1

Research Article

Identification of Potent Hepatitis C Virus RdRp Inhibitors by Structure Based Drug Designing

Sudharsana Sundarrajan, Sweta Kumari, Sajitha Lulu and Mohanapriya Arumugam* Bioinformatics Division, School of Biosciences and Technology, Vellore Institute of Technology University, Vellore, Tamil

Nadu, India – 632014. Correspondence should be addressed to Mohanapriya Arumugam Received 2 July 2014; Accepted 9 July 2014; Published 23 July 2014 Copyright: © 2014 Sudharsana Sundarrajan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Non-structural protein, RNA dependent RNA polymerase, docking, Andrographolide.

Year: 2014; Volume: 1; Issue: 1, Article ID: BCI14 04;

Pages: 1-14

Abstract

Hepatitis C, the silent disease caused by Hepatitis C virus (HCV) is a chronic health infection globally. HCV

causes permanent hepatic cirrhosis and carcinoma in humans. WHO estimated about 3 million incidents of

HCV infection around the world. Multiple variant genotypes along with the development of Quasi-species

limited the efficacy of drugs used for the treatment of HCV infections. This heterogeneity of the virus

hampered the drug development against them. The virus hosts many structural and non-structural (NS)

proteins. NS5B is a non-structural protein with a unique structure and function. The protein is a RNA

dependent RNA polymerase (RdRp) responsible for building the vital genetic component of the virus.

Inhibition of NS5B stops viral replication and propagation. The major role played by RdRP makes it a

preferential target for anti-HCV drug development. An association of docking and rescoring studies was

performed to 24 compounds derived from various plant sources to estimate their activity against HCV NS5B

RdRp. Based on the docking characterization and ADMET properties andrographolide, esculetin, columbin

and tinosporide were identified as they showed greater potency against HCV NS5B RdRp. However, based

on hepato bioactive spectrum and ADMET score, andrographolide from Andrographuis paniculata emerged

as a strong contender with lead like characteristics acting as a promising drug candidate.

2

Introduction

Hepatitis C virus infections represent primary

public health concern. It is estimated that 3 to 4

million people are chronically infected globally.

HCV infection increases the risk of liver cirrhosis

and hepatocellular carcinoma development [1].

HCV infected individuals are treated with

interferon, Ribavirin (a nucleoside analog)

combination and other approved protease

inhibitors [2]. Hepatitis C is termed as a silent

disease since the incubation period of HCV ranges

from 6 to 8 months with no symptoms, making

early detection difficult [3]. However, the therapies

have limited efficacy against HCV genotypes. HCV

has been classified into different strains based on

their genetic differences. HCV diverges into 6

genetic variants. Genetic diversity is also reported

within the subgroups making genetic heterogeneity

responsible for differences in disease outcome and

response to treatment among HCV infected

individuals [4]. Severe drug regime, side effects and

high treatment costs limit patients’ compliance with

the treatment. Therefore, a major focus towards

development of cost effective and efficient anti-viral

therapeutics targeting viral proteins with limited

effects on the host system is of high priority.

HCV contains 9.6 kb positive – sense RNA genome

and belong to Flaviviridae family. The viral

polyprotein is processed into individual structural

proteins - Core, E1, E2 and P7 and non-

structural(NS) proteins - NS2, NS3, NS4A, NS4B,

NS5A and NS5B [5]. Host cytoplasm acts as a

factory for HCV replication. RNA dependent RNA

polymerase (RdRp) activity of HCV nonstructural

protein NS5B mediates viral RNA genome

replication. The importance of NS5B in synthesizing

HCV RNA makes it a preferential target for anti-HCV

inhibitor development. Furthermore, the host cells

lack RdRp activity leading to minimal or no side

effect on the host counterpart.

HCV NS5B has a number of enzymatic and

structural differences from cellular RNA

polymerases, which make it a promising target for

small molecule antiviral development [6].

Phytochemicals from medicinal plants shows

prominent role in prevention and therapy of many

diseases [7]. Our research focuses on the insilico

identification of promising bioactive molecules

against HCV infections by considering NS5B as a

primary target.

Materials and Methods

Identification of protein structure and

preparation of small molecules

The NS5B is 65 kDa proteins with a typical ‘right

hand’ polymerase shape containing finger, palm and

thumb domain. The lambda 1 loop of HCV NS5B,

which extends from the fingers to the thumb

domain, is unique to RNA-dependent RNA

polymerases and the beta-hairpin toward the

catalytic region is specific for HCV NS5B. The

structure of NS5B encloses motifs A-F, which are

crucial for their functional activity [8]. Brookhaven

protein data bank (PDB) is a major resource

repository of three-dimensional structural

information of biological macromolecules like

nucleic acids and proteins. The X-ray structure of

NS5B protein was retrieved from PDB (PDBID-

2YOJ) [9].

