Computational drug design of potential alpha-amylase inhibitors using some commercially available flavonoids
Abstract
The primary objective of this study was to investigate the alpha-amylase inhibitory activity of flavonoids using in silico docking studies. In this perspective, flavonoids like biochanin, chrysin, hesperitin, morin, tricin and vitexycarpin were selected. Acarbose, a known alpha-amylase inhibitor was used as the standard. In silico docking studies were carried out using AutoDock 4.2, based on the Lamarckian genetic algorithm principle. The results showed that all the selected flavonoids showed binding energy ranging between -7.20 kcal/mol to -6.21 kcal/mol when compared with that of the standard (-2.94 kcal/mol). Inhibition constant (5.31 µM to 27.89 µM) and intermolecular energy (-8.99 kcal/mol to -7.41 kcal/mol) of the flavonoids also coincide with the binding energy. The alpha-amylase inhibitory activity of the selected flavonoids was in order of tricin > hesperitin > vitexycarpin > chrysin > morin > biochanin. These molecular docking analyses could lead to the further development of potent alpha-amylase inhibitors for the treatment of diabetes.
Introduction
Diabetes has become a leading killer disease in recent years. According to WHO, it is estimated that 3% of the world's population have diabetes and the prevalence is expected to double by the year 2025 to 6.3% (Li et al. 2011). Diabetes mellitus is a metabolic disorder characterized by hyperglycemia resulting from defects in insulin action, insulin secretion or both. Type 1 diabetes is caused by a deficiency of beta-pancreatic cells insulin secretion. Type 2 diabetes is associated with obesity and is characterized by an initial phase progressive insulin resistance, with ensuing reduction in the ability of pancreatic hormone to promote peripheral glucose disposal and to decreases hepatic glucose output (Vianna et al. 2011; Lamba et al. 2011).
The alpha-amylases are a group of enzymes which shares many common characteristic properties. This class of enzymes has a variety of different specific sites for action on various glucose residues linked through alpha-1-1, alpha-1-4 and alpha-1-6 glycosidic bonds (Wolfenden et al., 1998). Amylases are mainly classified into two categories, endoamylases and exoamylases. Endoamylases produces linear and branched oligosaccharides of different chain lengths catalysing hydrolysis in a random manner in the interior of the starch molecule. Exoamylases produces short end products by acting from the non-reducing end (Gupta et al., 2003).
Alpha-amylase (alpha-1,4 glucan-4-glucanohydrolase) at first converts starch to oligosaccharides by hydrolysing alpha-1,4-glucan bonds. Thus first reaction in digestion of carbohydrates is initiated by alpha amylase by forming oligosaccharides. Unabsorbed carbohydrates (disaccharides and oligosaccharides) will then get bound to alpha glucosidase enymes in the brush border of small intestine (Sarikaya et al., 2000).
In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex (Sandeep et al., 2011).Currently, the use of computers to predict the binding of libraries of small molecules to known target structures is an increasingly important component in the drug discovery process (Koppen, 2009).
AutoDock 4.2 is the most recent version which has been widely used for virtual screening, due to its enhanced docking speed (Collignon et al., 2011; Prakhov et al., 2010).Its default search function is based on Lamarckian Genetic Algorithm (LGA), a hybrid genetic algorithm with local optimization that uses a parameterized free-energy scoring function to estimate the binding energy. Docking of small molecules in the receptor binding site and estimation of binding affinity of the complex is a vital part of structure based drug design (Cosconati et al., 2010; Seeliger and Groot, 2010).
Flavonoids belong to a group of natural substances with variable phenolic structures and are found in fruit, vegetables, stems, flowers, tea, and wine. These natural products were known for their beneficial effects on health long before flavonoids were isolated as the effective compounds. Research on flavonoids received an added impulse with the discovery of the French paradox, the low cardiovascular mortality rate observed in Mediterranean populations in association with red wine consumption and a high saturated fat intake. The flavonoids in red wine are responsible, at least in part, for this effect (Groot and Rauen, 1998).
