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Diabetes mellitus is one of the most commonly found disorders relating to ageing and increasing obesity. It has been estimated that type 2 diabetes accounts for 90% of the cases worldwide (Zimmet et al., 2001; Tewari et al., 2003). Post prandial hyperglycemia has many other complications along with Type 2 Diabetes mellitus like the micro-muscular diseases. A lot of drugs are available today to combat this problem but most of them has serious side effects such as liver toxicities and adverse gastrointestinal symptoms.
The most coveted therapetic approach to reduce postprandial hyperglycemia is to target the intestinal carbohydrate hydrolysing enzyme (Holman et al., 1999), maltase gluco amylase and glucose isomaltase. Human intestinal maltase gluco amylase is a small intestinal enzyme, 875 amino acids in length and it works in tandem with the small intestinal sucrase isomaltase to hydrolyze linear alpha 1,4 and branched alpha 1,6 oligosaccharide residues. Both these enzymes (EC. 126.96.36.199, a-D-glucoside glucohydrolase) perform hydrolysis actions on the starch residues into simpler oligosaccharide units. Both of these enzymes are composed of N and C catalytic domains in which the N-terminal catalytic domains of maltase glucoamylase can act on shorter alpha 1,4 glycosidic chains and the sucrose isomaltase has a greater specificity towards larger alpha 1,4 and alpha 1,6 glycosidic chains .Its widely distributed in a large number of organisms including the microorganisms, plants and animal tissues although there is a high variation in substrate specificity.[chiba-chiba-kimura].Basically the alphaglucosidase inhibitors are divided into 3 classes- polyhydroxylated N substituted heterocyclic compounds; polyhydroxylated cycloalkenes; and oligomers of pseudosugars.
Recently, various flavone glucoside derivatives have also been identified from cinnamon cassia as potent selective inhibitors of maltase glucoamylase. In this study, we made a study of maltase glucoamylase inhibiton involving the docking simulations and in vitro enzyme assay. Binding interactions of the inhibitors were analyzed by implementation of an accurate solvation model and calculations of the binding free energy between maltase glucoamylase and putative ligands, which would have an effect of increasing the hit rate in enzyme assay.( Shoichet, ).
It is shown that the docking simulation with the improved binding free energy function can be a useful tool for elucidating the activities of the identified inhibitors.
Fig1. Ligands-(Flavone Glycosides) Cinncassiol derivatives
Enzyme maltase gluco amylase preparation
Human maltase gluco amylase (PDB ID code: 3L4V) with high resolution 2.0 AËš was downloaded from Brookhaven Protein Data Bank to serve as the docking acceptor. The crystal structure is a complex of 3L4V/Kotanalol/NAG. First, crystallographic waters were removed; then, the complex was optimized under Gromacs force field by performing 500 steps steepest descent energy minimization and a followed conjugate gradient energy minimization with a root-mean square criterion of the potential energy gradient of 0.01 kcal/mol/AËš and finally, Kotanalol was deleted and the left 3L4V/NAG complex was used for docking experiment .
We mainly selected ten small-molecule flavone glucoside derivatives (Cinnacassiol) from the bark of Cinnamon cassiea, which bear structure identity. In order to get the most stable ligand conformations, the structure-optimizing calculation was carried out by hybrid density functional theory using the quantum chemistry software Gaussian[ M.J. Frisch ] and the structures with the lowest energy were selected for the following docking study. When docking, the Gasteiger-Huckel atomic charge was chosen for small-molecule ligand.
Automated Docking setup
We used the AutoDock program in the docking studies of maltase gluco amylase inhibitors because the outperformance of its scoring function over those of the others had been shown in several target proteins. The atomic coordinates of maltase gluco amylase obtained from the PDB (3L4V) were used as the receptor model in the docking simulations.
This selection was based on the drug-like filters that adopt only the compounds with physicochemical properties of potential drug candidates and without reactive functional group(s). All of the compounds were obtained from pubchem followed by the assignment of Gasteiger-Marsili atomic charges. AMBER force field parameters were assigned for calculating the van der Waals interactions and the internal energy of a ligand as implemented in the Auto-Dock program. Docking simulations with AutoDock were then carried out in the binding site of maltase gluco amylase to score and rank the compounds in the docking library according to their calculated binding affinities.