Traditional plant derived medicines are proved to

be efficient in the treatment of liver disorders [10].

The bioactive compounds which can act against

HCV NS5B were collected from literature [7, 11]

and books on Indian medicine (Table 1). The

SMILES (Simplified Molecular-Input Line-Entry

System) notation of the compounds were retrieved

from PubChem [30] and converted to PDB format

using CORINA server [31]. The 2D structures of the

compounds are presented in the S. Fig. 1.

3

Table 1: List of phytochemicals used in the study

Sl.No Phytochemical Plant name Family Reference

1. Andrographolide Andrographuis paniculata Acanthaceae [12]

2. Arjungenin Terminalia arjuna Combretaceae [13]

3. Arjunic Acid Terminalia arjuna Combretaceae [13]

4. Berberine Berberis aristata Berberidaceae [14]

5. Chasmanthin Tinospora cordifolia Menispermaceae [15]

6. Columbin Tinospora cordifolia Menispermaceae [16]

7. Desmethylwedelolactone Eclipta alba Asteraceae [17]

8. Esculetin Cichorium intybus Asteraceae [18]

9. Friedelin Azima tetracantha Salvadoraceae [19]

10. Gossypin Hibiscus vitifolius Malvaceae [20]

11. Hyperforin Hypericum perforatum Hypericaceae [21]

12. Hypericin Hypericum perforatum Hypericaceae [21]

13. Hypophyllanthin Phyllanthus amarus Phyllanthaceae [22]

14. Jatrorrhizine Enantia chlorantha Annonaceae [23]

15. Kutkoside Picrorhiza kurroa Plantaginaceae [24]

16. Phyllanthin Phyllanthus amarus Phyllanthaceae [22]

17. Picroside Picrorhiza scrophulariiflora Scrophulariaceae [25]

18. Pseudohypericin Hypericum perforatum Hypericaceae [21]

19. Silymarin Silybum marianum Asteraceae [26]

20. Sitosterol Melothria heterophylla Cucurbitaceae [27]

21. Swertisin Swertia chirayita Gentianaceae [28]

22. Thaliporphine Mahonia leschenaultia Berberidaceae [29]

23. Tinosporide Tinospora cordifolia Menispermaceae [16]

24. Wedelolactone Eclipta alba Asteraceae [17]

Binding site prediction – motif conservation

The full genome sequences of eighteen HCV strains

belonging to six approved HCV genotypes were

retrieved from NCBI [32] (Table 2). Considering

the NS5B sequence of 2YOJ.pdb as the reference

sequence all other sequences were multiply aligned

to identify the conservation of functionally

significant motifs A-D using CLC workbench

(www.clcbio.com). The identified conserved motifs

were considered as the ligand binding site.

4

Table 2: Genbank accession numbers of complete protein sequence of HCV genome

Molecular docking and ADMET predictions

Molecular docking predicts preferred orientation of

the ligand molecule to the protein when bound

together to form a stable complex. The scoring

function gives the strength of association between

them. AutoDock 4.2 [33] was used for docking study

with the Lamarkian genetic algorithm to find

globally optimized conformation. The grid spacing

was set to 0.403 nm and grid box dimension was set

to 86 x 60 x 60 which enclosed the residues of

motifs A to D. Default settings were applied for

remaining parameters. At the end of a docking with

multiple runs, a cluster analysis was performed.

Docking solutions were clustered together and

ranked by the lowest docking energy. The lowest

binding energy cluster was selected as the

representative binding mode. Out of twenty four

bioactive compounds, ten compounds with least

binding energy were considered for the next phase

of screening.

Absorption, distribution, metabolism, excretion and

toxicity (ADME/T) properties receive more concern

in rational drug design, as they determine the

development of safe orally bioavailable drug. The

determination of characteristics of compounds that

are more likely to exhibit satisfying ADME/T

properties has led to the concept of “drug-

likeliness”. Drug likeliness properties obeying rule

of thumb [34] and molecular properties governing

pharmacokinetics of the drugs, including human

intestinal absorption, aqueous solubility, plasma

protein binding and Ames mutagenicity values [35]

were predicted using MolInspiration [36] and

ChemSilico server (www.chemsilico.com). These

parameters act as a filter to screen out more

potential leads.

