Computational drug discovery of potential TAU protein kinase I inhibitors using in silico docking studies

  • Arumugam Madeswaran Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India.
  • Muthuswamy Umamaheswari Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India.
  • Kuppusamy Asokkumar Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India
  • Thirumalaisamy Sivashanmugam Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India
  • Varadharajan Subhadradevi
  • Puliyath Jagannath Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India.
  • Puliyath Jagannath Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India.
Keywords: Alzheimer's disease, Binding energy, Flavonoids, Inhibition constant, tau protein
DOI: 10.3329/bjp.v8i2.13886

Abstract

The objective of the current study is to evaluate the tau protein kinase I inhibitory activity of flavonoids using in silico docking studies. In silico docking studies were carried out using AutoDock 4.2, based on the Lamarckian genetic algorithm principle. Memantine, a known neuroreceptor antagonist is currently used in the treatment of Alzheimer’s disease. The results showed that all the selected flavonoids showed binding energy ranging between -7.1 to -4.9 kcal/mol when compared with that of the standard (-5.9 kcal/mol). Inhibition constant (6.6 to 280.1 µM) and intermolecular energy (-9.5 to -6.6 kcal/mol) of the ligands also coincide with the binding energy. These molecular docking analyses could lead to the further development of potent tau protein kinase I inhibitors for the treatment of Alzheimer’s disease. Further investigations on the above compounds and in vivo studies are necessary to develop potential chemical entities for the prevention and treatment of Alzheimer’s disease.

Introduction

Rational drug design is the inventive process of finding new medications based on the knowledge of the biological target. Drug research aims at the development of a novel therapeutic agent which is done by designing molecules that are complementary in shape and charge to the biomolecular target with which they interact and bind. Drug discovery and development is an intense, lengthy and an interdisciplinary endeavor. It is a linear, consecutive process that starts with target and lead discovery, followed by lead optimization and pre-clinical in vitro and in vivo studies to determine if such compounds satisfy a number of preset criteria for initiating clinical development (Ekins et al., 2005).      

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). 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 (Madeswaran et al., 2012).

Tau proteins belong to the family of microtubule-associated proteins. Majorly expressed in neurons where it has an important role in the assembly of tubulin monomers into microtubules to make up the neuronal microtubules network (Billingsley and Kincaid, 1997). Tau is a phosphoprotein with 79 potential serine (Ser) and threonine (Thr) phosphorylation sites on the longest tau isoform. Phosphorylation has been reported on approximately 30 of these sites in normal tau proteins. Microtubules are involved in maintaining the cell shape and serve as tracks for axonal transport (Shin et al., 1991).

Tau proteins are the major constituents of intraneuronal and glial fibrillar lesions described in Alzheimer's disease and numerous neurodegenerative disorders referred to as 'tauopathies'. A direct correlation has been recognized between the progressive involvement of the neocortical areas and the increasing severity of dementia, suggesting that pathological tau proteins are reliable marker of the neurodegenerative process (Galluzzo et al., 2006; Buee et al., 2000). Memantine, a known neuro-receptor antagonist is currently used in the treatment of Alzheimer's disease. Therefore, memantine is used as a standard for the current docking study.

Flavonoids are a large group of non-nutrient compounds naturally obtained from plants as part of their protective mechanisms against stresses of various origins. They emerged from being measured an agricultural oddity only after it was monitored that these compounds possess a potential defensive function against several human degenerative diseases (Budakoti et al., 2009). Flavonoids possess various biological activities like, antiamoebic activity, anti-inflammatory, anticoagulant, anti-cancer, antioxidants and antispasmodic (Aung et al., 2011; Beker et al., 2011; Chassany et al., 2007; Formica and Regelson, 1995).

Pharmacological treatments for neurodegenerative disorders are symptomatic and do not change the progression of the neurodegenerative disorders. These treatments are less satisfactory and may direct to serious side effects. Hence, the goal of current research is to develop potential compounds that could inhibit the tau protein and thereby it can be used for the treatment for neurodegenerative diseases.

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 AutoDock 4.2 was downloaded from www.scripps.edu, Discovery studio visualizer 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

We employed the Lamarckian genetic algorithm (LGA) for 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.

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). Binary complex structure of tau protein kinase I enzyme (1J1C) was downloaded from the Brookhaeven protein data bank (Figure 1).

Figure 1: The refined structure of tau protein kinase I enzyme

The flavonoid ligands like acacatechin, catechin, galangin, scopoletin, silbinin and memantine (Figure 2)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 acacatechin, 2 catechin, 3 galangin, 4 scopoletin, 5 silbinin, and 6 memantine)

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 various parameters of the ligands like molecular formula, molecular weight, aromatic carbons, rotatable bonds and no. of torsions were tabulated in Table I.

Table I: Ligand parameters

Ligands Molecular formula Molecular weight Aromatic carbons Rotatable bonds No. of torsions
Acacatechin C15H14O6 290.277 12 6 6
Catechin C15H14O6 290.277 12 6 6
Galangin C15H12O5 272.261 12 4 4
Scopoletin C10H8O4 192.174 9 2 2
Silbinin C23H18O10 454.397 18 8 8
Memantine C12H21N 179.300 19 1 1

The preparation of the target protein 1J1C 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. Gas teiger 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 × 277 × 277 Å with 0.6 Å 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. The energy of interaction of each atom in the ligand was encountered.

We have selected important docking parameters for the LGA as follows: population size of 150 individuals, 2.5 million energy evaluations, maximum of 27000 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 (Madeswaran et al., 2013).

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, 10 best poses were generated and scored using AutoDock 4.2 scoring functions (Umamaheswari et al., 2011).

