Combined pharmacophore, virtual screening and molecular dynamics studies to identify Bruton's tyrosine kinase inhibitors

Aromatase inhibitors identified from Saraca asoca to treat infertility in women with polycystic ovary syndrome via in silico and in vivo studies

Polycystic ovary syndrome (PCOS) is a widely occurring metabolic disorder causing infertility in 70%-80% of the affected women. Saraca asoca, an ancient medicinal herb, has been shown to have therapeutic effects against infertility and hormonal imbalance in women. This study was aimed to identify new aromatase inhibitors from S. asoca as an alternative to the commercially available ones via in silico and in vivo approaches. For this, 10 previously reported flavonoids from S. asoca were chosen and the pharmacodynamic and pharmacokinetic properties were predicted using tools like Autodock Vina, GROMACS, Gaussian and ADMETLab. Of the 10, procyanidin B2 and luteolin showed better interaction with higher binding energy when docked against aromatase (3S79) as compared to the commercial inhibitor letrozole. These two compounds showed higher stability in molecular dynamic simulations performed for 100 ns. Molecular mechanics Poisson-Boltzmann surface analysis indicated that these compounds have binding free energy similar to the commercial inhibitor, highlighting their great affinity for aromatase. Density functional theory analysis revealed that both compounds have a good energy gap, and ADMET prediction exhibited the drug-likeness of the two compounds. A dose-dependent administration of these two compounds on zebrafish revealed that both the compounds, at a lower concentration of 50 µg/ml, significantly reduced the aromatase concentration in the ovarian tissues as compared to the untreated control. Collectively, the in silico and in vivo findings recommend that procyanidin B2 and luteolin could be used as potential aromatase inhibitors for overcoming infertility in PCOS patients with estrogen dominance.

Identification of novel pyrazole containing ɑ-glucosidase inhibitors: insight into pharmacophore, 3D-QSAR, virtual screening, and molecular dynamics study

Pharmacophore modelling, 3 D QSAR modelling, virtual screening, and molecular dynamics study, all-in-one combination were employed successfully design and develop an alpha-glucosidase inhibitor. To explain the structural prerequisites of biologically active components, 3 D-QSAR models were generated using the selected best hypothesis (AARRR) for compounds 55 included in the model C. The selection of 3 D-QSAR models showed that the Gaussian steric characteristic is crucial to alpha glucosidase's inhibitory potential. The alpha-glucosidase inhibitory potency of the compound is enhanced by other components, including Gaussian hydrophobic groups, Gaussian hydrogen bond acceptor or donor groups, Gaussian electrostatic characteristics, and a Gaussian steric feature. An identification of structure-activity relationships can be obtained from the developed 3 D-QSAR, C model, with R2 = 0.77 and SD = 0.02 for training set, and Q2 = 0.66, RMSE 0.02, and Pearson R = 0.81 for testing set, corresponding to elevated predictive ability. Additionally, docking and MM/GBSA experiments on 1146023 showed that it interacts with critical amino acids in the binding site when coupled with acarbose. Further, five compounds that display a high affinity for alpha-glucosidase were found, and these compounds may serve as potent leads for alpha-glucosidase inhibitor development. Biological activity will be tested for these compounds in the future.Communicated by Ramaswamy H. Sarma.

Virtual screening and structure-activity relationship study of novel BTK inhibitors in Traditional Chinese Medicine for the treatment of rheumatoid arthritis

Bruton tyrosine kinase (BTK) is a known drug target for the treatment of autoimmune diseases, including rheumatoid arthritis (RA). In this study, a series of 1-amino-1H-imidazole-5-carboxamide derivatives with good inhibitory activity against BTK were selected to explore the structure-activity relationships of these BTK inhibitors (BTKIs). Furthermore, we concentrated on 182 prescriptions of Traditional Chinese Medicine with therapeutic effects on RA. 54 herbs with a frequency of ≥10 were counted to establish a database containing 4027 ingredients for virtual screening. Five compounds with relatively higher docking scores and better absorption, distribution, metabolism, elimination and toxicity (ADMET) parameters were then selected for higher precision docking. The results demonstrated that the potentially active molecules form hydrogen bond interactions with the hinge region residues Met477, Glu475, glycine-rich P-loop residue Val416, Lys430 and DFG motif Asp539. In particular, they also interact with the key residues Thr474 and Cys481 of BTK. The molecular dynamics (MD) results demonstrated that all five compounds above could bind with BTK stably as its cognate ligand in dynamic conditions. This work identified several potential BTKIs using a computer-aided drug design approach and may provide crucial information for developing novel BTKIs.Communicated by Ramaswamy H. Sarma.