Biological activity profiles are precise indicators not

only for molecular properties, but also for biological

response of the molecules. Biospectrum connects

chemical scaffolds with biological activity. PASS

(prediction of activity spectra for biologically active

substances) server [37] predicts the

pharmacological effects and biochemical

mechanisms of biologically active substances based

on their structural formula. The biological activity

spectrums for compounds emerging out of

screening processes were predicted. The pass

activity score for hepato-protectant activity (HPA),

hepatic disorder treatment (HPT) and anti-

inflammatory activity (AIA) were taken under

consideration for further analysis.

Molecular dynamics simulation

The molecular dynamics simulation (MDS)

calculates the time dependent behavior of the

molecular system. They are used to investigate the

thermodynamics of biological macromolecules and

their complexes. MDS was performed using

GROMACS 4.5.3 [38]. Gromos96 forcefield [39] was

used to prepare the protein topology file. Ligand

Genotype GB accession number Genotype GB accession number

1a AAB66324.1 3k BAA09890.1

1b Q9WMX2 4a CAA72338.1

1c BAA03581.1 5a CAA73640.1

2a AAY24373.1 6a CAA72801.1

2b AAP55704.1 6b BAA07103.1

2c BAA08911.1 6d AAZ85046.1

2k BAA88057.1 6g BAA09891.1

3a AAC03058.1 6h BAA32667.1

3b BAA08372.1 6k BAA32666.1

5

topology and forcefield parameter file were

prepared using PRODRG server [40]. The full

system was subjected to 5 ns MDS at 300 K

temperature and 1 bar pressure. The best protein

ligand complex was subjected for molecular

dynamics simulation study.

Results and Discussion

NS5B structural and functional analysis

The hydrophilic nature of NS5B was predicted as -

0.201 based on a Gravy index (grand average of

hydropathy) by Expasy ProtParam [41]. RdRp

(Fig.1) is a compact globular protein divided into

finger, palm and thumb domains. The finger domain

has two sub-domains, α finger with seven α helix

and β finger with four β strands. The β finger is

connected to thumb domain through two loops

Ala9-Thr41 (loop 1) and Asn142-Ala157 (loop2).

The incoming ribonucleoside tri-phosphates (rNTP)

enter through a small hole at the bottom of loop 2

to reach the binding cleft. The palm domain is a

combination of three anti-parallel β strands and

four helices [6]. It forms the most important

catalytic center of RdRP and contains highly

conserved motifs A to F. Seven α helices and an

anti-parallel β strand constitutes the thumb region.

The processed RNA duplex is released with the help

of the β sheet in the thumb domain [42].

The multiple sequence alignment of NS5B protein

sequences from six genotypes [43, 44] and their

sub-types approved by ICTV (International

Committee for the Taxonomy of Viruses) were

proved to be conserved in RNA dependent

polymerases by MSA. Motif A (Asp220-Asp225),

motif C (Gly317-Asp319) and motif D (Ala342-

Tyr346) are involved in nucleotide tri-phosphate

binding (NTP) and catalysis while motif B (Ser282-

Asn291) is involved in template-primer positioning.

The conserved patterns of motifs A-D are

highlighted in Fig.2. The residues involved in

divalent cation binding, substrate binding, template

selection and differentiation remain highly

conserved thus validating the binding site

preferences of the drugs.

Molecular Docking

To identify novel candidate inhibitors of HCV

polymerase protein, the twenty four compounds

were docked in the catalytic center of the palm

region. Different binding poses of twenty four

compounds were searched and ranked based on

their binding affinity. Top ten compounds

(andrographolide, esculetin, tinosporide, columbin,

kutokoside, arjunic acid, chasmanthin, silymarin,

gossypin and hypercin) were identified based on

the AutoDock ranking (Fig.3). The binding energy

distribution of the first ten compounds ranged from

-5.42 kcal/mol to -10.43 Kcal/mol. The Binding

free energy and binding interaction patterns of all

the ligands were analyzed using Discovery Studio

Visualizer and PyMol. The free binding energy and

interaction patterns of the ligands represented in

Table 3 are considered as the basic criteria for

efficient activity. The binding mode analysis of the

compounds with NS5B provides a better insight

into the essential residue interactions.

Cation-п interactions [45] are strong, non-covalent

binding force conferring stability in ligand-receptor

complex. Strong cation-п interaction was reported

among the aromatic group of the ligands with the

residues Arg158, Lys141. The lower end of the β

finger of NS5B protein forms a rim of basic amino

acids (Arg48, Lys51, Lys141, Lys155 and Arg158)

to anchor negatively charged rNTPs and Arg158

plays a key role in rNTP binding. The blockage of

this region can inhibit the RdRp activity of the

protein. HCV NS5B shows a strong preference

towards RNA as a template and rNTPs as substrate.

