Human Papilloma Virus Proteins Biology Essay

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3.1.1 Human papilloma virus proteins and their mode of action

Human papillomavirus (HPV) is a ubiquitous sexually transmitted DNA virus. A subset of mucosal HPVs are termed "high-risk" (for example, types 16, 18 and 31) because of an increased association with cervical cancer (Zur Hausen., 1996). Among this group, HPV16 is the most common type, being found in about fifty percent of invasive cancers worldwide (Clifford et al., 2003). Two HPV16 oncoproteins, E6 and E7, are actively expressed in cervical cancer cells and are responsible for host cell transformation and cancer progression (Griep et al., 1993, Yutsudo et al., 1998). From previous studies, the E6(I) transcript has been found to be the most abundant one detected in HPV16 transformed cells, transgenic animals, cervical cancer cell lines and clinical samples ( Griep et al.,1993, Smotkin et al., 1989, Cornelissen et al., 1990 ). While E7 is predicted to translate from spliced products as well as full-length transcripts, E6 protein can only be encoded from full-length transcripts (Smotkin et al., 1989, Schneider et al., 1988). The splicing has been proposed to promote E7 translation by providing space for ribosome initiation to occur (Smotkin et al., 1989, Smotkin et al., 1986). However, it has been revealed that the translation of E7 from the full-length transcript is as efficient as those from spliced transcripts and that splicing is not required for E7 synthesis (Stacey et al., 1995).

3.1.2 Human papilloma virus -16 E6 and E7 proteins structure

The target for oral and cervical cancer as HPV 16 E6 and E7 has been taken for this study. The proteins taken for this study retrieved from the PDB (Protein Data Bank). The X-ray crystallography structure (3D structure) taken for the HPV 16 E6 (PDB ID: 2FFK4) and HPV E7 (PDB ID: 2B9D) protein taken for this study. The of sequence informations for both the protein in fig 3.1.2.1 and 3.1.2.2.

>2FK4: A |PDBID|CHAIN|SEQUENCE

GAMSYSLYGTTLEQQYNKPLSDLLIRCINCQKPLSPEEKQRHLDKKQRFHNIRGRWTGRCMSCSRSSRTRRETQL

>SPR|P03126|VE6_HPV16 (158 AA) E6 protein [Human papillomavirus type 16]

MHQKRTAMFQDPQERPRKLPQLCTELQTTIHDIILECVYCKQQLLVYDFAFRDLCIVYRDGNPYAVCDKCLKFYSKISEYRHYCYSLYGTTLEQQYNKPLCDLLIRCINCQKPLCPEEKQRHLDKKQRFHNIRGRWTGRCMSCCRSSRTRRETQL

Fig. 3.1.2.1 Fasta Sequence of HPV16 E7 Protein In PDB (PDB ID: 2FK4)

>2B9D:A|PDBID|CHAIN|SEQUENCE

MKQPYAVVASCAYCEKLVRLTVLADHSAIRQLEEMLLRSLNIVCPLCTLQRQ

Fig. 3.1.2.2 Fasta Sequence of HPV E7 Protein

3.1.3 Structural composition of target proteins

This has been well known that Papillomaviruses (PV) infect mammals, birds and reptiles world wide. The virus is small circular double stranded DNA (8 kb) more than 170 PV (>100 HPV) strains. These viruses infect cutaneous or mucosal epithelial cells. Papilloma viruses were classified as High risk and Low risk on the basis of infection. Low risk HPVs generate benign lesions (warts) while High risk HPVs may generate tumours. The percentage of virus in cervical cancers is linked to HPV infection are 99 percentage. Link between HPV and cancer was being recognised by Nobel Prize winner H. zur Hausen in 2008. The main oncoproteins of HPV are called E6 and E7. E6 and E7 cooperate to prime cell proliferation for the sake of viral replication.E6 (genital high-risk HPVs). E6 participates to proliferation of infected cells in host cells and also participates to maintenance of viral episomes. This is well reported that E6 and E7 genes always integrated in cervical cancer cells. E6 has immortalizing and transforming effects.E6 modulates apoptosis, senescence and cell adhesion processes too in cancer pathogenesis.