Flavonoids and their related compounds are low molecular weight substances, which are a group of natural products which exhibits various biological and pharmacological activities like antibacterial, antiviral, antioxidant, anti-inflammatory, antiallergic, hepatoprotective, antithrombotic, antiviral and antimutagenic effects and inhibition of several enzymes (Madeswaran et al., 2012; Formica and Regelson, 1995).
However there is no conclusive report as to whether the alpha-amylase activity of the flavonoids.The stereochemistry of binding of the flavonoids on alpha-amylase has not yet been characterized. In the present study, the structural models of the ligands in the alpha-amylase binding sites has been carried out, which may facilitate further development of more potent alpha-amylase inhibitory agents.
Materials and Methods
Software required
Python 2.7- language was downloaded from www.python.com, Cygwin (a data storage) c:\program and Python 2.5 were simultaneously downloaded from www.cygwin.com, Molecular graphics laboratory (MGL) tools and AutoDock4.2 was downloaded from www.scripps.edu, Discovery studio visualize 2.5.5 was downloaded from www.accelerys. com, Molecular orbital package (MOPAC), ChemSketch was downloaded from www.acdlabs.com. Online smiles translation was carried out using cactus.nci.nih. gov/translate/.
Docking Methodology
Lamarckian genetic algorithm (LGA) is employed for the ligand conformational searching, which is a hybrid of a genetic algorithm and a local search algorithm. This algorithm first builds a population of individuals (genes), each being a different random conformation of the docked molecule. Each individual is then mutated to acquire a slightly different translation and rotation and the local search algorithm then performs energy minimizations on a user-specified proportion of the population of individuals. The individuals with the low resulting energy are transferred to the next generation and the process is then repeated. The algorithm is called Lamarckian because every new generation of individuals is allowed to inherit the local search adaptations of their parents (Madeswaran et al., 2012).
An extended PDB format, termed as PDBQT file was used for coordinate files which includes atomic partial charges. AutoDock Tools was used for creating PDBQT files from traditional PDB files (Khairallah et al., 2008). Crystal structure of alpha-amylase enzyme was downloaded from the Brookhaeven protein data bank (Figure 1).
Figure 1: alpha-Amylase enzyme from Brookhaeven protein data bank (1HNY)
In Figure 2, the flavonoid ligands like biochanin, chrysin, hesperitin, morin, tricin, vitexycarpin, and acarbose were built using ChemSketch and optimized using "Prepare Ligands" in the AutoDock 4.2 for docking studies (Bikadi and Hazai, 2009).
Figure 2: The optimized ligand molecules (1 biochanin, 2 chrysin, 3 hesperitin, 4 morin, 5 tricin, 6 vitexycarpin, and 7 acarbose)
Lead optimization of the selected compounds was done by computation of druglikeness properties. The drug-likeness scores of the compounds were evaluated with the help of Lipinski's rule. The preparation of the target protein 1HNY (unbound target) with the AutoDock Tools software involved adding all hydrogen atoms to the macromolecule, which is a step necessary for correct calculation of partial atomic charges. Gasteiger charges are calculated for each atom of the macromolecule in AutoDock 4.2 instead of Kollman charges which were used in the previous versions of this program. Three-dimensional affinity grids of size 277 x 277 x 277 Angstrom with 0.6 Angstrom spacing were centered on the geometric center of the target protein and were calculated for each of the following atom types: HD, C, A, N, OA, and SA, representing all possible atom types in a protein. Additionally, an electrostatic map and a desolvation map were also calculated (Konc et al., 2011).
Rapid energy evaluation was achieved by precalculating atomic affinity potentials for each atom in the ligand molecule. In the AutoGrid procedure, the target enzyme was embedded on a three dimensional grid point (Madeswaran et al., 2013). The energy of interaction of each atom in the ligand was encountered. Important docking parameters for the LGA as follows: population size of 150 individuals, 2.5 million energy evaluations, maximum of 27,000 generations, number of top individuals to automatically survive to next generation of 1, mutation rate of 0.02, crossover rate of 0.8, 10 docking runs, and random initial positions and conformations. The probability of performing local search on an individual in the population was set to 0.06.