Asp225 of motif A and Ser288 of motif B are

substrate discriminating residues. It should be

noted that, many compounds under our analysis

form hydrogen bonds and electrostatic interactions

with the above residues, silencing their pivotal

function. A hydrophobic pocket formed by Asp225

of motif A and Ser282, Thr281 and Asn291 of motif

B accommodates ribose moiety of rNTP. Interaction

with these residues further hinders the protein’s

functionality. Majority of the ligands interactions

were observed with the catalytic aspartate residues

Asp220, Asp318 and Asp319. The C-terminal region

6

constitutes the regulatory part of HCV NS5B

protein. Ser556 and Ser543 are phosphorylated via

casein kinase-2. Upon phosphorylation RdRp

becomes more active. Interaction of the ligands

with any one of these residue can inactivate RdRp

functional propagation.

Fig. 1: Three dimensional structure of HCV NS5B polymerase (2YOJ) with finger, palm and thumb domains. The domains are colored to emphasize their structural significance. Purple: α fingers, green: β fingers, cyan: palm and pink: thumb domains. The cartoon representation of motifs A – D.

Fig. 2: Multiple sequence alignment of the RdRp sequences of 6 different genotypes from region 200-400 depicting the conservation patterns. The motifs A: D220, T221, R222, C223, F224 and D225.motif B: S282, G283, V284, L285, T286, T287, S288, C289, G290 and N291. Motif C: G317, D318 and D319 and motif D: A342, M343, T344, R345 and Y346 are enclosed within a rectangular box. The amino acids are colored based on their nature.

7

Fig. 3: The successors from first screening phase. Interaction of phytochemicals with the motifs A-C is highlighted. M-A*: interaction with motif A residues; M-A & B*: interaction with motifs A and B residues; M- B& C* interaction with motif B and C residues; M- C*: interaction with motif C residues. (The compounds in our study did not show preferential binding towards the residues of motif D, hence not shown in Fig).

TABLE 3: Binding energy and interacting residues of all the 24 ligands

Sl.No Compounds Binding

Energy

(Kcal/mol)

Residues involved in interactions

1. Andrographolide -10.43 Thr287, Asn291, Asp318

2. Esculetin -9.91 Ser288, Asn291, Gly317(σ-п), Asp318, Tyr555

3. Columbin -8.96 Lys141, Arg158(σ-п), Asn291

4. Tinosporide -8.6 Lys141, Asn291, Asp318

5. Kutkoside -7.7 Arg158, Tyr219, Thr221, Asp225, Asn291

6. Arjunic Acid -7.66 Arg158, Ser226

7. Chasmanthin -7.57 Lys141, Asp225, Asn291, Asp318

8. Silymarin -7.54 Tyr219, Asp319, Leu320, Ser556

9. Gossypin -7.24 Arg158(+-п), Cys223, Asp225, Asn291, Asn316, Asp318, Asp319,

Ser367

10. Hypericin -7.14 Lys141(+-п), Arg158, Ser282, Asp318

11. Picroside -6.92 Asp318, Asp319, Ser556

12. Swertinin -6.84 Arg158 (+-п), Ser226, Asp318

13. Sitosterol -6.80 Ser556

14. Hyperforin -6.67 Asp225, Asn291

15. Wedelolactone -6.65 Arg158(+-п), Asp225, Ser282, Asn291, Asp318

16. Friedelin -6.5 No interaction

17. Berberine -6.32 Arg158(+-п), Asn291

18. Arjungenin -6.08 Asp225, Asp318, Cys366, Ser556

19. Jatrorrhizine -6.00 Arg158(+-п), Asp318

20. Desmethylwedelolactone -5.94 Arg158(+-п), Asp225, Ser556

21. Pseudohypericin -5.53 Arg158(+-п), Asp225, Asp318, Asn316

22. Phyllanthin -5.42 Lys141, Arg158(+-п)

23. Thaliporphyine -5.04 Arg158 (+-п), Asn291

24. Hypophyllanthin -4.46 Lys141, Arg158, Ser556

8

Physiochemical property profile and Bioactivity

predictions

The subsequent screening phase was based on

physiochemical properties of the compounds under

study. The ten phytochemical compounds were

evaluated against thirteen physiochemical

properties which form the backbone of drug

designing process. Compounds which satisfy the

Lipinski’s rule of five (RO5) and other

physiochemical parameters were selected for

further analysis.