The structure of shows that HPV 16 E6 contains 150 residues, 2 zincs sites. The cellular localization of HPV 16 E6 is preferentially nuclear and it binds and often degrades more than 50 cellular target proteins. The protein recruits the cellular ubiquitin ligase E6AP and helps promotes degradation of tumor suppressor p53 (E6/E6AP/p53 complex). The protein promotes degradation of several PDZ domain proteins and promotes expression of the RT subunit of telomerase is a DNA-binding protein recognizing four-way Holliday junctions and it strongly binds to RNA Fig.3.1.3.1(A). The two domains of consisting of paired CXXC motifs that are each related to the E7 carboxyl terminus (Cole et al, 1987).

A

Fig.3.1.3.1(A).Schematic representation of the HPV-16 E6 oncoprotein. The sequence contains two metal binding motifs that are related to the E7 carboxyl terminus (blue). The E6 carboxyl terminus contains a PDZ protein-binding motif (yellow) that is similar to the carboxyl-terminal PDZ binding motif of Ad9 E4 ORF1. Many HPV-16 E6 binding proteins, including E6-AP, paxillin, E6-BP, and IRF-3, contain a conserved α-helical domain and presumably interact with similar E6 sequences. The isoleucine residue at position 128 importantly contributes to interaction with α-helix domains containing E6 binding proteins. Identical and chemically similar amino acid residues are highlighted by red and blue boxes, respectively.

B

Fig.3.1.3.1 (B)Schematic representation of the HPV-16 E7 oncoprotein. The amino-terminal 37 amino acid residues have sequence similarity to a portion of CR1 (green) and to CR2 (red) of Ad E1A. Identical and chemically similar amino acid residues between HPV-16 E7 and Ad5 E1A are highlighted by red and blue boxes, respectively. CR1 sequences are necessary for cellular transformation and pRB degradation but do not directly contribute to pRB binding. Sequences in CR2 include the core pRB binding site (LXCXE), which is necessary for cellular transformation, as well as a casein kinase II consensus phosphorylation site (CKII). The E7 carboxyl terminus (blue) contains a metal binding motif and mediates association with multiple host cellular proteins, including histone-modifying enzymes, which may also contribute to cellular transformation.

The ability of high-risk HPV E6 proteins to associate with PDZ host proteins is relevant to cellular transformation. This relevance has been best illustrated in a transgenic mouse model in which the ability of HPV-16 E6 to induce skin hyperplasias (Lambert et al, 1993) is dependent on the integrity of the carboxyl-terminal PDZ binding domain. A considerable number of additional cellular proteins have been reported to associate with E6. These include the EF-hand calcium-binding protein E6-BP (reticulocalbin 2) (Chen et al., 1995) the interferon regulatory factor IRF-3 ( Ronco et al., 1998) and the focal adhesion protein paxillin (Tong et al., 1997, Vande et al., 1998.). Hyperactivity of focal adhesion kinase (FAK) has been detected in cervical cancer and HPV immortalized epithelial cell lines, but the mechanism is unclear (McCormack et al., 1997). Because these and other potential E6 cellular target proteins share a conserved α-helical interaction site for E6 association (Elston et al., 1998) (Fig.3.1.3.1 (A)), it has been difficult to determine the relevance of these individual interactions to the biological activities of high-risk HPV E6 proteins.

High-risk HPV-derived E7 proteins interact with pRB more efficiently than E7 proteins encoded by low-risk mucosal HPVs (Gage et al., 1990, Munger et al., 1989), and mutations in the LXCXE domain that affect pocket protein association are transformation defective in different assay systems . High-risk HPV E7 proteins have the unique ability to destabilize the pocket proteins through a proteasome-dependent mechanism (Berezutskaya et al., 1997, Boyer et al., 1996). In addition to the LXCXE domain, sequences within the amino-terminal CR1 homology domain of high-risk HPV E7 are necessary for the ability to destabilize pocket proteins. High-risk HPV E7 proteins with mutations in the CR1 homology domain are also transformation deficient Hence, the ability of high-risk E7 proteins to destabilize pocket proteins is critical for cellular transformation (Fig.3.1.3.1 (A)). In addition to pRB binding and degradation, E7 has other cellular targets that are relevant to cellular transformation.HPV E7 can override the growth-inhibitory activities of cyclin dependent kinase inhibitors, including p21CIP1(Funk et al., 1997) and p27KIP1(Zerfass et al.,1996). Since these proteins are critical regulators of cell cycle arrest during keratinocyte differentiation, their inhibition by E7 may also contribute to the maintenance of a replication-competent cellular milieu in differentiated host epithelial cells. Similar to the case for the amino-terminal pRB binding site, the integrity of the carboxyl terminal E7 sequences that have been implicated in histone deacetylase binding are necessary for the viral life cycle (88). Hence, these interactions may contribute to transforming activities of high-risk HPV E7 proteins (Fig.3.1.3.1 (B)).

