Chanin Park

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

Aim:Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches.Methods:The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations.Results:The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischers randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10.Conclusion:Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.

Highly potent tyrosinase inhibitor, neorauflavane from Campylotropis hirtella and inhibitory mechanism with molecular docking.

Tyrosinase inhibition may be a means to alleviate not only skin hyperpigmentation but also neurodegeneration associated with Parkinsons disease. In the course of metabolite analysis from tyrosinase inhibitory methanol extract (80% inhibition at 20 μg/ml) of Campylotropis hirtella, we isolated fourteen phenolic compounds, among which neorauflavane 3 emerged as a lead structure for tyrosinase inhibition. Neorauflavane 3 inhibited tyrosinase monophenolase activity with an IC50 of 30 nM. Thus this compound is 400-fold more active than kojic acid. It also inhibited diphenolase (IC50=500 nM), significantly. Another potent inhibitor 1 (IC50=2.9 μM) was found to be the most abundant metabolite in C. hirtella. In kinetic studies, compounds 3 showed competitive inhibitory behavior against both monophenolase and diphenolase. It manifested simple reversible slow-binding inhibition against monophenolase with the following kinetic parameters: Ki(app)=1.48 nM, k3=0.0033 nM(-1) min(-1) and k4=0.0049 min(-1). Neorauflavane 3 efficiently reduced melanin content in B16 melanoma cells with 12.95 μM of IC50. To develop a pharmacophore model, we explored the binding mode of neuroflavane 3 in the active site of tyrosinase. Docking results show that resorcinol motif of B-ring and methoxy group in A-ring play crucial roles in the binding the enzyme.

A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors

Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure–activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.

Multi-conformation dynamic pharmacophore modeling of the peroxisome proliferator-activated receptor γ for the discovery of novel agonists.

Activation of the peroxisome proliferator-activated receptor γ (PPARγ) is important for the treatment of type 2 diabetes and obesity through the regulation of glucose metabolism and fatty acid accumulation. Hence, the discovery of novel PPARγ agonists is necessary to overcome these diseases. In this study, a newly developed approach, multi-conformation dynamic pharmacophore modeling (MCDPM), was used for screening candidate compounds that can properly bind PPARγ. Highly populated structures obtained from molecular dynamics (MD) simulations were selected by clustering analysis. Based on these structures, pharmacophore models were generated from the ligand-binding pocket and then validated to check the rationality. Consequently, two hits were retrieved as final candidates by utilizing virtual screening and molecular docking simulations. These compounds can be used in the design of novel PPARγ agonists.

Computational Exploration for Lead Compounds That Can Reverse the Nuclear Morphology in Progeria

Progeria is a rare genetic disorder characterized by premature aging that eventually leads to death and is noticed globally. Despite alarming conditions, this disease lacks effective medications; however, the farnesyltransferase inhibitors (FTIs) are a hope in the dark. Therefore, the objective of the present article is to identify new compounds from the databases employing pharmacophore based virtual screening. Utilizing nine training set compounds along with lonafarnib, a common feature pharmacophore was constructed consisting of four features. The validated Hypo1 was subsequently allowed to screen Maybridge, Chembridge, and Asinex databases to retrieve the novel lead candidates, which were then subjected to Lipinskis rule of 5 and ADMET for drug-like assessment. The obtained 3,372 compounds were forwarded to docking simulations and were manually examined for the key interactions with the crucial residues. Two compounds that have demonstrated a higher dock score than the reference compounds and showed interactions with the crucial residues were subjected to MD simulations and binding free energy calculations to assess the stability of docked conformation and to investigate the binding interactions in detail. Furthermore, this study suggests that the Hits may be more effective against progeria and further the DFT studies were executed to understand their orbital energies.

Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinskis rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

Exploration of Virtual Candidates for Human HMG-CoA Reductase Inhibitors Using Pharmacophore Modeling and Molecular Dynamics Simulations

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.