In-silico study of seaweed secondary metabolites as AXL kinase inhibitors

AXL kinase is an attractive cancer target for drug design and it is involved in different cancers. A set of molecule databases with 1072 secondary metabolites from seaweeds were screened against the AXL kinase active site and eight molecules were shortlisted for further studies. From the docking analysis of the complexes, four molecules GA011, BE005, BC010, and BC005 are showing prominent binging. From the 100 ns of molecular dynamics simulations and ligand-bound complex MM-PBSA free energy analysis, two molecules BC010 (ΔG = −135.38 kJ/mol) and BE005 (ΔG = −141.72 kJ/mol) are showing molecule stability in the active site also showing very strong binding free energies. It suggests these molecules could be the potent molecules for AXL kinase.

Discovery of new small-molecule cyclin-dependent kinase 6 inhibitors through computational approaches

Excessive cell proliferation due to cell cycle disorders is one of the hallmarks of breast cancer. Cyclin-dependent kinases (CDKs), which are involved in the transition of the cell cycle from G1 phase to S phase by combining CDKs with cyclin, are considered promising targets with broad therapeutic potential based on their critical role in cell cycle regulation. Pharmacological evidence has shown that abnormal cell cycle due to the overexpression of CDK6 is responsible for the hyperproliferation of cancer cells. Blocking CDK6 expression inhibits tumour survival and growth. Therefore, CDK6 can be regarded as a potential target for anticancer therapeutics. Thus, small molecules that can be considered CDK inhibitors have been developed into promising anticancer drugs. In this study, combined structure-based and ligand-based in silicon models were created to identify new chemical entities against CDK6 with the appropriate pharmacokinetic properties. The database used to screen drug-like compounds in this thesis was based on the best E-pharmacophore hypothesis and the best ligand-based drug hypothesis. As a result, 147 common compounds were identified by further molecular docking. Surprisingly, the in vitro evaluation results of 20 of those compounds showed that the two had good CDK6 inhibitory effects. The best compound was subjected to kinase panel screening, followed by molecular dynamic simulations. The 50-ns MD studies revealed the pivotal role of VAL101 in the binding of inhibitors to CDK6. Overall, the identification of two new chemical entities with CDK6 inhibitory activity demonstrated the feasibility and potential of the new method.

In silico identification and design of potent peptide inhibitors against PDZ-3 domain of Postsynaptic Density Protein (PSD-95)

Unique intrinsic properties of peptides like low toxicity, high biological activity, and specificity make them attractive therapeutic agents. PDZ-binding peptide inhibitors have been demonstrated for curing of Alzheimer, Parkinson, Dementia, and other central nervous system ailments. In this article, we report the successful use of an integrated computational protocol to analyze the structural basis of how peptides bind to the shallow groove of the third PDZ domain (PDZ-3) from the postsynaptic density (PSD-95) protein. This protocol employs careful and precise computational techniques for design of new strategy for predicting novel and potent peptides against PDZ protein. We attempted to generate a pharmacophore model using crystal structure of peptide inhibitor bound to the PDZ-3. A highly specific and sensitive generated pharmacophore model was used for screening virtual database generated using different combination of amino acid substitutions as well as decoy peptide database for its sensitivity and specificity. Identified hit peptides were further analyzed by docking studies, and their stability analyzed using solvated molecular dynamics. Quantum Mechanics/Molecular Mechanics (QM/MM) interaction energy and GMX-PBSA scoring schemes were used for ranking of stable peptides. Computational approach applied here generated encouraging results for identifying peptides against PDZ interaction model. The workflow can be further exercised as a virtual screening technique for reducing the search space for candidate target peptides against PDZ domains.