DOCKING STUDIES OF BENZIMIDAZOLE DERIVATIVES USING HEX 8.0

DOCKING STUDIES OF BENZIMIDAZOLE DERIVATIVES USING HEX 8.0

M. Sai Harika ^*, T. Raj Kumar and L. Siva Sankar Reddy

Department of Pharmaceutical Chemistry, Creative Educational Society’s College of Pharmacy, Chinnatekur, Kurnool, Andhra Pradesh, India.

ABSTRACT: N-mannich bases of benzimidazole derivatives can work against convulsions. Docking used for virtual screening of database and for the prediction of the strongest binders based on various scoring functions. Docking studies were carried out on different benzimidazole derivaties for better anticonvulsant activity which is important for the development of new class of inhibitors. Protein–ligand interaction plays an important role in structural based drug design. In our research we selected different receptors. The receptors were docked with different imidazole derivatives and the energy values were obtained. Our study reveals that highest minimal energy values were observed with receptors like 4NF8, 4JWY, 4JWX, 3QEK, 3OEK, 3OEL, 3OEM, 3OEN. The results suggests that N-mannich bases of benzimidazole derivatives might be a potential targets of NMDA receptors for effective anticonvulsant activity.

Anticonvulsant activity, Benzimidazole derivatives, Hex, Protein-ligand interaction

INTRODUCTION: The advent of powerful and inexpensive computers has revolutionized science and medicine. Today, drug design methods are widely used in both industrial and academic environments. It has become popular to carry out in silico screening of drug-receptor candidate interations, known as virtual high-throughput screening (vHTS), for future development. The drug-receptor fit and predicted physicochemical properties are used to score and rank compounds according to penalty functions and information filters like molecular weight, number of hydrogen bonds, hydrophobicity, etc. Although medicinal chemists are aware of absorption, distribution, metabolism, elimination, and toxicity (ADMET or ADME/Tox), in recent years, a much more focused approach address these issues in the early design stages. Many of the predictive ADME models use quantitative structure activity relationships (QSAR).

In general, understanding what chemical space descriptors are critical for drug-like molecules helps provide insight into design of chemical libraries for biological evaluation.

With the aid of molecular modeling software, pharmaceutical scientists can modify the structural features of a potential drug candidate in silico and make predictions about its physicochemical properties prior to laboratory synthesis. Crystallographic information about the receptor has allowed the scientists to use structure-based drug design approaches with tangible benefits. Given the difficulty in preparing organic compounds, one can immediately appreciate the power that computer- based methods offer.

MATERIALS AND METHODS: There are a range of software packages available for molecular docking like Auto Dock, Hex, Schrodinger GLIDE etc.

For our present study we used bioinformatics tools, biological data base PDB (protein data bank) and software like Chem Draw Ultra 12.0, OSIRIS Data Warrior, preADMET, PASS Online, Molinspiration,   and Hex 8.0 docking.

Chem Draw Ultra 12.0 is a powerful yet to use tool for producing a nearly unlimited variety of biological and chemical drawings. You can create your own drawings or use those provided in the library of available templates. Having completed a drawing, you can export it to a desktop publishing program, post it on a Web page, or store it in a database. You can calculate predicted values of selected physical and thermodynamic properties for structures of up to 100 atoms.

OSIRIS Data Warrior ¹ combines dynamic graphical views and interactive row filtering with chemical intelligence.  Data Warrior supports the enumeration of combinatorial libraries as the creation of evolutionary libraries. Compounds can be clustered and diverse subsets can be picked. Calculated compound similarities can be used for multidimensional scaling methods, e.g. Kohonen nets. Physicochemical properties can be calculated, structure activity relationship tables can be created and activity cliffs be visualized. Today, OSIRIS is a vital backbone that enables the entire research process. One component, Data Warrior, specializes as data visualization and analysis tool for chemical and biological data.

PASS² (Prediction of Activity Spectra for Substances) is a software product designed as a tool for evaluating the general biological potential of an organic drug-like molecule. PASS provides simultaneous predictions of many types of biological activity based on the structure of organic compounds. Thus, PASS can be used to estimate the biological activity profiles for virtual molecules, prior to their chemical synthesis and biological testing. PASS provides reasonable estimates of structure-activity relationships despite of incompleteness (or some errors in data) of PASS training set. It provides a possibility of simultaneous prediction of about 3,600 kinds of biological activity for drug-like organic compound. Input data represents a structural formula of a compound in MOLfile format. The output file represents a list of activities with two probabilities Pa (probability to be active) and Pi (probability to be inactive).

Pa (probability "to be active") estimates the chance that the studied compound is belonging to the sub-class of active compounds (resembles the structures of molecules, which are the most typical in a sub-set of "actives" in PASS training set).

