Molecular Docking of Quinoline-Based Derivatives with Colon & Breast Cancer Proteins And their ADME Study

Manoj Kumar Mahto^{1, 3,*,} Khunza Meraj²,  Karaneh Eftekhari², Zeinab Motahari Nejad², Gunduluru Poojitha², Matcha Bhaskar³

¹Department of Biotechnology, Acharya Nagarjuna University, Guntur, AP, India

²Aravinda Biosolutions, Hyderabad, AP, India

³ Division of Animal Biotechnology, Department of Zoology, Sri Venkateswara University, Tirupati, AP, India

*corresponding author’s email:manoj4bi@gmail.com, cell: +91-9885928832

 

ABSTRACT

Many studies have shown that natural, semi-synthetic and synthetic biologically active have demonstrated their high ability to elicit anti-proliferative and antitumor activity. The aim of this study was to carry out docking and post docking analysis  of already synthesized quinoline based inhibitors using computational tools and techniques. In the previous study these derivates were assessed for their invitro activity on human colon cancer cell line HT29 and breast cancer cell line MDA-MB231, therefore our goal was to target several oncoprotein which are common in both colon and breast cancers. Interestingly the result of docking was found to match with the previous invitro study where the molecule 4e is found be most active derivative against both tested cell lines. The derivative 4e is among the five best scoring ligands with all oncoproteins which are used as target with significant XP G scores. The ADME and toxicity study for evaluation of molecular properties and descriptors computed in Qikprop 3.4 for all the quinoline based derivatives is within the range or are the recommended values to become a potent inhibitor.

Keywords: Cancer, Quinoline, Schrödinger 2011, QikProp 3.4, Docking,  ADME, G Scores.

 

INTRODUCTION

Cancer results from uncontrolled growth of abnormal cells in the body and is the major cause of death worldwide. Cancer starts from normal cells which are our body’s building blocks. Normal cells divide and grow in order to maintain the cell population equilibrium and to balance cell death. Cancer occurs when unbounded growth of cells in the body happens fast. It can also occur when cells lose their ability to die. Cancer can develop in almost any organ or tissue. There are many different kinds of cancers, such as lung, colon, breast, skin, bones, or nerve tissue. There are many known causes of cancers like exposure to chemicals, drinking excess alcohol, excessive sunlight exposure, and genetic differences, to name a few [1]. The drug Camptothecin is considered a potential inhibitor for the nuclear enzyme which plays an important role in solving topological problems arising during DNA replication. Significantly increased levels of topoisomerase I and also expression of topoisomerase IIα enzymes is found in advanced stages of human colon adenocarcinoma and in xenografts of colon cancer. [2,3]. Topoisomerase II alpha (topo IIalpha) plays a key role in DNA replication and is a target for multiple chemotherapeutic agents in breast cancer. Studies show a significant association of increased expression of topo IIα  enzyme in primary breast cancers. This may indicate a potential role of topo II as a prognostic marker in breast cancer [4, 5]. Carbonic anhydrase isoenzymes I & II are present in colorectal cancer but in most of the cases isoenzyme I is not expressed. Therefore in our docking study we have used carbonic anhydrase isoenzyme II(PDB ID: 3KS3) as one of the targets [6,7]. Quinoline based derivatives are potential inhibitors of carbonic anhydrase isoenzymes and play important role in treatment of breast cancer.[8] A polymorphism in the manganese superoxide dismutase (MnSOD) gene, Ala-9Val, has been examined in association with breast cancer risk in several epidemiologic studies[9]. Also study has shown higher superoxide dismutase activity in tissues of human colorectal cancers. Quinoline derivatives inhibited the growth of cancer cells through their effect as free-radical regulators by increasing the activity of superoxide dismutase activity [10,11]. Functional studies revealed that chronic inflammation leads to an up-regulation of VEGFR II on intestinal epithelial cells. It is found that VEGFR-signaling acts as a direct growth factor for tumor cells in colitis associated cancer providing a molecular link between inflammation and the development of colon cancer[12].

