MOLECULAR DOCKING STUDIES OF N-(2-BENZOYLPHENYL)-L-TYROSINE DERIVATIVES WITH ANTI-DIABETIC ACTIVITY OF TYPE 2 DIABETES

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ABOUT AUTHORS:
Anuradha Sharma¹*, Vaibhav Walia², Monika Gahlawat^3
1Division of Pharma. Chemistry,² Division of Pharmacology,³ Division of Pharmaceutics,
G.V.M. College of Pharmacy,
Sonepat, Haryana, India
*anusarswat@gmail.com

ABSTRACT
Type 2 diabetes is one of the major life threatening diseases worldwide. These cases are progressing at an incremental rate every year and number of research works is going on to control the disease by targeting its enzymes or proteins. In modern drug designing, molecular docking is routinely used for understanding drug receptor interaction. In the present study molecular docking were performed on a diverse set of N-(2-benzoylphenyl)-L-tyrosine derivatives that demonstrate antidiabetic activity by stimulating peroxisome proliferator activated receptor- γ. The docking program in Glide dock justifies the correlation between the experimental values and the values derived computationally. Therefore, the dock analysis performed in Glide dock suggests the importance of evaluating the prediction accuracy of scoring functions adopted in various docking program.

REFERENCE ID: PHARMATUTOR-ART-2310

INTRODUCTION
Diabetes mellitus is one of the very common chronic diseases across the world and the number of diabetic patients is on the rise. The World Health Organization (WHO) estimates that about 200 million people all over the globe are suffering from diabetes and this figure is likely to be doubled by 2030. WHO says that about 80% of the deaths occur every year due to diabetes in middle-income countries ⁽¹⁾. Type 2 diabetes mellitus (T2DM) is a genetically heterogeneous, polygenic disease with a complex inheritance pattern and is caused by genetic predisposition and environmental factors ⁽²⁾ The disease is characterized by altered expression of many genes and their products in several tissue types ^(3,4). The recently published Indian council for medical research-India diabetes (ICMR-INDIAB) national study reported that there are 62.4 million people with type 2 diabetes (T2DM) and 77 million people with prediabetes in India ⁽⁵⁾. This will be increased to 100 million by 2030. T2DM predominantly affects older individuals in developed countries, while in developing nations like India; it is affecting the younger population in the prime of their working lives and thus poses an even greater threat to the health of these individuals ^(5, 6). Molecular 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⁽⁷⁾ Docking can be defined as an optimization problem, which would describe the ‘‘best-fit’’ orientation of a ligand that binds to a particular protein of interest. Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs ⁽⁸⁾.

Molecular docking is an efficient tool for investigating receptor-ligand interactions and virtual screening, which plays a key role in rational drug design especially when the crystal structure of a receptor or enzyme is available. Enzymes take a key role in the research of pharmaceutical industry, because they represent targets for the specific development of drugs. A number of docking programs are employed extensively in the pharmaceutical and biotechnology industries, of which the most widely used, appear to Gold, Glide, Autodock, FlexX.

Computational methods

Compound selection
Docking studies has been performed on a series of 79 N-(2-Benzoylphenyl)-L-tyrosine derivatives ^{(9, 10)} having PPAR-γ agonistic activity. PPARγ agonistic activity was expressed as pK_i-log of the concentration of test compound required to achieve an apparent Ki value according to the equation Ki = IC₅₀/(1 + [L]/K_d), where IC₅₀ is the concentration of test compound required to inhibit 50% of the specific binding of the radioligand, [L] is the concentration of the radioligand used, and K_d is the dissociation constant for the radioligand at the receptor. Basic structure of the all analogues is shown in the figure 1and the various substituents are enlisted in the Table 1.