Result and Discussion

In silico docking study, was carried out to identify the inhibiting potential of selected flavonoids against tau protein kinase I enzyme. In this study 5 different flavornoids were selected for the in silico docking studies. The docking studies were performed by the use of AutoDock 4.2. 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).

Hyperphosphorylation of the tau protein result in the self-assembly of tangles of paired helical filaments and straight filaments, which are involved in the pathogenesis of Alzheimer's disease and other tauopathies (Alonso et al., 2001). Recent research suggests that tau protein may be released extracellularly by an exosome based mechanism in Alzheimer's disease. Some aspects of how the disease functions also suggest that it has some similarities to prion proteins (Hall and Patuto, 2012).

Flavonoids are the excellent antioxidants when compared to other compounds. Extracts from onion and different flavonoids activate the cellular antioxidant system. Analysis of the receptor/ligand complex models generated after successful docking of the flavornoids was based on the various parameters such as hydrogen bond interactions, binding energy, inhibition constant and orientation of the docked compound within the active site (Madeswaran et al., 2013).

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)                    

Flavonoids showed binding energy ranging between -7.1 to -4.9 kcal/mol (Table II). Silbinin showed better binding energy -7.1 kcal/mol than the standard memantine (-5.9 kcal/mol). All the selected flavonoids had showed binding energy compared to that of standard. This proves that flavonoids consist of potential tau protein kinase I inhibitory binding sites similar to that of the standard. In addition, two other parameters like inhibition constant (Ki) and intermolecular energy were also determined. Inhibition constant is directly proportional to binding energy. Flavonoids showed inhibition constant ranging from 6.6 to 280.1 µM (Table III). Silbinin showed excellent inhibition constant 6.6 µM than the standard memantine (48.2 µM). All the selected compounds had lesser inhibition constant when compared to the standard. Thus, the potential tau protein kinase I inhibitory activity of the flavonoids were compared with the memantine.

Table II:Binding energies of the compounds based on their rank

Compounds Inter molecular energies of the compounds based on their rank (kcal/mol)
1 2 3 4 5 6 7 8 9 10
Acacatechin -6.6 -6.6 -6.3 -5.9 -6.2 -6.2 -6.1 -5.9 -5.7 -5.6
Catechin -7.6 -7.6 -7.5 -7.3 -6.9 -7.2 -7.2 -6.8 -6.4 -6.3
Galangin -7.7 -7.2 -6.9 -7.1 -6.8 -6.7 -6.6 -6.4 -6.3 -6.2
Scopoletin -6.0 -6.0 -5.9 -5.9 -5.9 -5.8 -5.8 -5.4 -5.3 -5.2
Silbinin -9.5 -8.6 -8.3 -7.6 -7.5 -7.2 -7.2 -7.1 -6.9 -6.6
Memantine -6.2 -6.0 -6.0 -6.0 -5.9 -5.9 -5.9 -5.9 -5.9 -5.8

Table III: Inhibition constant of the compounds based on their rank

Compounds Inhibition constant of the compounds based on their rank (µM, mMa)
1 2 3 4 5 6 7 8 9 10
Acacatechin 280.1 302.5 533.7 1.0a 566.2 628.8 739.0 919.0 1.4a 1.6a
Catechin 51.1 52.4 67.2 97.2 190.3 100.6 116.0 229.1 419.3 488.0
Galangin 17.4 43.2 67.4 43.6 84.8 87.1 111.0 147.3 169.9 217.3
Scopoletin 104.8 105.1 124.3 129.4 132.0 163.2 164.1 283.0 338.3 394.0
Silbinin 6.6 29.4 48.3 156.3 181.1 296.7 312.2 329.2 473.3 797.7
Memantine 48.2 64.2 66.4 69.0 85.4 76.0 79.2 79.5 84.5 96.5

Intermolecular energy is also directly proportional to binding energy. Flavonoids showed intermolecular energy ranging between -9. to -6.6 kcal/mol which was lesser when compared to the standard (-6.2 kcal/mol; (Table IV). We found a decrease in intermolecular energy of all the selected compounds with a simultaneous decrease in the binding energy. This result further proved the tau protein kinase I inhibitory activity of all the selected flavonoids.

Table IV: Intermolecular energies of the compounds based on their rank

Compounds Binding energies of the compounds based on their rank (kcal/mol)
1 2 3 4 5 6 7 8 9 10
Acacatechin -4.9 -4.8 -4.5 -4.1 -4.4 -4.4 -4.3 -4.1 -3.9 -3.8
Catechin -5.9 -5.8 -5.7 -5.5 -5.1 -5.5 -5.4 -5.0 -4.6 -4.5
Galangin -6.5 -6.0 -5.7 -6.0 -5.6 -5.5 -5.4 -5.2 -5.1 -5.0
Scopoletin -5.4 -5.4 -5.3 -5.3 -5.3 -5.2 -5.2 -4.8 -4.7 -4.6
Silbinin -7.1 -6.2 -5.9 -5.2 -5.1 -4.8 -4.8 -4.8 -4.5 -4.2
Memantine -5.9 -5.7 -5.7 -5.7 -5.6 -5.6 -5.6 -5.6 -5.6 -5.5

Based on the docking studies, thetau protein kinase I inhibitory activity of the selected compounds was found to be decreased in the order of silbinin, galangin, memantine, galangin, scopoletin and acacatechin. On the basis of the above study, silbinin and galangin possess potential tau protein kinase I inhibitory binding sites similar to that of the standard. This may be attributed due to the differences in the position of the  functional groups in the compounds.

Conclusion

These results clearly indicate that from the selected flavonoids, silbinin and galangin have better binding sites and interactions with tau protein kinase I enzyme and further investigations are necessary to develop potential chemical entity for the prevention and treatment of Alzheimer's disease.

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Published
2019-12-09

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