3.2 Materials and Methods

3.2.1 Docking and their types

The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or "pipeline", and consists of a number of distinct stages

Stages in drug discovery and development:

1. Discovery/Basic Research

Synthesis and Extraction- the process of identifying new molecules with the potential to produce a desired change in a biological system

Biological Screening and Pharmacological Testing- studies to explore the pharmacological activity and therapeutic potential of compounds

2. Preclinical Testing

Toxicology and Safety Testing- tests to determine the potential risk a compound poses to humans and the environment, involve use of animals, tissue cultures or other test systems

Pharmaceutical Dosage Formulation and Stability - the process of turning an active compound into a form and strength suitable for human use

3. Regulatory Review : IND

Application to regulatory authority to use compound in human testing. In

the US the compound is then called an Investigational New Drug (IND)

4. Phase I Clinical Trials

Testing of a new compound in 20-80 healthy human volunteers to determine tolerance, pharmacological effects, and absorption, distribution, metabolism and excretion (ADME) patterns

5. Phase II Clinical Trials

Trials in 100-300 patients with the targeted condition to determine effectiveness in treating disease or medical condition and short term risks

6. Phase III Clinical Trials

Trials on 1000-5000 patients to determine clinical benefit and incidence of adverse reactions

7. Process Development for Manufacturing and Quality Control

Engineering and manufacturing design activities to establish capacity to produce in large volumes and to ensure stability, uniformity and overall quality

8. Bioavailability Studies

Use of healthy volunteers to show that formulation used in trials is equivalent to product to be marketed

9. Regulatory Review: NDA

Application for approval to market a new drug. In the US this is called a New Drug Application (NDA)

10. Phase IV

Post marketing trials to identify undetected adverse effects and long term

morbidity and mortality profile

Molecular recognition plays a key role in promoting fundamental biomolecular events such as enzyme-substrate, drug-protein and drug-nucleic acid interactions. Detailed understanding of the general principles that govern the nature of the interactions (van der Waals, hydrogen bonding, electrostatic) between the ligands and their protein or nucleic acid targets may provide a conceptual framework for designing the desired potency and specificity of potential drug leads for a given therapeutic target. Practical application of this knowledge requires structural data for the target of interest and a procedure for evaluating candidate ligands. There are various computational docking methods are available (Kuntzet et al., 1982, DesJarlais et al., 1988, Rarey et al., 1996, Jones et al., 1997. Abagyan et al.,1994). These provide one approach to the ranking of potential ligands with respect to their ability to interact with a given target.

Computational docking of a small molecule to a biological target involves efficient sampling of possible poses of the former in the specified binding pocket of the latter in order to identify the optimal binding geometry, as measured by a user-defined fitness or score function. X-ray crystallography and NMR spectroscopy continue to be the primary source of 3-dimensional structural data for protein and nucleic acid targets. In favorable cases where proteins of unknown structure have high sequence homology to known structures, homology modeling can provide a viable alternative by generating a suitable starting point for 'in silico' discovery of high affinity ligands. Over the last few years a vast amount of effort has been directed toward developing efficient docking methods and scoring functions as tools for the identification of lead compounds.

