose of screening a large number of molecular structures with respect to their suitability as new battery electrolyte solvents.

A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+

We present new dispersion and hydrogen bond corrections to the PM6 method, PM6-D3H+, and its implementation in the GAMESS program. The method combines the DFT-D3 dispersion correction by Grimme et al. with a modified version of the H+ hydrogen bond correction by Korth. Overall, the interaction energy of PM6-D3H+ is very similar to PM6-DH2 and PM6-DH+, with RMSD and MAD values within 0.02 kcal/mol of one another. The main difference is that the geometry optimizations of 88 complexes result in 82, 6, 0, and 0 geometries with 0, 1, 2, and 3 or more imaginary frequencies using PM6-D3H+ implemented in GAMESS, while the corresponding numbers for PM6-DH+ implemented in MOPAC are 54, 17, 15, and 2. The PM6-D3H+ method as implemented in GAMESS offers an attractive alternative to PM6-DH+ in MOPAC in cases where the LBFGS optimizer must be used and a vibrational analysis is needed, e.g., when computing vibrational free energies. While the GAMESS implementation is up to 10 times slower for geometry optimizations of proteins in bulk solvent, compared to MOPAC, it is sufficiently fast to make geometry optimizations of small proteins practically feasible.

Accurate Modeling of Organic Molecular Crystals by Dispersion-Corrected Density Functional Tight Binding (DFTB)

The ambitious goal of organic crystal structure prediction challenges theoretical methods regarding their accuracy and efficiency. Dispersion-corrected density functional theory (DFT-D) in principle is applicable, but the computational demands, for example, to compute a huge number of polymorphs, are too high. Here, we demonstrate that this task can be carried out by a dispersion-corrected density functional tight binding (DFTB) method. The semiempirical Hamiltonian with the D3 correction can accurately and efficiently model both solid- and gas-phase inter- and intramolecular interactions at a speed up of 2 orders of magnitude compared to DFT-D. The mean absolute deviations for interaction (lattice) energies for various databases are typically 2-3 kcal/mol (10-20%), that is, only about two times larger than those for DFT-D. For zero-point phonon energies, small deviations of <0.5 kcal/mol compared to DFT-D are obtained.

Comparison of molecular mechanics, semi-empirical quantum mechanical, and density functional theory methods for scoring protein-ligand interactions

Correctly ranking protein-ligand interactions with respect to overall free energy of binding is a grand challenge for virtual drug design. Here we compare the performance of various quantum chemical approaches for tackling this so-called "scoring" problem. Relying on systematically generated benchmark sets of large protein/ligand model complexes based on the PDBbind database, we show that the performance depends first of all on the general level of theory. Comparing classical molecular mechanics (MM), semiempirical quantum mechanical (SQM), and density functional theory (DFT) based methods, we find that enhanced SQM approaches perform very similar to DFT methods and substantially different from MM potentials.

Specific Reaction Path Hamiltonian for Proton Transfer in Water: Reparameterized Semiempirical Models

The semiempirical MNDO-based AM1 and PM3 methods and the orthogonalization-corrected OM1, OM2, and OM3 models were reparameterized to improve their description of bulk water and of proton transfer in water. Reference data included the gas-phase geometries and energies of the water molecule, small water clusters, the hydronium ion, and small hydronium ion-water clusters, as well as the gas-phase potential energy surface for proton transfer between the two water molecules in a Zundel ion, all calculated at the MP2/aug-cc-pVTZ level of theory. Combined QM/MM molecular dynamics simulations were carried out for bulk water and for a proton solvated in water using large cluster models. Both the authentic and reparameterized semiempirical models were employed in the simulations. The reparameterization led to significantly better results in all cases. The new set of OM3 parameters gave the best overall results for the structural and dynamic properties of water and the hydrated proton, with a small but finite barrier of 0.1-0.2 kcal/mol in the potential of mean force for proton transfer, in agreement with ab initio path-integral molecular dynamics simulations. The reparameterized OM3 model is expected to be useful for efficient modeling of proton transfer in aqueous solution.

Corrected small basis set Hartree-Fock method for large systems

A quantum chemical method based on a Hartree-Fock calculation with a small Gaussian AO basis set is presented. Its main area of application is the computation of structures, vibrational frequencies, and noncovalent interaction energies in huge molecular systems. The method is suggested as a partial replacement of semiempirical approaches or density functional theory (DFT) in particular when self-interaction errors are acute. In order to get accurate results three physically plausible atom pair-wise correction terms are applied for London dispersion interactions (D3 scheme), basis set superposition error (gCP scheme), and short-ranged basis set incompleteness effects. In total nine global empirical parameters are used. This so-called Hartee-Fock-3c (HF-3c) method is tested for geometries of small organic molecules, interaction energies and geometries of noncovalently bound complexes, for supramolecular systems, and protein structures. In the majority of realistic test cases good results approaching large basis set DFT quality are obtained at a tiny fraction of computational cost.