Hypericin and kutokoside with molecular weight

504.45 Da and 512.46 Da were excluded from the

study, as high molecular weight affects drug

solubility, absorption and diffusion. Moderate

ClogP (Partition coefficient) is desired for GI track

absorption [46]. Based on RO5 compounds

possessing hydrogen bond donor <5, hydrogen

bond acceptor <10 and total hydrogen bonds <=12

were retained for further analysis [35]. Compounds

with CSLogD7.4 (Distribution coefficient) in the

range of 1-3 shows moderate solubility and

permeability along with favorable in-vivo oral

absorption and blood brain barrier (BBB)

penetration [47]. Gossypin with the distribution

coefficient value of -1.5 was excluded from further

analysis as it failed to fall within desired range.

Chasmanthin with high intrinsic solubility value

makes it an unfavorable lead compound [34]. The

intact drug binding to plasma protein affects its

displacement in the body [35] and silymarin with

98% of plasma binding was eliminated from the

study. The molar refractivity value of arjunic acid

was found to be 187.72 making it unfavorable for

entering the next phase [48]. Polar surface area

(PSA) measures drug’s ability to permeate cell [49,

50] and its cutoff were between 60 Å2 and 140Å2 for

good permeability. Compound kutokoside showed

higher PSA value along with high molecular weight.

Aromatic and heteroaromatic rings are ubiquitous

features in small-molecule drugs. The rings possess

fewer degrees of freedom compared to chains;

hence show increased drug-receptor binding

energy. Less number of aromatic rings (< 3) favors

an oral drug candidate [51]. Hypericin with 6

aromatic rings and high molecular weight was

excluded from the screening process.

Improvisation of our previous study on

phytochemical screening against NS3 protein [5]

was accomplished by performing human intestinal

absorption (HIA) and Ames test for crucial selection

of phytochemicals against our current target HCV

NS5B. Final screening tested HIA [52] and Ames

mutagenecity. The compounds which showed

positive HIA and Non-Toxic Ames test were finally

selected. After a series of crucial screening

tinosporide, columbin, andrographolide and

esculetin emerged as promising drug candidates

satisfying all the screening criteria (Table 4).

Table 4: Physiochemical and pharmacokinetic properties of final hits

C*

P1a P2

b P3

c P4

d P5

e P6

f P7

g P8

h P9

i P10

j P11

k P12

l P13

m P14

n

C1a 1.7 1.7 -2.8 68.1 127.9 55 86.9 350 5 3 6 0 + NT

C2b 1.3 1.1 -2.4 76.4 53.5 19 70.6 178 4 2 0 1 + NT

C3c 1.7 1.7 -1.7 81.2 113.1 49 98.5 374.2 7 1 1 1 + NT

C4d 1.5 1.5 -3.2 88.7 113.7 48 85.9 358.4 6 1 1 1 + NT

C*: Compounds; C1a: Andrographolide; C2b: Esculetin; C3c: Tinosporide; C4d: Columbin;

P1a: CSLogP-Partition co-efficient; P2b: CSLogD7.4-Distribution co-efficient at pH 7.4; P3c: CSLogSo-aqueous solubility; P4d:Plasma binding (%);P5e: Molar refractivity; P6f: Number of atoms; P7g: Polar surface Area,Å2; P8h: Molecular Weight (Daltons); P9i: Number of hydrogen bond acceptor; P10j: Number of hydrogen bond donor; P11k: Number of rotatable bonds; P12l: Number of rings ; P13m: Human intestinal absorption; P14n: AMES test; NT: non toxic

9

Bioactivity prediction forms the final phase of

screening. The Hepato-protectant, hepatic disorder

treatment and anti-inflammatory activity were

predicted for the final four hits using the Pass

server (Fig.4). The compound with Pa (probability

to be active) > 0.7 was considered to exhibit the

activity in an experiment by the PASS server.

Andrographolide was finally narrowed down as it

showed high Hepato-protectant activity (Pa=0.98)

and a higher binding energy of -10.43 Kcal/mol

(Fig.5).

Fig. 4: Bioactive spectrum plot of top 4 hits (tinosporide, columbin, andrographolide and esculetin).

Fig 5. (a) Binding of Andrographolide with NS5B polymerase. Binding site residues are colored yellow and ligand is represented in ball and stick model. (b) 2D plot showing ligand binding site and interacting residues. Arrows indicate hydrogen bond formation between the ligand and the protein. The surface representation of the protein was generated using PyMol and 2D plot was generated using Discovery Studio Visualizer.