The complexity of computational docking increases in the following order: (a) rigid body docking, where both the receptor and small molecule are treated as rigid. (b) flexible ligand docking, where the receptor is held rigid, but the ligand is treated as flexible; and (c) flexible docking, where both receptor and ligand flexibility is considered. Thus far, the most commonly used docking algorithms use the rigid receptor/flexible ligand model. The principal docking methods that are used extensively employ search algorithms based on Monte Carlo, genetic algorithm, fragment-based and molecular dynamics. Some programs that are well-suited for high throughput docking of a large database of molecules include: DOCK (Kuntzet et al., 1982, DesJarlais et al., 1988), FlexX (Rarey et al., 1996), GOLD (Jones et al., 1997), and ICM (Abagyan et al., 1994).

3.2.2 Softwares procurement for active site identification

The In silico interaction study for the active site identification was carried out by the following softwares.

1. Ligsite: online software for active site prediction (http://gopubmed2.biotec.tudresden.de/cgibin/index.php)

2. Deep view /swiss -pdb Viewer3.7: for the visualization of proteins

3-D structure and active site identification in targets.

3.2.3 Softwares procurement for docking study

Python -2.4.4: As supporting tool for the docking process

Autodock Tools-1.5.1: for the docking progress

Cygwin: As supporting tool for the docking process

3.2.4 Softwares procurement for visualization of target ligand interactions

Chimera-1.2470 (win 32): for the visualization of proteins and ligands interaction in the form of H-Bonds.

The NMR structure of oncoprotein HPV 16 E6 was downloaded from the Brookhaven Protein Data Bank (PDB) with PDB entry of 2FK4. Three Pockets were identified in 2fk4 by the LIGSITE. The grid space taken for 3 pockets was 1.0 Angstrom and the probe radius for potential binding site was 5.0 Angstrom. All 3 pockets were being visualized by Swiss-Pdb viewer (http://www.expasy.org/spdbv/). Finding of 3 nearest residues to these pockets was also determined within the range of 5 A°.

It was found that GLY 8, ARG 25 and GLN 46 are the closest residues to PKT 1, PKT 2 and PKT 14 in HPV 16 E6 (PDB CODE: 2FK4) respectively (Fig.3.3.1). GLN44, LYS 57 and LEU77 of HPV E7 (PDB CODE: 2B9D) of chain-B were the closest residue (Fig. 3.3.2).

Fig. 3.3.1 Three pockets of (PDB ID: 2FK4) showing closest residue GLY 8, ARG 25 and GLN 46 in range of 5 A°.

Fig. 3.3.2 Three pockets of (PDB ID: 2B9D) showing closest residue GLN44, LYS 57 and LEU77 in range of 5 A°.

Curcuminoids and its analogs (Table: 2.6.1) were generated in PDB file format and were docked separately on three pockets of both proteins by Autodock 4.0. Crystal structures of known target protein of HPV 16 E6 (PDB id: 2FK4) and HPV E7 (PDB CODE: 2B9D) were retrieved from PDB (Protein Data Bank).

The docking study parameters by Autodock 4 includes grid space 60Ã-60Ã-60 A°. Genetic algorithms method was used as searching method to predict best binding confirmation. In GA method total 10 runs with population size 150 and 2500000 evaluation were used in docking process by Autodock4 to predict the best interactions (Fig. 3.3.3). All ligands were docked on the all three residues of HPV 16 E6 and E7 proteins.

Fig. 3.3.3: Search parameters through Genetic algorithms in docking analysis by Autodock 4.0 for Ligands

3.3 Results

3.3.1 Docking parameters

HPV 16 E6 and E7 proteins in silico docking study were done by the following parameters by Autodock 4.0 programm.

Run (Out of 10 Run in which the minimum energy was calculated)

Minimum Binding energy

Inter molecular H-Bond

Intra molecular H-Bond

3.3.2 Docking results of HPV 16 E6 and E7 protein with ligands by Autodock 4.0

There were 10 runs for each docking positions and the minimum binding energy run was taken for best docking on HPV 16 E6 (PDB ID: 2FK4). The ligands were visualized by Chimera for the number of inter and intra H-Bonds (Table-3.3.1). The three active site were docked by Autodock 4.0 programm in which shows Curcumin exhibits maximum number i.e. 10 of intra molecular H-bonding on 1st active site with residue GLY 8 which is an indication of highest potency while Cholorogenic acid and cyclocurcumin indicated -7.13 and -4.41 Kcal/mol binding energy on GLY 8 residue respectively. Piperic acids show 9 intra molecular H Bonds after curcumin on GLY 8 target residue.