Molecular Docking and Dynamics Simulation, Receptor-based Hypothesis: Application to Identify Novel Sirtuin 2 Inhibitors

Sirtuin, NAD+‐dependent histone deacetylase enzyme, emerged as a potential therapeutic target, and modulations by small molecules could be effective drugs for various diseases. Owing to the absence of complex structure of sirtuin 2 (SIRT2), sirtinol was docked in the NAD+ binding site and subjected to 5‐nseconds molecular dynamics (MD) simulation. LigandScout was used to develop hypotheses based on 3‐representative SIRT2 complex structures from MD. Three structure‐based hypotheses are generated and merged to form dynamics hypothesis. The dynamics hypothesis was validated using test and decoy sets. The results showed that dynamic hypothesis represents the complementary features of SIRT2 active site. Dynamic hypothesis was used to screen ChemDiv database, and hits were filtered through ADMET, rule of five, and two different molecular docking studies. Finally, 21 molecules were selected as potent leads based on consensus score from LigandFit, Gold fitness score, binding affinity from VINA as well as based on the important interactions with critical residues in SIRT2 active site. Hence, we suggest that the dynamic hypothesis will be reliable in the identification of SIRT2 new lead as well as to reduce time and cost in the drug discovery process.

Structural importance of the C-terminal region in pig aldo-keto reductase family 1 member C1 and their effects on enzymatic activity

BackgroundPig aldo-keto reductase family 1 member C1 (AKR1C1) belongs to AKR superfamily which catalyzes the NAD(P)H-dependent reduction of various substrates including steroid hormones. Previously we have reported two paralogous pig AKR1C1s, wild-type AKR1C1 (C-type) and C-terminal-truncated AKR1C1 (T-type). Also, the C-terminal region significantly contributes to the NADPH-dependent reductase activity for 5α-DHT reduction. Molecular modeling studies combined with kinetic experiments were performed to investigate structural and enzymatic differences between wild-type AKR1C1 C-type and T-type.ResultsThe results of the enzyme kinetics revealed that Vmax and kcat values of the T-type were 2.9 and 1.6 folds higher than those of the C-type. Moreover, catalytic efficiency was also 1.9 fold higher in T-type compared to C-type. Since x-ray crystal structures of pig AKR1C1 were not available, three dimensional structures of the both types of the protein were predicted using homology modeling methodology and they were used for molecular dynamics simulations. The structural comparisons between C-type and T-type showed that 5α-DHT formed strong hydrogen bonds with catalytic residues such as Tyr55 and His117 in T-type. In particular, C3 ketone group of the substrate was close to Tyr55 and NADPH in T-type.ConclusionsOur results showed that 5α-DHT binding in T-type was more favorable for catalytic reaction to facilitate hydride transfer from the cofactor, and were consistent with experimental results. We believe that our study provides valuable information to understand important role of C-terminal region that affects enzymatic properties for 5α-DHT, and further molecular mechanism for the enzyme kinetics of AKR1C1 proteins.

Competitive protein tyrosine phosphatase 1B (PTP1B) inhibitors, prenylated caged xanthones from Garcinia hanburyi and their inhibitory mechanism

Protein tyrosine phosphatase 1B (PTP1B) plays important role in diabetes, obesity and cancer. The methanol extract of the gum resin of Garcinia hanburyi (G. hanburyi) showed potent PTP1B inhibition at 10µg/ml. The active compounds were identified as prenylated caged xanthones (1-9) which inhibited PTP1B in dose-dependent manner. Carboxybutenyl group within caged motif (A ring) was found to play a critical role in enzyme inhibition such as 1-6 (IC50s=0.47-4.69µM), whereas compounds having hydroxymethylbutenyl 7 (IC50=70.25µM) and methylbutenyl 8 (IC50>200µM) showed less activity. The most potent inhibitor, gambogic acid 1 (IC50=0.47µM) showed 30-fold more potency than ursolic acid (IC50=15.5µM), a positive control. In kinetic study, all isolated xanthones behaved as competitive inhibitors which were fully demonstrated with Km, Vmax and Kik/Kiv ratio. It was also proved that inhibitor 1 operated under the enzyme isomerization model having k5=0.0751µM-1S-1, k6=0.0249µM-1S-1 and Kiapp=0.499µM. To develop a pharmacophore model, we explored the binding sites of compound 1 and 7 in PTP1B. These modeling results were in agreement with our findings, which revealed that the inhibitory activities are tightly related to caged motif and prenyl group in A ring.