Molecular dynamics simulation

10

For a stronger and better insight into the validity of

screening strategy, andrographolide-NS5B complex

was subjected to molecular dynamic simulation

study. The stability of the complex was monitored

over the entire simulation time. The lowest binding

energy conformation was taken as initial

conformation and solvated using SPC (single point

charge) water molecule and neutralized by adding

chlorine ions. In the first equilibration phase a 30 ps

molecular dynamics was performed at 300K and

the system was relaxed by 1000 steps of conjugate

gradient energy minimization. In the second

equilibration phase the system was subjected to

2000 ps dynamics at 300 K and 1 bar pressure.

Finally a 5 ns MD simulation was performed to

relax the protein-ligand complex. The potential

energy fluctuations and RMSD of Cα atoms were

monitored. The potential energy of the whole

system remained constant throughout the

simulation process. The protein structural flexibility

during the simulation process was evident from the

small drifts observed in the RMSD plot. But the

RMSD fluctuations converged after 3 ns simulation

proving that the stability was conferred in the

protein structure (Fig.6).

Fig 6. (a) The RMSD plot obtained by fitting C-alpha of the protein after MD simulation showing convergence after 3 ns. (b) Potential energy plot depicting the stability of the protein.

Conclusion

A high throughput in silico method curtails the time

and cost spent in the synthesis and testing of

compounds before entering the clinical trials.

Modern drugs have their lineage in traditional

medicines. The Indian system of medicines based

on herbs is gaining importance globally in recent

years because of their efficacy, safety, easy

accessibility and cost effectiveness. ADMET has

become an integrated part of the drug discovery

setup, providing guidance in lead selection and

optimization. In our study andrographolide,

columbin, esculetin and tinosporide have exhibited

the characteristics of a lead compound. In addition

to the lead like characteristics, andrographolide has

shown good bioactive spectrum as hepato-

protectant. On further enhancement these leads can

evolve as promising therapeutic agents against HCV

induced hepatic disorders.

Conflict of Interest

We have no conflicts of interest to disclose.

Acknowledgements

We acknowledge VIT University, India for providing

computational facility and support throughout the

work.

11

References

1. Banerjee, Ray RB, Ray R. Onogenic potential of

hepatitis C virus proteins. Viruses 2010; 2:2108-

2133.

2. Murkowski Z, Heim MH. Interferon signalling in

the liver during hepatitis C virus infection.

Cytokine 2009; 59:460–466.

3. Keyvani H, Fazlalipour M, Monavari SH, Mollaie

HR. Hepatitis C virus - proteins, diagnosis,

treatment and new approaches for vaccine

development. Asian Pac J Cancer Prev 2012;

13:5931-5949.

4. Zein NN. Clinical Significance of Hepatitis C Virus

Genotypes. Clin Microbiol Rev 2009; 13:223–

235.

5. Joyce MA, Tyrrell DL.The cell biology of hepatitis

C virus. Microbes and Infect 2010; 12:263-271.

6. Mohanapriya A, Sajitha L, Sweta kumari D,

Nancy veena kumari. Computational screening

and evaluation of bioactive compounds against

NS3 helicase of HCV. Int J Pharm Pharm Sci

2013; 5: 370-376.

7. Koehn FE, Carter GT. The evolving role of

natural products in drug discovery. Nat Rev

Drug Discov 2005; 4:206-220.

8. Ago H, Adachi T, Yoshida A, Yamamto M, Habuka

N, Yatsunami H, Miyano M Crystal structure of

the RNA-dependent RNA polymerase of hepatitis

C virus. Structure 1999; 7:1417-1426.

9. Chen KX, Srikanth Venkatraman, Gopinadhan

NA, Zeng Q, Lesburg CA et al. Discovery of SCH

900188: A potent Hepatitis C Virus NS5B

polymerase inhibitor prodrug as a development

candidate. J Med Chem 2013; 5:244-248.

10. Chaudhary GD, Kamboj P, Singh I, Kalia AN. Herb

as liver savers – A review. Indian J Nat Prod Res

2010; 1:397-408.

11. Govind P. Medicinal plants against liver diseases.

IRJP 2011; 2:115-121.

12. Handa SS, Sharma A. Hepatoprotective activity

of andrographolide from Andrographuis

paniculata against carbon tetrachloride. Indian J

Med Res 1990; 92:276-283.

