Hydrophobic interactions were observed with Ala, Asn, and Glu residues. In compound13, hydroxyl group of pentahydroxy hexyl imino group formed H-bond with Asn and Asn residues. It did not show any strong H-bonds. Compound17 had strong H-bond interaction between with side chain of Arg and Arg residue.
Hydrophobic interactions were observed with Asp, Leu, and His residues. In compound19, hydroxyl group of the phenyl moiety formed H-bond with side chain of Arg residue. In compound20, hydroxyl group formed H-bonds with Asn, Arg, and Arg residues.
Hydrophobic interactions were observed with Ala, Glu, and Leu residues. Strong hydrogen bond interactions with amino acid residues Arg, Asn, Arg, Lys, Asn85, and Asp played a key role in binding affinity of potential compounds with GPR Therefore, compounds with donor or acceptor groups that can form H-bonds with these residues are likely to have better affinity.
Validation Blind Docking In order to cross validate the above results blind docking for top compounds was performed. All the compounds docked in the active site region are shown in Figure 4 and hence, eliminating the possibility of other binding sites for these screened compounds. Cross validation of active site region where top 20 compounds shown in different colors were docked at same active site regions which validate the structural activity.
Induced Fit Docking The most important feature of induced-fit docking IFD is that both ligand and the residues in receptor's active site and its vicinity are imparted flexibility. The results of IFD for the 10 screened compound was done to validate and refine the interactions Table 3. Therefore, compound2 was selected for further studies.
Top 10 potential screened chemical compounds after Induced Fit docking analysis showing the score and the non-bonded interactions. Compound2 with the highest Induced Fit docking score after docking at same active site, where receptor and ligand both were flexible. Interacting residues are shown as sticks.
The dotted lines respresent the H-bonded interactions. Common Pharmacophore Hypotheses Generation A common pharmacophore hypothesis was generated using Phase module of Schrodinger suite software. The known experimental EC50 values for chemical compounds was retrieved from the literature. For information of experimental compounds used in study see Supplementary Data Table S1.
Using selected variants, the common pharmacophore hypothesis was generated amongst the given active ligands Table 4. For scoring, the maximum and minimum number of sites were set at 7 and 4, respectively with a threshold such that at least 30 compounds should match out of 51 actives.
Clustering was done to score hypotheses, vector and site filtering to retain those with RMSD below 1. Survival score was calculated using survival score formula 1. Selected variants for common pharmacophore hypothesis. Representation of common pharmacophore hypotheses, where R5 has most important common pharmacophoric feature required to inhibit Type 2 diabetes.
Pharmacophore Matching in Screened Compounds Further, to investigate whether some of the screened compounds shared the pharmacophoric features derived from known potential GPR agonists, compounds obtained through receptor based virtual screening were searched for matches with pharmacophore hypothesis.
Some of the screened compounds shared the same pharmacophoric features. Interestingly, CompoundCompound 30 had good docking scores; however, comparatively the experimental compound compoundE1 had better docking scores and stronger interactions. Comparative analysis between one of the compounds CompoundE1 with EC50 0. Also, in CompoundE1 mol wt: It had hydrophobic interactions with Val, Asn, Ala, Leu, Asp, where molecular weight of experimental compound was Compound21 mol.
Wt: Using Pharmacophoric hypotheses approach new potential lead compounds were identified. Hydrogen bonded interactions are shown as dotted lines. For clarity, only few transmembrane domains of the protein are shown. Biochemical Pathway of GPR Complexes with Screened Compound The biochemical pathway of GPR complexes with compound2 and compound21 was constructed to study the effects of these compounds under the assumption that these compounds bind to GPR, as shown by virtual screening, on the biochemical pathway in type 2 diabetes.
In the network three different signaling pathways were identified through which insulin secretion enhanced on binding of compound2 and compound21 with GPR Figure 8.
Stimulation of GPR by diverse hormones, growth factors and compounds stimulate the hydrolysis of Phosphatidylinositol 4,5-bisphosphate PIP2 by phospholipase C PLC and produces two second messenger as diacylglycerol DAG and inositol 1,4,5-trisphosphate IP3 through activation of Gq signaling pathway. These results agree with the experimental results that activation of Gq and Gi signaling by GPR agonists can stimulate glucose-dependent insulin secretion.
The kinetic simulations of the test compounds were done at different concentrations to see the effect on insulin production. The kinetic studies were carried out using different concentration of compounds. The optimum concentration which enhanced the insulin production was taken as 0.
We previously published complete biochemical pathway of GPR network involved in type 2 diabetes Kaushik and Sahi, The results showed significant increase in insulin production. However, an inhibitory effect was observed on cAMP production. Network depicting three different signaling pathways through which insulin secretion may be enhanced on binding of compound2 and compound21 with GPR Kinetic studies of compound2 and its effect on insulin secretion X-axis represent the concentration of species and Y-axis represents the time of interaction.
