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In Silico Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)

Jayameena P., Sivakumari K. , Ashok K., Rajesh S.
Journal of Cancer Research and Treatment. 2018, 6(2), 28-33. DOI: 10.12691/jcrt-6-2-1
Published online: April 27, 2018

Abstract

Rutin as a flavonoid compound contains many flavonoids having antitumor properties. Therefore, the present study was aimed to dock rutin compound with apoptotic proteins like TNF, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytohrome C by AutoDock software. The docking scores were highest in Nitric oxide synthase (-3.68 kcal/mol) followed by Tumor Necrosis Factor (-3.22 kcal/mol), Caspase-3 (-2.95 kcal/mol), Collagenase (-2.47 kcal/mol), Cytochrome C (-2.31 kcal/mol), NF-kappa-B (-1.8 kcal/mol) and P53 (-0.32 kcal/mol). The Log P value and lower hydrogen bond counts, confirming the ability of rutin compound for binding at the active sites of the receptor was determined by the in silico method. The potential drug candidate can further be validated by wet lab studies for its proper function.

1. Introduction

Apoptosis or Programmed Cell Death is an evolutionarily conserved and extremely synchronized form of cell death to facilitate the deletion of redundant, infected, injured or malformed cells during the normal life span in various biological systems which is an essential course of action in maintaining homeostasis in multicellular organisms. It is usually implicated in embryogenesis, metamorphosis, immune system and normal adult tissue remodeling as well as in a number of pathological disorders such as cancer, autoimmunity and degenerative diseases. Generally cancer cells themselves are more prone to undergo apoptosis and a comprehensive understanding of the molecular pathways that regulate apoptosis will assist in investigating novel cancer chemotherapeutic targets 1 which in turn would offer new opportunities for the discovery and development of drugs 2.

Flavonoids, the polyphenolic compounds act as the major nutritional constituents of plant-based food as habitual and folkloric medicine worldwide 3, 4. Rutin, a common dietary flavonoid with a wide range of pharmacological activities is present in many plants, fruits, vegetables and red wine 5, 6, 7. Different studies have represented the biological effects of rutin, such as anti-oxidative, anti-inflammatory, antihypertensive, anti-carcinogenic, cytoprotective, anti-platelet, antithrombic, anti-diabetic, anti-adipogenic, neuroprotective, hormone therapy and cardioprotective activities 8, 9, 10.

In the present investigation, to study the physco chemical properties of the apoptoticproteins, to carry out docking of seven different kinds of apoptotic proteins viz., Tumor Necrosis Factor (TNF-alpha), Caspase-3, NF-kappa-B p105 subunit, Cellular Tumor Antigen p53, 72 kDa Type IV Collagenase, Nitric Oxide Synthase and Cytochrome C oxidase subunit 2.

2. Materials and Methods

2.1. Preparation of Protein Structure

The protein information was obtained from Swissprot and the apoptotic protein structures were obtained from RCSB Protein Databank (www.rcsb.org/pdb/home/home.do). The hydrogen atoms were added to the target protein molecules after removing the water molecules for docking. The 3D structures of the proteins were visualized using RASMOL.

2.2. Preparation of Ligand Structure

Ligand is a small molecule, which interacts with protein’s binding sites. There are several possible mutual confirmations in which binding may occur. These are commonly called binding modes 11. ChemSketch developed by advanced chemistry development, inc., (http;//www.acdlabs.com) was used to construct structure of rutin. Using draw mode of ChemSketch, the ligands were generated and the three dimensional optimization were done and then saved in .mol file and TORSDOF was used in calculating the changes in free energy caused by the loss of torsional degrees of freedom using binding. After all the above conditions are set and the ligand was saved in “pdbq” format.

2.3. Preparation of Receptors

The receptor file used by AutoDock must be in “pdbqs” format which is pdb plus “q” charge “s” salvation parameters: AtVol, the atomic fragmental volume and AtSolPar, the atomic salvation parameter which are used to calculate the energy contributions of desolvation of the receptors, ie., macromolecules by ligand binding was also calculated using Open Babel.

2.4. Preparation of Grid Parameter File

The grid parameter file tells AutoGrid the types of maps to compute, the location and extent of those maps and specifies pair-wise potential energy parameters. In general, one map is calculated for each element in the ligand plus an electrostatic map. Self-consistent 12-6 Lennard Jones energy parameters equilibrium internuclear separation and the energy well depth are specified for each map based on types of atoms in the macromolecule. If we want to model hydrogen bonding, this is done by specifying 12-10 instead of 12-6 parameters in “gpf” format. The grid parameter were set using AutoGrid.