The second residue ARG 25 shows best binding energy for Cholorogenic acids, cyclocurcumin and demethoxycurcumin which were -7.28, -6.88 and-6.30 Kcal/mol respectively. Maximum H-Bond interaction bisdemethoxy curcumin and demethoxy curcumin exhibits 9, 8, 8 respectively.

The third residue GLN 8 shows best binding for bisdemethoxy curcumin, cholorogenic acid, caffeic acid and cyclocurcumin shows +24.69, -6.38, -5.49, and -5.44 Kcal/mol respectively (Table-3.3.1).

The docking study on HPV E7 (PDB ID: 2B9D) shows that on first residue GLN 44 cholorogenic acid, quercetin, cyclocurcumin, bisdemethoxy curcumin -7.66, -5.83, -5.47 and -5.17 Kcal/mol binding energy respectively. The Highest intra molecular H-Bonds found in capsaicin, eugenol, Bis demethoxy curcumin, curcumin dipiperoyl ester i.e, 43, 19, 16 and 16 respectively.

Second residue LYS 57 shows maximum binding for the cholorogenic acid, quercetin, ferulic acid, cyclocurcumin -5.80,-5.49,-5.07 and -5.36 Kcal/Mol respectively. The intra-molecular H Bond in case of quercetin, cholorogenic acid, ferulic acid, 45, 41, and 35 respectively. Third residue of HPV E7 shows maximum binding for cyclocurcumin, cholorogenic acid, and dibenzoyl methane shows -6.03,- 5.63 and -5.58 k cal/mol respectively. Intra-molecular H Bonds maximally shows in 44, 43, 42 for piperic acid, Yakuchinone B and Dehydro Zingerone respectively (Table -3.3.2).

Ligands

No of H bond on 1st active site

(Residue 8: GLY )

No of H bond on 2st active site

(Residue 25: ARG)

No of H bond on 3st active site

(Residue 46: GLN)

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Curcumin

5

63.44

4

10

3

-5.62

8

3

4

-4.66

3

1

Bisdemethoxy Curcumin

6

-4.09

5

1

6

-5.90

9

3

3

+24.69

4

6

Caffeic acid

2

-4.01

5

4

3

-5.06

4

0

2

-5.49

3

0

Capsaicin

6

19.05

0

7

4

-3.99

2

1

8

-3.53

1

7

Cassumunins A

7

-0.17

3

4

10

-3.73

3

3

1

-2.37

4

6

Cassumunins B

6

-2.72

0

7

9

-3.41

2

1

7

-3.71

1

2

Cholorogenic

Acid

6

-7.13

4

2

2

-7.28

5

4

8

-6.38

3

2

Curcumin dipiperoyl ester

1

-2.20

3

7

7

-3.58

5

4

1

-2.64

3

0

Cyclocurcumin

5

-4.41

4

4

5

-6.88

3

2

3

-5.44

2

1

Dehydro

Zingerone

10

-3.99

3

1

1

-4.17

3

0

3

-3.99

3

3

Demethoxy

Curcumin

2

-3.81

3

3

8

-6.30

8

3

9

-4.93

3

1

Diaryl

Pentanoids

9

-4.27

3

4

4

-6.55

2

3

3

-4.48

4

6

Diaryl

Pentanoids II

6

-2.45

2

3

7

-3.51

2

4

10

-3.88

1

6

Dibenzoyl

Methane

9

-4.20

0

2

4

-5.40

0

0

4

-5.39

1

3

Dihydro guarietic acid

10

-3.99

1

3

1

-4.17

3

1

3

-3.99

2

7

Eugenol

10

-3.99

2

1

3

-4.30

2

0

2

-3.36

2

2

Ferulic acid

7

-4.86

2

1

1

-5.70

5

0

2

-5.81

3

3

Iso Eugenol

8

-3.98

2

1

7

-4.36

2

1

1

-3.32

2

1

Piperic acid

2

-3.08

3

9

2

-4.20

5

1

9

-3.68

3

3

Quercetin

( Flavnoids)