AutoDock was run several times to get various docked conformations, and used to analyze the predicted docking energy. The binding sites for these molecules were selected based on the ligand-binding pocket of the templates. AutoDock Tools provide various methods to analyze the results of docking simulations such as, conformational similarity, visualizing the binding site and its energy and other parameters like intermolecular energy and inhibition constant. For each ligand, ten best poses were generated and scored using AutoDock 4.2 scoring functions (Madeswaran et al., 2011).
Result and Discussion
In silico docking study, was carried out to identify the inhibiting potential of selected flavonoids against alpha-amylase enzyme. The docking studies were performed by the use of AutoDock4.2. In the docking studies, if a compound shows lesser binding energy compared to the standard it proves that the compound has higher activity (Chang et al., 2010).
Analysis of the receptor/ligand complex models generated after successful docking of the flavonoids was based on the parameters such as hydrogen bond interactions, drug-drug interactions, binding energy, RMSD of active site residues and orientation of the docked compound within the active site (Madeswaran et al., 2012).
The docking poses were ranked according to their docking scores and both the ranked list of docked ligands and their corresponding binding poses. This ranking of the compounds were based on their binding energy with the enzyme. If the binding energy of the compound is less, then the particular compound has more active in nature. In Figure 3, docked pose of alpha-amylase enzyme with the ligands tricin and acarbose clearly demonstrated the binding positions of the ligand with the enzyme.
Figure 3: Docked pose of alpha-amylase enzyme (1HNY) with tricin and acarbose
The binding sites of the acarbose was found to be Tyr151, Leu162, Ala198, Ser199, Lys200, His201, Glu233, Val234, Ile235, Leu237, Glu240, Ala307. The potential binding sites of the tricin was found that, Trp58, Trp59, Tyr62, Val98, His101, Tyr151, Leu162, Asp197, Ala198, Lys200, His201, Glu233, Val234, Ile235, His299, Asp300. This proves that the effective binding sites are present in the selected flavonoid tricin when compared with the standard acarbose.
Binding energy of the individual compounds were calculated using the following formula,
Binding energy = A+B+C-D
where, A denotes final intermolecular energy + van der Walls energy (vdW) + hydrogen bonds + desolvation energy + electrostatic energy (kcal/mol), B denotes final total internal energy (kcal/mol), C denotes torsional free energy (kcal/mol), D denotes unbound system's energy (kcal/mol). Based on the docking studies, the alpha-amylase inhibitory activity of the selected compounds was found to be decreased in the order of tricin, hesperitin, vitexycarpin, chrysin, morin and biochanin (Table I). Further investigations on the above compounds and in vivo studies are necessary to develop potential chemical entities for the prevention and treatment of diabetes. On the basis of the above study, tricin and hesperitin possess potential alpha-amylase inhibitory binding sites similar to that of the standard (Figure 4). This may be attributed due to the differences in the position of the functional groups in the compounds.
Figure 4: alpha-Amylase inhibitory activity of flavonoids based on docking studies
Table I: Docking parameters of the selected flavonoids
| Compound | Binding energy (kcal/mol) | Inhibition Constant (µM, mM*) |
Intermolecular energy (kcal/mol) |
|---|---|---|---|
| Biochanin | -6.21 | 27.89 | -7.41 |
| Chrysin | -6.36 | 21.86 | -7.25 |
| Hesperitin | -7.19 | 5.35 | -8.68 |
| Morin | -6.35 | 21.99 | -8.14 |
| Tricin | -7.20 | 5.31 | -8.99 |
| Vitexycarpin | -6.56 | 15.61 | -8.65 |
| Acarbose | -2.94 | 6.98* | -9.50 |
Conclusion
These results indicate that from the selected flavonoids, tricin and hesperitin have better binding sites and interactions with alpha-amylase enzyme.
References
Bikadi Z, Hazai E. Application of the PM6 semiempirical method to modeling proteins enhances docking accuracy of AutoDock. J Cheminform. 2009; 1: 15-17.