Pi (probability "to be inactive") estimates the chance that the studied compound is belonging to the sub-class of inactive compounds (resembles the structures of molecules, which are the most typical in a sub-set of "inactives" in PASS training set).

PreADMET ³ is a web-based application for predicting ADME data and building drug-like library using in silico method.  PreADMET consists of four main parts as following:

1. Molecular Descriptor Calculation: The ADME/Tox properties are closely related to physico-chemical descriptors such as lipophilicity (logP), molecular weight, polar surface area, and water solubility. The TOPOMOL module calculates more than 2500 molecular descriptors including constitutional, topological, electrostatic, physico-chemical, and geometrical descriptors for ADME/Tox prediction from 2D and 3D chemical structure: TOPOMOL reads MDL mol or sd files and provides a rapid means to calculate all 2D descriptors of 1,000,000 compounds in less than 1 hour using Pentium IV 3.4GHz PC.

2. Drug-likeness Prediction: The most well known rule relating the chemical structures to their biological activities is Lipinski’s rule, it is called the ‘rule of five’. Another well known rule is the Lead-like rule, i.e. starting from a quantitative survey based upon 18 lead and drug pairs of structure. PreADMET contains drug-likeness prediction module based on these rules. Also, it is possible to use several drug-like rules that several researchers defined drug-like characteristics of drug DB such as CMC (covering more than 80% of the compounds), WDI (world drug index), and MDDR DB.

3. ADME Prediction: Numerous in vitro methods have been used in the drug selection process for assessing the intestinal absorption of drug candidates. Among them, Caco2-cell model and MDCK (Madin-Darby canine kidney) cell model has been recommended as a reliable in vitro model for the prediction of oral drug absorption. In absorption, this module provides prediction models for in vitro Caco2-cell and MDCK cell assay. Additionally, in silico HIA (human intestinal absorption) model and skin permeability model can predict and identify potential drug for oral delivery and transdermal delivery. In distribution, BBB (blood brain barrier) penetration can give information of therapeutic drug in the central nervous system (CNS), plasma protein binding model in its disposition and efficacy. In order to build these QSAR models, genetic functional approximation is used to select relevant descriptors from all 2D descriptors that calculated by Topomol module, followed by Resilient back-propagation (Rprop) neural network to develop successful nonlinear model.

4. Toxicity prediction: In silico toxicity prediction will have more and more importance in early drug discovery since 30% of drug candidates fail owing to these issues.

Ligands:

TABLE 1: THE STRUCTURES WERE DRAWN IN CHEM DRAW ULTRA 12.0 AND FURTHER FOR DOCKING STUDIES

S.no	Molecular Formula	Ligand and its IUPAC Name
P1	C18H19N3O
P2	C18H20N4
P3	C19H21N3
P4	C18H20N4
P5	C18H19N5
P6	C17H18N4O
P7	C18H19N3O2
P8	C19H21N3O
P9	C18H20N4O
P10	C18H19N4Cl
P11	C19H20N4Cl
P12	C18H18N3OCl
P13	C18H20N4O
P14	C19H22N4
P15	C18H21N5

Docking studies: Hex 8.0.0 protein docking uses spherical polar Fourier Correlations ⁴. Hex is an interactive molecular graphics program for calculating and displaying feasible docking modes of pairs of protein and DNA molecules. Hex can also calculate protein-ligand docking, assuming the ligand is rigid, and it can superpose pairs of molecules using only knowledge of their 3D shapes. It is the first protein docking program to be able to use modern graphics processor units (GPUs) to accelerate the calculations.

The PDB (Protein Data Bank) is the single world wide archive of Structural data of Biological macromolecules, established in Brookhaven National Laboratories (BNL) in 1971 ⁵. It contains Structural information of the macromolecules determined by X-ray crystallographic, NMR methods etc.

For docking studies Ligand structures and targets are converted to pdb file using Open Babel Graphical User Interface. Generally, most actions cause an immediate effect on the display so that once you’ve loaded a protein and the ligand. Docking allows the scientist to virtually screen a database of compounds and predict the strongest binders based on various scoring functions. It explores ways in which two molecules, such as drugs and an enzyme NMDA receptor fit together and dock to each other well, like pieces of a three-dimensional jigsaw puzzle. The molecules binding to a receptor, inhibit its function, and thus act as drug.

The collection of ligands and receptor complexes was identified via docking and their relative stabilities were evaluated using molecular dynamics and their binding affinities, using free energy simulations ⁶. The parameters used for the docking process were

• Correlation type – Shape only
• FFT Mode – 3D fast lite
• Grid Dimension – 0.6
• Receptor range – 180
• Ligand Range – 180
• Twist range – 360
• Distance Range – 40

The drug and its analogues were docked with the receptor using the above parameters.