2.5. Starting AutoGrid and AutoDock

AutoGrid and AutoDock must be run in the directories where the macromolecules, gpf, dpf files, ligand and parameter files are to be found.

2.6. Analysing the Docking Results

The key results in a docking log are the docked structures found at the end of each run, the energies of these docked structures and their similarities to each other. The similarities of docked structures are measured by computing the RMSAD between the coordinates of the atoms. The docking results consists of the PDBQ of the Cartesian coordinates of the atoms in the docked molecule along with the atate variables that describes this docked conformation and position and this was done by PyMol.

3. Results and Discussion

In the present study, the interaction between Tumor Necrosis Factor (TNF-alpha), Caspase-3, Nuclear Factor NF-kappa-B p105 subunit, Cellular Tumor Antigen p53, 72 kDa Type IV Collagenase, Nitric oxide Synthase and Cytochrome C oxidase subunit 2 were studied to explore the binding mode, docking study was performed using AutoDock with PyMol tool. Apoptotic protein structures were derived from PDB Database and used as a target for docking simulation. The ligands were created and prepared for docking studies using ChemSketch. The structure of the ligands obtained from ChemSketch is given in Figure 1. The deduction of ligand-binding sites is the initial step for normal drug discovery. Here the Q-site finder predicted the active site of the apoptotic proteins precision as shown in Figure 2. As most of the amino acid resumes in the active site are hydrophobic, they are the main contributions to the receptor-ligand interactions.

3.1. Details of Docking Interaction

To study the binding mode of rutin compound in the binding site of apoptic proteins, intermolecular flexible docking simulations were performed and energy values were calculated from the docked conformations of the protein-inhibitor complexes. Docking studies yielded crucial information concerning the orientation of the inhibitors in the binding pocket of the target proteins. Several potential inhibitors have been identified through the docking simulation. The binding affinity of the apoptotic proteins with the rutin compound was measured by kcal/mol. The docking scores were highest for Nitric oxide synthase with -3.68 kcal/mol with the stronger interaction followed by Tumor Necrosis Factor (-3.22 kcal/mol), Caspase-3 (-2.95 kcal/mol), Collagenase (-2.47 kcal/mol), Cytochrome C (-2.31 kcal/mol) Nuclear Factor NF-kappa-B (-1.8 kcal/mol) and the least score was found in p53 (-0.32 kcal/mol) as shown in the Table 1 and Figure 3. Likewise, hydrogen bond formation was good in all the seven proteins, when docked with rutin. The hydrogen bond formation was high in Caspase 3 with 6 hydrogen bond formation, followed by TNF and Collegenase with 4 hyrogen bond formation, NF-kappa-B with 3 hydrogen bond formation, p53 and Nitric Oxide Syntase with 2 hydrogen bonf formation and the least was observed in Cytochrome C with 1 hydrogen bond formation. Similar type of studies with Qucertin, Fucoidan and Resveratrol compounds were also performed by Ashok and Sivakumari (2015) 12, Manimaran et al. (2015) 13, Muthukala et al. (2015) 14 and Rajesh et al. (2016) 15. The protein-ligand interaction plays an important role in structural-based designing 16.

Drug discovery is most prominent process in current days and that begin with target and lead discovery, followed by lead optimization and pre-clinical in vitro and in vivo studies to recognize the potent compounds for which assure the main criteria for drug development 17. To develop a drug through an in vivo and in vitro methods take long time and with high expenditure 18, 19. Computational drug discovery can help in identifying potent drugs molecules and targets via., bioinformatics tools. They can also be used to evaluate the target structures for possible binding/active sites, generate active drug molecules, check for their dynamic and kinetic properties, the docking studies of these molecules with the target molecules will help us to know the affinity and efficacy of developed molecule and we rank them according to their binding affinities 20. The molecules which are showing better activity can be modified and built to get good activity towards the target molecules, and further the molecules are optimized to improve binding characteristics. The use in silico methods will help us in all aspects of drug discovery today and forms the importance of structure-based drug design. There are plenty of programs which are helping us to build an active drug molecule. Meanwhile, high-performance computing, data management software and internet are helping us to generate high quality data generated complex data and also transformation of huge complex biological data into accessible knowledge in current trends to discover a novel drug molecules 21, 22.

Apoptosis is a tightly regulated and at the same time highly efficient cell death program which requires the interplay of a multitude of factors. The components of the apoptotic signaling network are genetically encoded and are considered to be usually in place in a nucleated cell ready to be activated by a death-inducing stimulus 23, 24, 25, 26, 27, 28, 29, 30, 31. Apoptosis is well identified biological response exhibited by cells after suffering DNA damage and is a useful marker for screening compounds for subsequent development as possible anti-cancer agents 32. Apoptosis provides a number of clues with respect to effective anticancer therapy, and many chemotherapeutic agents reportedly exert their antitumor effects by inducing apoptosis in cancer cells 33.