13. Doorika P, Ananthi T. Antioxidant and

Hepatoprotective properties of Terminalia

arjuna bark on Isoniazid Induced Toxicity in

Albino rats. Asian J Pharm Tech 2012; 2:15-18

14. Tiwari BK, Khosa RL. Evaluation of the

Hepatoprotective and antioxidant effect of

Berberis asiatica against experimentally induced

liver injury in rats. Int J Pharm Pharm Sci 2010;

2:92-97.

15. Vipin Kumar, Modi PK, Saxena KK. Exploration

of hepatoprotective activity of aqueous extract

of Tinospora cordifolia - an experimental study.

Asian J Pharm Clin Res 2013; 6:87-91.

16. Patwardhan B, Manish Gautam. Botanical

immunodrugs: scope and opportunities. Drug

Discov Today 2005; 10:495-502.

17. Murthy VN, Praveen Reddy B, Venkateshwarlu V,

Kokate CK. Antihepatotoxic activity of Eclipta

alba, Tephrosia purpurea and Boerhaavia

diffusa. Anc Sci Life 1992;11:182-186.

18. Atmaca M, Bilgin HM, Obay BD, Diken H et al.

The hepatoprotective effect of coumarin and

coumarin derivates on carbon tetrachloride-

induced hepatic injury by antioxidative activities

in rats. J Physiol Biochem 2011; 67:569-576.

19. Ekbote, Ramesh MT, Mahmood CK,

Thippeswamy R et al. Hapatoprotective and

antioxidant effect of Azima tetracantha Lam,

leaves extracts against CCL4 induced liver injury

in rats. Indian J Nat Prod Res 2010; 1:493-499.

20. Samuel AJ, Mohan S, Chellappan DK,

Kalusalingam A, Ariamuthu S. Hibiscus vitifolius

(Linn.) root extracts shows potent protective

action against anti-tubercular drug induced

hepatotoxicity. J Ethnopharmacol 2012;

141:396–402.

21. Bayramoglu G, Bayramoglu A, Engur S, Senturk

H et al. The hepatoprotective effects of

Hypericum perforatum L. on hepatic

ischemia/reperfusion injury in rats.

Cytotechnology 2014 ;66:443-448.

22. Pramyothin P, Ngamtin C, Poungshompoo S,

Chaichantipyuth C. Hepatoprotective activity of

Phyllanthus amarus Schum. et. Thonn. Extract in

ethanol treated rats: in vitro and in vivo studies.

J Ethnopharmacol 2007; 114:169–173.

23. Adesokan AA, Akanji MA. Effect of

administration of aqueous extract of Enantia

chlorantha on the activities of some enzymes in

the small intestine of rats. Nigerian J Biochem

Molec Biol 2003; 18:103-105.

24. Jeyakumar R, Rajesh R, Rajaprabhu D, Ganesan B

et al. Hepatoprotective effect of Picrorrhiza

kurroa on antioxidant defense system in

antitubercular drugs induced hepatotoxicity in

rats. Afr J Biotechnol 2009; 8:1314-1315.

12

25. Gao H, Zhou Y. Inhibitory effect of picroside II

on hepatocyte apoptosis. Acta Pharm Sin B

2005;26:729–736.

26. Saller R, Meier R, Brignoli R. The use of silymarin

in the treatment of liver diseases. Drugs 2001;

61: 2035-2063.

27. Mondal A, Maity TK, Pal D, Sannigrahi S et al.

Isolation and in vivo hepatoprotective activity of

Melothria heterophylla (Lour.) Cogn. against

chemically induced liver injuries in rats. Asian

Pac J Trop Med 2011; 4:619–623.

28. Jagmohan S, Pramod Singh, Rawat B. Chemical

constituents and Biological importance of

Swertia: A review. Current Research in

Chemistry 2011; 3:1-15;

29. Vijayan P, Raghu C, Ashok G, Dhanaraj A et al.

Antiviral activity of medicinal plants of Nilgiris.

Indian J Med Res 2004; 120:24-29.

30. Bolton E, Wang Y, Thiessen PA, Bryant SH.

PubChem: Integrated Platform of Small

Molecules and Biological Activities, in Annual

Reports in Computational Chemistry, American

Chemical Society, Washington, DC. 2008.

31. Sadowski J, Gasteiger J, Klebe G. Comparison of

Automatic Three-Dimensional Model Builders

Using 639 X-Ray Structures. J Chem Inf Comput

Science 1994; 34:1000-1008.

32. Simmonds P, Bukh J, Combet C, Deleage G et al.

Consensus Proposals for a Unified System of

Nomenclature of Hepatitis C Virus Genotypes.

Hepatology 2005; 42:962-973.