 

In silico molecular modeling and docking studies have considerably increased to predict potential inhibitors (drugs) for the treatment of several diseases. Homology modeling, Docking, quantitative structure activity relationships(3D QSAR), virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools are becoming increasingly important In silico approaches in new drug design and have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. The computational process by which we can assess the complementary aspects between a ligand and a receptor binding site has been explored with the design of specifically dedicated computational technique like docking. Early docking methods were based uniquely on assessing the shape complementarity between a pocket in the 3D structure of a protein and low energy conformers of a ligand. [13],[14,[15] Today there is a considerable increment in the application of in silico molecular modeling and docking studies to predict potential inhibitors (drugs) for the treatment of several diseases. [16] Further computational prediction of pharmacokinetic parameters like Absorption, Distribution, Metabolism and Excretion (ADME) & toxicity studies have become increasingly important in drug selection and promotion process and are promising tools for early screening of potential drug candidates [17].

 

In this study, we used 22 quinoline based derivatives which were retrieved from previous study on quinoline compounds see reference paper titled “Synthesis and in vitro anti-proliferative effect of novel quinoline-based potential anticancer agents”. [18] The structures were designed and docking study was carried out and these results have been confirmed by the G Scores obtained from Grid – based Ligand Docking with Energetics (GLIDE) of Schrödinger 2011 [19, 20].

MATERIALS & METHODS

Selection of target proteins

Following seven cancer proteins with their resolutions and ligand interaction diagram as retrieved from the data bank (PDB) (www.rcsb.org/pdb)are targeted in this study (see table 1). Structural and active site studies of the protein were done by using CASTP (Computed Atlas of Surface Topography of Proteins).

 

Molecules information

A series of 22 quinoline based inhibitors are retrieved from the available article on “Synthesis and in vitro anti-proliferative effect of novel quinoline-based potential anticancer agents”. It has been concluded from their work that quinoline based compounds are potent inhibitor for several cancer proteins. Among the 22 compounds that we have subjected for docking and post docking analysis, six are quinoline based anti cancer compounds and 16 are new synthesized quinoline derivatives. 

 Figure 1: Structure of quinoline

Protein Preparation & Active site prediction

Prior to docking, it is important to identify the binding site in the target protein. The predicted ligand binding site residues in each protein was predicted from the ligand interaction diagram. The PDB structure files of the proteins are downloaded in PDB format. The processing, optimization and minimization of these proteins is carried out in the protein preparation wizard of the Schrodinger 2011 by applying the OPLS_2005 force field and active site amino acid in all proteins and is specified in the receptor grid generation.

 

Table 1: Proteins used as target in the docking study 

 

Molecules Preparation

The 22 compounds are drawn in ChemBioDraw Ultra 12.0 software and obtained in .mol format and used as input structures for processing in LigPrep 2.5 for geometry optimization and energy minimization which is run from maestro9.2 of Schrodinger2011. Among many conformers obtained in the LigPrep, the conformer with least potential energy(shown in table 2) are subjected to Impact minimization module under the applied OPLS_2005 force field. The significance of impact minimization is to observe the Lennard Jones Energy, which should be in negative 21. The Impact minimized molecules are further subjected for the docking study.

 

Table 2: The LigPrep 2.5 result showing conformers with least potential energy

Docking

Docking studies are computational techniques for exploration of possible binding mode of a substrate to a given receptor, enzyme or other binding site. The docking studies of the above 22 drug molecules with the seven different oncoproteins involved in breast and colon cancerare subjected to docking using the Glide of Maestro 9.2 run from Schrodinger 2011 software. All 22 molecules are docked with negative XP G Score and were ranked by the interaction energy. Overall, the Vander Waals energy contributed most to the interaction energy, but the electrostatic energy showed the greatest variation and was therefore the major factor for the ranking of molecules. Docking result revealed that all the molecules were docked efficiently as it is evident from the XP Glide scores (see table: 3). The top five molecules showing highest XP G scores with each protein are highlighted in blue color and the G scores of molecule 4e is found to be among the top five in all the proteins and is highlighted in green. 

