An explorative study on Staphylococcus aureus MurE inhibitor: induced fit docking, binding free energy calculation, and molecular dynamics

Structure-based virtual screening of natural compounds against wild and mutant (R1155K, A1156T and D1168A) NS3-4A protease of Hepatitis C virus

NS3-4A, a serine protease, is a primary target for drug development against Hepatitis C Virus (HCV). However, the effectiveness of potent next-generation protease inhibitors is limited by the emergence of mutations and resulting drug resistance. To address this, in this study a structure-based drug design approach is employed to screen a large library of 7320 natural compounds against both wild-type and mutant variants of NS3-4A protease. Telaprevir, a widely used protease inhibitor, was recruited as the control drug. The top 10 compounds with favorable binding affinities underwent drug-likeness evaluation. Based on ADMET studies, complexes of NP_024762 and NP_006776 were selected for molecular dynamic simulations. Principal component analysis (PCA) was employed to explore the conformational space and protein dynamics of the protein-ligand complex using a Free Energy Landscape (FEL) approach. The cosine values obtained from FEL analysis ranged from 0 to 1, and eigenvectors with cosine values below 0.2 were chosen for further analysis. To forecast binding free energies and evaluate energy contributions per residue, the MM-PBSA method was employed. The results highlighted the crucial role of amino acids in the catalytic domain for the binding of the protease with phytochemicals. Stable associations between the top compounds and the target protease were confirmed by the formation of hydrogen bonds in the binding pocket involving residues: His1057, Gly1137, Ser1139, and Ala1157. These findings suggest the potential of these compounds for further validation through biological evaluation.Communicated by Ramaswamy H. Sarma.

In vitro and in silico evaluation of the design of nano-phyto-drug candidate for oral use against Staphylococcus aureus

Onopordum acanthium is a medicinal plant with many important properties, such as antibacterial, anticancer, and anti-hypotensive properties. Although various studies reported the biological activities of O. acanthium, there is no study on its nano-phyto-drug formulation. The aim of this study is to develop a candidate nano-drug based on phytotherapeutic constituents and evaluate its efficiency in vitro and in silico. In this context, poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) of O. acanthium extract (OAE) were synthesized and characterized. It was determined that the average particle size of OAE-PLGA-NPs was 214.9 ± 6.77 nm, and the zeta potential was -8.03 ± 0.85 mV, and PdI value was 0.064 ± 0.013. The encapsulation efficiency of OAE-PLGA-NPs was calculated as 91%, and the loading capacity as 75.83%. The in vitro drug release study showed that OAE was released from the PLGA NPs with 99.39% over the 6 days. Furthermore, the mutagenic and cytotoxic activity of free OAE and OAE-PLGA-NPs were evaluated by the Ames test and MTT test, respectively. Although 0.75 and 0.37 mg/mL free OAE concentrations caused both frameshift mutation and base pair substitution (p < 0.05), the administered OAE-PLGA NP concentrations were not mutagenic. It was determined with the MTT analysis that the doses of 0.75 and 1.5 mg/mL of free OAE had a cytotoxic effect on the L929 fibroblast cell line (p < 0.05), and OAE-PLGA-NPs had no cytotoxic effect. Moreover, the interaction between the OAE and S. aureus was also investigated using the molecular docking analysis method. The molecular docking and molecular dynamics (MD) results were implemented to elucidate the S. aureus MurE inhibition potential of OAE. It was shown that quercetin in the OAE content interacted significantly with the substantial residues in the catalytic pocket of the S. aureus MurE enzyme, and quercetin performed four hydrogen bond interactions corresponding to a low binding energy of -6.77 kcal/mol with catalytic pocket binding residues, which are crucial for the inhibition mechanism of S. aureus MurE. Finally, the bacterial inhibition values of free OAE and OAE-PLGA NPs were determined against S. aureus using a microdilution method. The antibacterial results showed that the inhibition value of the OAE-PLGA NPs was 69%. In conclusion, from the in vitro and in silico results of the nano-sized OAE-PLGA NP formulation produced in this study, it was evaluated that the formulation may be recommended as a safe and effective nano-phyto-drug candidate against S. aureus.

Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of non-covalent interaction energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

Molecular representations are fundamental tools for machine-learning models. The current work introduces a new set of molecular representations demonstrated to enable accurate predictions of molecular conformational energy and solvation free energy.

Identification of potential leukocyte antigen-related protein (PTP-LAR) inhibitors through 3D QSAR pharmacophore-based virtual screening and molecular dynamics simulation

Owing to its negative regulatory role in insulin signaling, protein tyrosine phosphatase of leukocyte antigen-related protein (PTP-LAR) was widely thought as a potential drug target for diabetes. Now, it was urgent to search for potential LAR inhibitors targeting diabetes. Initially, the pharmacophore models of LAR inhibitors were established with the application of the HypoGen module. The cost analysis, test set validation, as well as Fischer's test was used to verify the efficiency of pharmacophore model. Then, the best pharmacophore model (Hypo-1-LAR) was applied for the virtual screening of the ZINC database. And 30 compounds met the Lipinski's rule of five. Among them, 10 compounds with better binding affinity than the known LAR inhibitor (BDBM50296375) were discovered by docking studies. Finally, molecular dynamics simulations and post-analysis experiments (RMSD, RMSF, PCA, DCCM and RIN) were conducted to explore the effect of ligands (ZINC97018474 and Compound 1) on LAR and preliminary understand why ZINC97018474 had better inhibitory activity than Compound 1 (BDBM50296375). Communicated by Ramaswamy H. Sarma.