33. Morris GM, Huey R, Lindstrom W, Sanner MF et

al. Autodock4 and AutoDockTools4: automated

docking with selective receptor flexibility. J

Comput Chem 2009; 30:2785-2791.

34. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ.

Experimental and computational approaches to

estimate solubility and permeability in drug

discovery and development settings. Adv Drug

Deliv Rev 2001;46:3–26.

35. Edward HK, Li Di. Drug-like properties:

Concepts, Structure Design and Methods: from

ADME to Toxicity optimization. Academic Press

2008.

36. Ertl P, Rohde B, Selzer P. Fast calculation of

molecular polar surface area as a sum of

fragment-based contributions and its

application to the prediction of drug transport

properties. J Med Chem 2000;43:3714-3717.

37. Filimonov D, Poroikov V. Bioactive compound

design: Possibilities for industrial use. PASS:

Computerized prediction of biological activity

spectra for chemical substances, BIOS Scientific

Publishers. 1996; 44-56.

38. Berendsen HJC, Van der Spoel D, Van Drunen R.

GROMACS: A message-passing parallel

molecular dynamics implementation. Comput

Phys Commun 1995; 91:43-56.

39. Schuler LD, Daura X, Van Gunsteren WF. An

improved GROMOS96 force field for aliphatic

hydrocarbons in the condensed phase. J Comput

Chem 2001; 22:1205–1218.

40. Schüttelkopf AW, Van Aalten DMF. PRODRG: a

tool for high-throughput crystallography of

protein-ligand complexes. Acta Crystallogr 2004;

D60:1355–1363.

41. Gasteiger E, Hoogland C, Gattiker A, Duvaud S et

al. In John M. Walker (ed) Protein Identification

and Analysis Tools on the ExPASy Server; 2005.

42. Ranjith-Kumar CT ,Cheng Kao C In Seng-Lai Tan

(ed) Hepatitis C Viruses: Genomes and Molecular

Biology; 2006.

43. Waheed Y, Saeed U, Anjum S, Afzal MS et al.

Development of Global Consensus Sequence and

Analysis of Highly Conserved Domains of HCV

NS5B Protein. Hepat Mon 2012; 12.

44. Sarwar MT, Kausar H, Ijaz B, Ahmad W et al.

NS4A protein as a marker of HCV history

suggests that different HCV genotypes originally

evolved from genotype 1b. Virol J 2011; 8:317-

320.

45. Dougherty DA. Cation-п Interactions Involving

Aromatic Amino Acids. J Nutr 2007; 137:1504S–

1508S.

46. Mannhold R, Poda GI, Ostermann C, Tetko IV.

Calculation of Molecular Lipophilicity: State-of-

the-Art and Comparison of Log P Methods on

More Than 96,000 Compounds. J Pharm Sci

2008; 98:861-893.

47. Pajouhesh H, Lenz GR. Medicinal Chemical

Properties of Successful Central Nervous System

Drugs. NeuroRx® 2005; 2 : 541-553.

48. Wager TT, Ramalakshmi Y, Chandrasekaran,

Hou X et al. Defining Desirable Central Nervous

System Drug Space through the Alignment of

Molecular Properties, in Vitro ADME, and Safety

Attributes. ACS Chem Neurosci 2010; 1: 420–

434.

49. Ertl P, Rohde B, Selzer P.Fast Calculation of

Molecular Polar Surface Area as a Sum of

Fragment-Based Contributions and Its

13

Application to the Prediction of Drug Transport

Properties. J Med Chem 2000; 43: 3714–3717.

50. Ghose AK , Viswanadhan VN, Wendoloski JJ. A

knowledge-based approach in designing

combinatorial or medicinal chemistry libraries

for drug discovery. A qualitative and

quantitative characterization of known drug

databases. J Comb Chem 1999; 1:55-68.

51. Ritchie TJ, Macdonald S. The impact of aromatic

ring count on compound developability – are too

many aromatic rings a liability in drug design?

Drug Discov Today 2009; 14:1011-1020.

52. Foye WO. Principles of Medicinal Chemistry. Lea

and Febiger, Philadelphia. 1998.

S. Fig. 1: 2D structures of Phytochemicals used in the study

Andrographolide Arjungenin Arjunic acid Berberine

Chasmanthin Columbin Desmethylwedelolactone Esculetin

Friedelin Gossypin Hyperforin Hypericin

14

Hypophyllanthin Jatrorrhizine Kutkoside Phyllanthin

Picroside Pseudohypericin Silymarin Sitosterol

Swertisin Thaliporphine Tinosporide Wedelolactone