 

ADME & Toxicity Studies

The ability to detect problematic candidates early can dramatically reduce the amount of wasted time and resources, and streamline the overall drug development process. ADME (Absorption, Distribution, Metabolism and Excretion) studies are carried out in the QikProp 3.4 for assessing the disposition and potential toxicity of ligand with in an organism. It has published models that we computed to analyze blood brain barrierlevel, Absorption level, solubility level, hepatotoxicity level, CYP2D6 level and plasma protein binding level of all the molecules with ADME and their toxicity risks. The overall pharmacological properties (Tables 5(A) & 5(B)) of these molecules justify that the molecules are biologically active without any toxic functional groups. 

 

RESULTS

The XP G scores of the 22 derivatives are tabulated in the table 3 which were docked using the Glide module of the Schrodinger 2011 software. The table 3 shows the G-score of the 22 molecules with seven different oncoproteins C-MET, Super oxide dismutase, Tubulin, VEGFR, Topoisomerase I and Topoisomerase II. The stability of the docking between ligand with the target protein depends on the binding interactions and thus the G score describes how well the drug has interacted withthe protein. The active sites amino acids of the seven oncoproteins targeted for docking are shown in the table 1. The best scoring five ligands with each of the proteins are shown in blue color. The molecule 4e is among the five best scoring ligands with all proteins. The docking images in Figure 2(a), 2(b) and 2(c) show the hydrogen bonds and their distances between the ligand 4e and the amino acids of target proteins.  The ADME and toxicity result of all the molecules computed in Qikprop 3.4 properties and descriptors are falling within the range or are the recommended values as shown in table 4. 

 

Table 3: Docking result showing XP G scores of all the 22 drug molecules with seven oncoproteins

 

(Scores of five best molecules are indicated in blue color and the score of 4e with all protein is indicated in green)

DISCUSSION

This study presents the docking and Pharmacokinetic parameters study of 22 quinoline based inhibitors retrieved from previous invitro cell line studies on colon and breast cancer. The binding interaction between the ligand molecule and protein has a significant role in determining the activity and binding potential of the drug in computational study. The molecule 4e has given exceptionally good docking score i.e., the XP G score among the other ligands with all the oncoproteins targeted in this study, which shows that 4e is a potential inhibitor and is an important molecule for researchers to develop cancer drugs.

 

Table 4. QikProp properties and descriptors.

Table 5 (A): Predictions of ADMET pharmacological properties for the 22 molecules by QIKPROP 3.4

Table 5(B): Predictions of ADMET pharmacological properties for the 22 molecules by QIKPROP 3.4

Fig 2(a): Docking image of 4e with 1K4T

Fig 2(b): Docking image of 4e with 1ZXM

Fig 2(c): Docking image of 4e with 4B5O

ACKNOWLEDGEMENT

We thank Raghu Rangaswamy, Senior Director and Vinod Devaraji IT consultant from Schrödinger for providing academic evaluations software and continuous support to undertake this research work.

 

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Supplimental Data:

 

Figure given below: 2(a)-2(g) shows binding interaction models of the best scoring molecule:

Figure: 2(a)-2(g) shows binding interaction models of the best scoring molecule as a result of docking with each protein. The hydrogen bonding (broken line shown in pink) between the molecule and the active site amino acids and the bond distance is also depicted.

 

 

Manoj Kumar Mahto persueing his Ph.D. degree in Bioinformatics from the Nagarjuna University, Dept. of Biotechnology, Guntur (A.P). He has completed his M.Sc. in Bioinformatics from Sikkim Manipal University. He has also completed his P.G. Diploma in Bioinformatics from University of Mysore.  He is having an Experience of 8 years in the field of Bio, Chem, Immuno and Medical informatics. He has worked on various Projects of Homology Modeling, Novel lead designing, Docking studies, 3D QSAR studies, Immunoinformatics, PERL, BIOPERL, Micro Array Gene Expression Analysis and Drug Designing. He has been invited as a Guest faculty in various Universities of India. He is having very good research aptitude with quality publications.