The goal of ligand-protein docking is to predict the predominant binding model(s) of a ligand with a protein of known three dimensional structures 34. Ligand binding is the key step in enzymatic reactions and, thus, for their inhibition. Therefore, a detailed understanding of interactions between small molecules and proteins may form the basis for a rational drug design strategy 35, 36, 37, 38, 39. The unit of Glide Score is Kcal/mol and it includes ligand-protein interaction energies, hydrophobic interactions, hydrogen bonds, internal energy, pi stacking interactions, RMSD and desolvation 40. Molecular docking, both structure-based and ligand-based, has become a powerful and inexpensive method for searching a novel lead compound. Molecular docking has been successful in discovering novel anticancer compounds against several protein targets, such as BCR-ABL tyrosine kinase, Chk1, FKBP, protein tyrosine phosphatase (PTP) Caspase-3, Caspase-9, Cytochrome-C, NF-kappa-B, β-Actin, Transferrin, Plasminogen, BCL-2 and EGFR as well.

The docking result showed that there exists a binding interaction between each protein and rutin ligand, which was validated by the formation of hydrogen bond between the proteins and the ligand. Lipinski rule also suggests ruitn as the best therapeutic drug. The results clearly depicts that there is an interaction between the ligand and proteins. Hence, the in silico molecular docking studies suggests that rutin can be utilized as a potential and green therapeutic agent to treat various diseases.

4. Conclusion

In this study, the molecular docking was carried out to explore the binding interaction of rutin compound with apoptotic proteins and to correlate its docking score with the activity of rutin compound. The results are helpful for designing and developing a novel drug that has better inhibitory activity against several types of cancers. From this study we conclude that rutin compound is one of the best phytochemical anticancer agent. This potential drug candidate awaits further validation by wet lab studies for its proper function as an anticancer drug.

Acknowledgements

The authors are thankful to Mrs. K.Shyamala Devi for the technical support.

References

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In article      
 
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In article      View Article  PubMed
 
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In article      View Article  PubMed
 
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In article      View Article
 
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In article      View Article
 
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In article      View Article  PubMed
 
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In article      View Article  PubMed
 
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In article      
 
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Published with license by Science and Education Publishing, Copyright © 2018 Jayameena P., Sivakumari K., Ashok K. and Rajesh S.

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
Jayameena P., Sivakumari K., Ashok K., Rajesh S.. In Silico Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C). Journal of Cancer Research and Treatment. Vol. 6, No. 2, 2018, pp 28-33. http://pubs.sciepub.com/jcrt/6/2/1
MLA Style
P., Jayameena, et al. "In Silico Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)." Journal of Cancer Research and Treatment 6.2 (2018): 28-33.
APA Style
P., J. , K., S. , K., A. , & S., R. (2018). In Silico Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C). Journal of Cancer Research and Treatment, 6(2), 28-33.
Chicago Style
P., Jayameena, Sivakumari K., Ashok K., and Rajesh S.. "In Silico Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)." Journal of Cancer Research and Treatment 6, no. 2 (2018): 28-33.
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  • Figure 2. The active sites of apoptotic proteins have the amino acid sequences are as follows: A - TNF-alpha protein, B - Caspase-3, C - NF-kappa-B, D - P53, E – Collagenase, F - Nitric Oxide Synthase, G – Cytochrome C
  • Figure 3. Pictorial representation of Docked Complex using PYMOL tool where, Protein is in pink color, Rutin in blue color and the H-Bond is indicated by yellow color dots: A - TNF-alpha protein, B - Caspase-3, C - NF-kappa-B, D - P53, E – Collagenase, F - Nitric Oxide Synthase, G – Cytochrome C
[1]  Bailey K, Cook HW, McMaster CR. The phospholipid scramblase PLSCR1 increases UV induced apoptosis primarily through the augmentation of the intrinsic apoptotic pathway and independent of direct phosphorylation by protein kinase C δ. Biochim.Biophys Acta 2005;1733:199-209.
In article      
 
[2]  Alam JJ. Apoptosis: target for novel drugs. Trends Biotechnol 2003; 21:479-83.
In article      View Article  PubMed
 
[3]  Carnat AP, Carnat A, Fraisse D, Lamaison JL, Heitz A, Wylde R. Violarvensin, a new flavone di-C-glycoside from viola arvensis. J Nat Prod 1998; 61: 272–274.
In article      View Article  PubMed
 
[4]  Goncalves AFK, Friedrich RB, Boligon AA, Piana M, Beck RCR, Athayde ML. Antioxidant capacity, total phenolic contents, and HPLC determination of rutin in Viola tricolor (L) flowers. Free Radicals Antioxid 2012; 2: 32–37.
In article      View Article
 