Docking simulations with AutoDock were then carried out to score and rank the compounds according to their calculated binding affinities.In the actual docking simulation of the compounds in the docking library, we used the empirical AutoDock scoring function improved by the implementation of a new solvation model for a compound. The modified scoring function has the following form:
C:\Users\sree\Desktop\Untitled.png Eq 1.
where WvdW, Whbond, Welec, Wtor, and Wsol are the weighting factors of van der Waals, hydrogen bond, electrostatic interactions, torsional term, and desolvation energy of inhibitors, respectively. Rij represents the interatomic distance, and Aij, Bij, Cij, and Dij are related
to the depths of the potential energy well and the equilibrium separations between the two atoms. The hydrogen bond term has an additional weighting factor, E(t), representing the angle-dependent directionality. Cubic equation approach was applied to obtain the dielectric constant required in computing the inter atomic electrostatic interactions between maltase gluco amylase and a ligand molecule.( Park) In the entropic term, Ntor is the number of sp3 bonds in the ligand. In the desolvation term, Si and Vi are the solvation parameter and the fragmental volume of atom i,( Stouten) respectively, while Occmax i stands for the maximum atomic occupancy. In the calculation of molecular solvation free energy term in Eq. 1, we used the atomic parameters recently recently developed by Kang et al.( Kang, ) because those of the atoms other than carbon were unavailable in the current version of Auto-Dock. This modification of the solvation free energy term is expected to increase the accuracy in virtual screening because the underestimation of ligand solvation often leads to the overestimation of the binding affinity of a ligand with many polar atoms.( Shoichet)
The docking simulation of a compound in the docking library started with the calculation of the three-dimensional grids of interaction energy for all of the possible atom types present in chemical database. These uniquely defined potential grids for the receptor protein were then used in common for docking simulations of all compounds in the docking library. As the center of the common grids in the active site, we used the center of mass coordinates of the docked structure of the probe molecule, kotanalol , whose binding mode had been known in the active site maltase gluco amylase The calculated grid maps were of dimension 61 · 61 · 61 points with the spacing of 0.375 A Ëš , yielding a receptor model that includes atoms within 22.9 A Ëš of the grid center. For each compound , 10 docking runs were performed .Maximum number of generations and energy evaluation were set to 27,000 and 2.5 · 105, respectively.
Molecular dynamics setup
Results and Discussion
To study the differences between the binding modes of these ligands and to reveal the most essential amino acid residues involved ligand recognition, molecular docking was performed. The ten docking conformations for each ligand were divided into groups according to a 1.0AËš RMSD criterion by using the Cluster module in ADT. Cluster conformation analysis are to compare the RMSD of the lowest energy conformations and their RMSD to one another, to group them into families of similar conformations or clusters. The reliability of the docked result depends on the similarity of its final docked conformation. The groups indicate that all the ligands mainly take one conformation.
Besides RMSD cluster analysis, AutoDock also uses binding free energy evaluation to find the best binding mode. Energy items calculated by AutoDock are characterized by intermolecular energy (consist of van der Walls energy, hydrogen bonding energy, desolvation energy, and electrostatic energy), internal energy of ligand, and torsional free energy. The first two of these combined give the docking energy while the first and third terms build up the binding energy. During all these interactions, the electrostatic interaction between ligands and receptor is the most important, because in most cases it can decide the binding strength and the location of ligand, while the hydrophobic interaction of some certain groups can affect the inhibitory activity to a larger extent. The energy information is listed in Table 1, and the interaction modes of the ligands and maltase gluco amylase are depicted in Fig. 2,where only the amino acid residues located within 5AËš of the agonists are displayed.
Binding and docking energies of ligands and maltase gluco amylase calculated by AutoDock.
Inhibition constant (pKi)
Experimental activity pIC50 (assay)
To obtain better binding configurations for maltase gluco amylase-ligand complexes, we have performed molecular dynamics (MD) in aqueous solution. The most stable structures of maltase gluco amylase -ligand complexes obtained from docking simulation were equilibrated in solution
through 0.5 ns MD simulation with AMBER program, which had been successful in modeling the structures of proteins23 and nucleic acids24 in solution. This equilibration procedure started with the addition sodium ions as the counterion to neutralize the total charge of the all-atom model of maltase gluco amylase. The system was then immersed in a rectangular solvent box containing about 8000 TIP3P water molecules.After 1000 cycles of energy minimization to remove bad vander Waals contacts, we equilibrated the system beginning with 20 ps equilibration dynamics of the solvent molecules at 300 K.The next step involved equilibration of the solute with a fixed configuration of the solvent molecules for 10 ps at 10, 50, 100, 150, 200, 250, and 300 K. Then, the equilibration dynamics of the entire system was performed at 300 K for 500 ps using the periodic boundary condition. We used a time step of 1.5 fs and a nonbond-interaction cutoff radius of 12 Å.