Chang MW, Ayeni C, Breuer S. Virtual screening for HIV protease inhibitors: A comparison of AutoDock 4 and vina. PLOS ONE 2010; 5: 119-55.
Collignon B, Schulz R, Smith JC. Task-parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high-performance supercomputers. J Comput Chem. 2011; 32: 1202-09.
Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson A. Virtual screening with AutoDock: Theory and practice. J Exp Opin Drug Disc. 2010; 5: 597-607.
Formica JV, Regelson W. Review of the biology of quercetin and related bioflavonoids. Food Chem Toxicol. 1995; 33: 1061-80.
Groot H, Rauen U. Tissue injury by reactive oxygen species and the protective effects of flavonoids. Fundam Clin Pharmacol. 1998; 12: 249-55.
Gupta R, Gigras P, Mohapatra H, Goswami VK, Chauhan B. Microbial α-amylases: A biotechnological perspective. Process Biochem. 2003; 38: 1599-616.
Khairallah M, Khairallah RJ, Young ME. Sildenafil and cardiomyocyte-specific cGMP signaling prevent cardiomyopathic changes associated with dystrophin deficiency. Proc Nat Acad Sci. 2008; 105: 7028-33.
Konc J, Konc JT, Penca M, Janezic M. Binding-sites prediction assisting protein-protein docking. Acta Chim Solv. 2011; 58: 396-401.
Koppen H. Virtual screening: What does it give us? Curr Opin Drug Disc Dev. 2009; 12: 397-407.
Lamba HS, Bhargava CS, Thakur M, Bhargava S. αÂglucosidase and aldose reductase inhibitory activity in vitro and anti diabetic activity in vivo of Tribulus terrestris L. (dunal). Int J Pharm Pharmac Sci. 2011; 3: 270-72.
Li Y, Li C, Xu Q, Kang W. Antioxidant, α-glucosidase inhibitory activities in vitro and alloxan-induced diabetic rats protective effect of Indigofera stachyodes Lindl. root. J Med Plants Res. 2011; 5: 3321-28.
Madeswaran A, Umamaheswari M, Asokkumar K, Sivashanmugam T, Subhadradevi V, Jagannath P. Computational drug discovery of potential TAU protein kinase I inhibitors using in silico docking studies. Bangladesh J Pharmacol. 2013; 8: 131-35.
Madeswaran A, Umamaheswari M, Asokkumar K, Sivashanmugam T, Subhadradevi V, Jagannath P. In Silico docking studies of lipoxygenase inhibitory activity of comercially available flavonoids. Orient Pharm Exp Med. 2012; 12: 157-61.
Madeswaran A, Umamaheswari M, Asokkumar K, Sivashanmugam T, Subhadradevi V, Jagannath P. Discovery of potential aldose reductase inhibitors using in silico docking studies. J Comput Method Mol Design 2011; 1: 65-72.
Prakhov ND, Chernorudskiy LA, Gainullin RM. VSDocker: A tool for parallel high-throughput virtual screening using AutoDock on Windows-based computer clusters. Bioinformatics 2010; 26: 1374-75.
Sandeep G, Nagasree KP, Hanisha M, Kumar MMK. AUDocker LE: A GUI for virtual screening with AUTODOCK Vina. BMC Res Notes 2011; 4: 445-47.
Sarikaya E, Higassa T, Adachi M, Mikami B. Comparison of degradation abilities of α- and β-amylases on raw starch granules. Proc Biochem. 2000; 35: 711-15.
Seeliger D, Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Design 2010; 24: 417-24.
Vianna R, Brault A, Martineau LC, Couture R. In vivo anti-diabetic activity of the ethanolic crude extract of Sorbus decora C. K. Schneid. (Rosacea): A medicinal plant used by Canadian James Bay Cree Nations to treat symptoms related to diabetes. Evid Based Complement Altern Med. 2011; 1: 10-15.
Wolfenden R, Lu X, Young G. Spontaneous hydrolysis of glycosides. J. Am. Chem. Soc. 1998; 120: 6814-15.