[5]  Buszewski B, Kawka S, Suprynowicz Z, Wolski T. Simultaneous isolation of rutin and esculin from plant material and drugs using solid-phase extraction. J Pharm Biomed Anal 1993; 11: 211-215.
In article      View Article
 
[6]  Tang DQ, Wei YQ, Gao YY, Yin XX. Protective effects of rutin on rat glomerular mesangial cells cultured in high glucose conditions. Phytother Res 2011; 25: 1640–1647.
In article      View Article  PubMed
 
[7]  Wu CH, Lin MC, Wang HC, Yang MY, Jou MJ, Wang CJ. Rutin inhibits oleic acid induced lipid accumulation via reducing lipogenesis and oxidative stress in hepatocarcinoma cells. J Food Sci 2011; 76: 65-72.
In article      View Article  PubMed
 
[8]  Chen S, Gong J, Liu F, Mohammed U. Naturally occurring polyphenolic antioxidants modulate IgE-mediated mast cell activation. Immunology 2000; 100: 471-480.
In article      View Article  PubMed
 
[9]  Lee S, Suh S, Kim S. Protective effects of the green tea polyphenol (-)-epigallocatechin gallate against hippocampal neuronal damage after transient global ischemia in gerbils. Neurosci Lett 2000; 287: 191-194.
In article      View Article
 
[10]  Novakovic A, Gojkovic-Bukarica L, Peric M, Nezic D, Djukanovic B, Markovic-Lipkovski J. The mechanism of endothelium-independent relaxation induced by the wine polyphenol resveratrol in the human internal mammary artery. J Pharmacol Sci 2006; 101: 85–90.
In article      View Article  PubMed
 
[11]  Kittal RR, McKinnon RA, Sorich MJ. Comparison data stes for benchmarking QSAR methodologies in lead optimization. J Chem Inf Model 2009:49:1810-20.
In article      View Article  PubMed
 
[12]  Ashok K, Sivakumari K. In silico docking of fucoidan compound against the selective proteins of HepG-2 cell line. IJCPS 2015 6(4): 13-16.
In article      
 
[13]  Manimaran M, Sivakumari K, Ashok K. Molecular docking studies of 16.Reseveratrol against the human oral cancer cell line proteins (KB cells). Int J Curr Adv Res 2015 4(10): 275-280.
In article      
 
[14]  Muthukala B, Sivakumari K, Ashok K. In silico docking of Qucertin compound against the HeLa cell line proteins. Int J Curr Pharma Res 2015: 13-16.
In article      
 
[15]  Rajesh S, Sivakumari K, Ashok K. In silico docking of selected compound from Cardiospermum halicacabum Linn. leaf against human hepatocellular carcinoma (HepG-2) cell line. Int. J. Comp. Bioin. In Silico Model, 2016; 5(2): 780-786.
In article      
 
[16]  Sanghani HV, Ganatra SH, Pande R. Molecular docking studies of potent anticancer agent. J Comput Sci Syst Biol 2015: 5: 012-015.
In article      
 
[17]  Bleicher, KH., Bohm, HJ., Muller, K, Alanine, AI. Hit and lead generation: Beyond high-throughput screening. Nat. Rev. Drug Discov., 2003: 2(5): 369-378.
In article      View Article  PubMed
 
[18]  DiMasi, JA. Trends in drug development costs. Drug Inform., 1995: 29: 375-380.
In article      View Article
 
[19]  DiMasi, JA., Hansen, RW, Grabowski, HG. The price of innovation: New estimates of drug development costs. J. Health Econ., 2003: 22(2): 151-185.
In article      View Article
 
[20]  Irwin, J., Lorber, DM., McGovern, SL., Wei, B, Shoichet, BK. Molecular docking and drug discovery. Comp. Nanosci. Nanotech., 2002: 2: 50-51.
In article      
 
[21]  Taft, CA., Silva, VB, Silva, CHT. Current topics in computer-aided drug design. J. Pharm Sci., 2008: 97(3):1089-1098.
In article      View Article  PubMed
 
[22]  Manimaran, M, Sivakumari, S, Ashok, K, Rajesh, S. Evaluation of the in vitro antimicrobial effect of resveratrol on human pathogens. Int. J. Zoology Studies, 2017: 2(5): 123-127.
In article      
 
[23]  Ishizaki, Y., Cheng, L., Mudge, AW, Raff, MC. Programmed cell death by default in embryonic cells, fibroblasts and cancer cells. Mol. Biol. Cell., 1995: 6(11): 1443-1458.
In article      View Article  PubMed
 
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