TABLE 2:  ALL THE 15 TARGETS OF NMDA RECEPTORS

5FXG	5CC2	4NF4
4NF5	4NF6	4NF8
4JWY	4JWX	3QEK
3QEL	3QEM	3OEK
3OEL	3OEM	3OEN

RESULTS AND DISCUSSION:

TABLE 3: PHYSICOCHEMICAL PROPERTIES

log P: Octanol-water coefficient, TPSA: Polar surface area, MW: Molecular weight, nON: No. of  hydrogen bond acceptors (O & N atoms), nOHNH: No. of  hydrogen bond donars (OH & NH groups), nviolations: No. of rule of 5 violation , nrtob: No. of rotatable bonds, Volume: Molecular volume, Among 15 compounds except P11, all follows Lipinski’s rule.

Lipinski's rule of five also known as the Pfizer's rule of five or simply the Rule of five (RO5) is a rule of thumb to evaluate drug likeness or determine if a chemical compound with a certain pharmacological or biological activity has properties that would make it a likely orally active drug in humans. The rule was formulated by Christopher A. Lipinski in 1997, based on the observation that most medication drugs are relatively small and lipophilic molecules. P11 which is having a logp > 5 violated this Lipinski’s rule which means absorption and bioavailability are likely to be poor (this is for oral drugs only).

TABLE 4: ADME PROPERTIES OF P1 – P8 LIGANDS

TABLE 5: ADME PROPERTIES OF P9 – P15 LIGANDS

TABLE 6: SOLUBILITY, DRUGLIKENESS AND TOXICITY PROPERTIES ACCORDING TO OSIRIS DATA WARRIOR

These toxicity studies are again conformed by mcule studies where it showed all 15 compounds are non toxic.

TABLE 7: ACTIVITY PREDICTION USING PASS ONLINE

Docking studies: The results of the docking pair are as follows

TABLE 8: DOCKING WITH FOUR TARGETS (5FXG, 5CC2, 4NF4, 4NF5) OF NMDA RECEPTORS

TABLE 9: DOCKING WITH FOUR TARGETS (4NF6, 4NF8, 4JWY, 4JWX) OF NMDA RECEPTORS

TABLE 10: DOCKING WITH FOUR TARGETS (3QEK, 3QEL, 3QEM, 3OEK) OF NMDA RECEPTORS

TABLE 11: DOCKING WITH FOUR TARGETS (3OEL, 3OEM, 3OEN) OF NMDA RECEPTORS

CONCLUSION: The Protein-Ligand interaction plays a significant role in structural based drug designing. In the present work we have taken the NMDA receptors and identified the drugs that were used against convulsions. When the receptors like 4NF8, 4JWY, 4JWX, 3QEK, 3OEK, 3OEL, 3OEM, 3OEN docked with molecules we observed a minimal energy values. From this we can conclude that NMDA receptors can be used as drug targets against convulsions. In future research work the ADME/T (Absorption, Distribution, Metabolism, Excretion/Toxicity) properties of these compounds can be calculated using the commercial ADME/T tools available thus reducing the time and cost in drug discovery process.

ACKNOWLEDGEMENT: The corresponding author is grateful to T. Rajkumar and L. Siva Sankar Reddy, Head of Department, Pharmaceutical Chemistry, who are encouraged me to do docking studies.

DECLARATION: There is no conflict of interest.

REFERENCES:

1. http://www.openmolecules.org/datawarrior/
2. http://www.pharmaexpert.ru/passonline/
3. https://preadmet.bmdrc.kr/
4. David W. Rithcie: Evaluation of Protein Docking Predictions using Hex 3.1 in CAPRI rounds 1 and 2. Proteins: Structure, Fucntion and Bioinformatics 2003; 52 (1):98-106.
5. Helen M. Berman, John westbrook et al., The Protein Data Bank. Nucleic Acids Research 2000; 28(1):235-242.
6. T. Rajkumar, P. Shirisha, S. Shah Faisal, Shaik Anwar Hussain, T. Nagaiah & Ayesha Sulthana: Prediction of Molecular Properties, Bioactivity & Docking of Novel Hydrazines. International Journal of Trends in Pharmacy and Life Sciences 2016; 2(1):757-768.
   

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   How to cite this article:

   Harika MS, Kumar TR and Reddy LSS: Docking studies of benzimidazole derivatives using hex. Int J Pharm Sci Res 2017; 8(4): 1677-88.doi: 10.13040/IJPSR.0975-8232.8(4).1677-88.

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