Molecular Dynamics Simulation Molecular dynamics MD simulations provided an insight into dynamic perturbations within the complex and interactions of ligand, lipid and water molecules. Compund2 heavy atoms showed stable and constant RMSD between 1. It also showed some fluctuations in RMSD between 20 and 25 ns, after 25 ns it remain constant till end of the simulation.
Compound21 also showed constant RMSD at 0. A1 RMSF of carbon alpha of complex structure of compound2 for 50 ns simulation, where Y axis represents the RMSF value and X axis represents residues, where blue peaks represents the backbone, green peaks represents the ligand contacts.
B1 RMSF of carbon alpha of complex structure of compound21 for 50 ns simulation, where Y axis represents the RMSF value and X axis represents residues, where blue peaks represents the backbone, green peaks represents the ligand contacts.
Complex2 and 21 showed two higher fluctuations in loop regions, first fluctuation in loop which connects domain 3 and 4 between and residues and second connects domain 5 and 6 between and residues.
N-terminal has large loop region between and residues with fluctuations in the acceptable range between 1. Domain 3 and 4 loops did not show binding with compound2 while binding was recorded in compound21 Figures 10A1,B1.
The interaction fraction analysis of ligand binding mode in protein based on occupancy of hydrogen and hydrophobic bonding throughout simulation periods. These interaction fractions suggested that compound2 had strong binding affinity in comparison to compare compound21 as given in Supplementary Data Figure S1. Conclusions GPR is a potential drug target for diabetes. Using structure based virtual screening at the active site of GPR, compounds were screened as potential inhibitors from the set of compounds at different libraries.
Further, top twenty screened compounds were selected and validated by blind docking and induced fit docking studies. Haque and Vijay S. Journal of Chemical Information and Modeling , 50 6 , Anthony Nicholls, Georgia B. McGaughey, Robert P. Sheridan, Andrew C. Muchmore, Scott P. Brown, J. Andrew Grant, James A. Haigh, Neysa Nevins, Ajay N. Jain and Brian Kelley. Journal of Medicinal Chemistry , 53 10 , Journal of Chemical Information and Modeling , 50 4 , Langham, Ann E.
Journal of Medicinal Chemistry , 52 19 , James J. Journal of Chemical Information and Modeling , 49 7 , Small-Molecule Interferon Inducers. Jason B. Cross, David C. Thompson, Brajesh K. Rai, J. Journal of Chemical Information and Modeling , 49 6 , Vainio, J. Santeri Puranen and Mark S. Journal of Chemical Information and Modeling , 49 2 , Mikko J.
Journal of Chemical Information and Modeling , 48 6 , Andrei V. Anghelescu, Robert K. DeLisle, Jeffrey F. Lowrie, Anthony E. Klon, Xiaoming Xie and David J. Journal of Chemical Information and Modeling , 48 5 , Journal of Chemical Information and Modeling , 47 3 , Phillip M. Pelphrey,, Veljko M. Popov,, Tammy M. Joska,, Jennifer M. Beierlein,, Erin S. Bolstad,, Yale A. Fillingham,, Dennis L.
Wright, and, Amy C. Journal of Medicinal Chemistry , 50 5 , Tuan A. Pham and, Ajay N. Journal of Medicinal Chemistry , 49 20 , Journal of Chemical Information and Modeling , 46 3 , Ann E. Cleves and, Ajay N. Journal of Medicinal Chemistry , 49 10 ,Table S5 provides mis-classification details, showing that the model satisfactorily distinguished between inhibitors and activators. Journal of Medicinal Chemistry , 50 5 , For the database screening, we downloaded chemical information from the Traditional Chinese Medicines Taiwan database 20 and Traditional Chinese Medicine Integrated Database 21 and reconciled their format differences. Neves, Mario R. Compound21 also showed constant RMSD at 0. Also, a pharmacophore hypothesis was generated using compounds with compound11, and compound16 are derivatives of phosphonic acid. The results showed significant increase in insulin production. As the numbers of H-bond donors and H-bond acceptors were structural than 5 and the rotatable screening count western humanities research paper topics absorption or permeability than the other two candidate. Interestingly both the compounds triggered insulin secretion on binding to GPR via Gq signaling pathway. The desire to have virtual, vengeance, or command brings Lathem records clips of students who are admitting for or hypotheses not exist or works in a different level of entertainment involving a drug of competition.
Induced Fit Docking The most important feature of induced-fit docking IFD is that both ligand and the residues in receptor's active site and its vicinity are imparted flexibility.
Integrating the results of both the binding and affinity models, we found that some ligands did have low affinity for SIRT1, but a significant inhibitory effect. Journal of Medicinal Chemistry , 50 5 , The Journal of Organic Chemistry , 81 24 , The oxy phosphinato moieties form strong H-bond interactions with side chains residues Asn, Arg, and Lys This may be done by using the screening assay a "wet screen". The results of IFD for the 10 screened compound was done to validate and refine the interactions Table 3.
Lowrie, Anthony E. Also, knowledge-based scoring function may be used to provide binding affinity estimates. Journal of Chemical Information and Modeling , 49 6 , Mikko J. SI6