Ligand preparation
The LigPrep process consists of a series of steps that perform conversions, apply corrections to the structures, generate variations on the structures, eliminate unwanted structures, and optimize the structures.The molecule were built using Maestro 9.0 and converted to 3D structure from the 2D structure using Ligprep version 5.5 ⁽¹¹⁾. LigPrep is a robust collection of tool designed to prepare high quality, all-atom 3D structure for large number of drug-like molecule, starting with the 2D or 3D structure in SD or Maestro format. The resulting structures are saved in maestro format. The simplest use of Ligprep produces a single, low-energy, 3D structure with correct chiralities for each successfully proposed input structure. While performing this step, chiralities were determined from 3D structure and original states of ionization were retained ⁽¹¹⁾.   

Protein Preparation
PPAR-γ (PDB ID 3ET0, 2PRG) X-ray structures were accessed from the protein data bank (PDB). The protein structures with polar hydrogen were prepared using the protein preparation wizard in maestro (12). In this step, bond order were assigned, all hydrogen in the structure were added, and the bond to the metal are deleted and adjust the formal charge on the metal & the neighbouring atoms and deleting water that were more than the 5Å specified distance. PPAR were heterodimerize and we have taken all the monomer. With generate Het state option predicted ionization and tautomeric state of the het group at pH 7 was done. The next stage of the protein preparation was to optimize the hydrogen bond network by reorienting hydroxyl group, water molecules, and amide groups of Asn and Gln, and selecting appropriates states and orientation of the rings in the residues. The final step in the preparation process is to refine the structure, with a restrained minimization. Their task is initiated in the impref minimization with the 0.3Å RMSD for the minimization OPLS_2001 force field.

Receptor grid generation
Glide searches for favourable interaction between one or more ligand molecule and a receptor molecule, usually a protein. The shape and properties of the receptor are the represented on a grid by several different sets of field that provide progressively more accurate scoring of the ligand poses. For receptors that adopt more than one conformation on binding, grids are prepared for each conformation, to ensure that possible actives are not missed. Ligand molecule is picked so it can be excluded from the grid generation with vanderwaal radius scaling 1.00 and partial charge cut off 0.25. Grids were generated using Glide version 5.5following the standard procedure recommended by Schrodinger. The docked pose discussed in it were not necessarily the highest scoring, but were selected as the highest scoring pose with reasonable conformation and binding mode as judge by models.

Docking
Glide ligand docking jobs require a set of previously calculated receptor grids and one or more ligand structures. Ligands must satisfy following conditions i.e. they must be three-dimensional (3D), they must have realistic bond lengths and bond angles, and they must each consist of a single molecule that has no covalent bonds to the receptor. The process of docking is repeated until a constant value of docking score is reached. This takes about 12,000–18,000 generation. The final results are parameterized in terms of docking score in kcal/mol.

FIG 1: BASIC STRUCTURE OFN-(2-BENZOYLPHENYL)-L-TYROSINES.

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TABLE 1: VARIOUS SUBSTITUTENTS ATTACHED TO BASIC STRUCTURE OFN-(2-BENZOYLPHENYL)-L-TYROSINES.

RESULTS AND DISCUSSION
Glide dock is used to study the docking molecules within the active site region of protein 3ETO and number of H-bond and amino acid involved in H-bond were determined. Docking studies may help elucidating the mechanism of PPARγ receptor-ligand interactions. Key feature of the modeling results include logical interaction of the ligand with the putative binding site of the receptor. The binding pocket within the PPARγ receptor is formed by Glu 295, Arg 288, Ser 302, Asp 381, Leu330 and Leu 333. Some of the newly designed molecules have glide score more than -7.029, which is the glide score of Pioglitazone. Hence further proof was provided by plotting a graph between experimental values and glide score, where it is clear that they represented a correlation of 0.634. Glide score and number of hydrogen bond interaction and hydrogen bond length of the docked ligand are shown in the Table 2. The docking study reveals following information with respect to N-(2-benzoylphenyl)-l-tyrosines derivatives.

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TABLE 2: GLIDE SCORE, NUMBER OF HYDROGEN BOND AND AMINO ACIDS INVOLVED IN HYDROGEN BOND INTERACTION