10

-4.05

3

3

7

-5.30

6

1

4

-5.23

5

0

Yakuchinone A

7

-0.17

0

2

10

-3.73

4

3

1

-2.37

0

1

Yakuchinone B

6

-2.72

2

1

9

-3.41

2

1

7

-3.71

2

6

Zingerone

2

-3.67

1

1

8

-4.27

3

0

10

-3.94

4

3

Table 3.3.1: Results of Docking study on ligand HPV16 E6 protein (PDB ID: 2FK4)

No of H bond on 1st active site

(Residue 44: GLN )

No of H bond on 2st active site

(Residue 57: LYS)

No of H bond on 3st active site

(Residue 77: LEU )

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Run

Minimum Binding energy

Inter

molecular

H-Bond

Intra molecular

H-Bond

Curcumin

6

-4.89

5

9

9

-4.08

5

7

10

-4.74

9

33

Bisdemethoxy Curcumin

2

-5.17

3

16

4

-4.25

4

9

6

-4.69

5

14

Caffeic acid

7

-4.68

6

6

3

-4.59

5

4

7

-4.23

7

37

Capsaicin

2

-3.71

6

43

6

-2.94

3

11

6

-3.90

2

6

CassumuninsA

2

-4.35

4

8

2

-3.37

5

23

7

-4.54

0

16

CassumuninsB

7

-4.32

3

7

2

-2.35

1

20

7

-5.09

4

11

Cholorogenic Acid

2

-7.66

9

9

8

-5.80

13

41

2

-5.63

7

41

Curcumin dipiperoyl ester

2

-5.02

3

16

4

-3.46

6

20

7

-4.15

4

36

Cyclocurcumin

10

-5.47

5

10

4

-5.36

3

11

1

-6.03

1

6

Dehydro Zingerone

7

-3.83

5

7

3

-3.40

3

9

3

-3.96

6

42

Demethoxy Curcumin

10

-5.15

7

11

5

-3.92

3

7

3

-4.36

6

11

Diaryl Pentanoids

5

-5.83

2

14

6

-5.38

11

2

2

-5.47

2

11

DiarylPentanoids II

1

-4.46

2

12

7

-4.00

5

40

4

-4.45

2

16

Dibenzoyl methane

5

+0.23

0

0

4

-4.72

7

7

4

-5.58

0

6

Dihydro guarietic acid

2

-4.68

4

8

3

-4.48

2

7

2

-4.94

2

7

Eugenol

2

-3.54

5

19

8

-3.82

2

7

7

-3.82

4

39

Ferulic acid

5

-5.37

7

7

4

-5.07

6

35

6

-5.10

6

35

Flavnoids

2

-4.47

0

6

3

-3.80

2

15

6

-4.44

2

37

Iso Eugenol

9

-3.73

5

16

4

-3.36

0

34

4

-3.82

3

34

Piperic acid

8

-4.0

6

10

9

-4.12

5

30

2

-4.17

6

44

Quercetin (Flavnoids)

9

-5.83

8

11

9

-5.49

9

45

4

-5.48

6

40

Yakuchinone A

6

-4.65

5

9

5

-3.28

4

27

3

-4.24

2

9

Yakuchinone B

1

-4.56

4

15

6

-3.88

1

10

6

-4.91

5

43

Zingerone

4

-3.99

2

11

9

-3.29

4

9

10

-4.01

6

31

Table 3.3.1: Results of Docking study on ligand HPV E7 protein (PDB ID: 2B9D)

3.2.3 Docking analysis of HPV 16 E6 and E7 protein with ligands by Scigress 7.7.0.47

The Automatic docking (Blind docking) analysis was performed by Scigress 7.7.0.47. The docking analysis includes the preparation of ligands, cleaning of targeted proteins HPV 16 E6 and E7and optimization of ligands. The results were predicted on the basis of PMF score - dock flexible ligand in rigid active site (kcal/mol) for both the proteins.

No.

Name of Ligands

PMF score for E6 Protein

(PDB ID: 2FK4)

PMF score for E6 Protein

(PDB ID: 2B9D)

1.

Bis Demethoxy Curcumin

-51.503

-54.278

2.

Caffeic acid

-62.267

-42.377

3.

Capsaicin

-50.654

-25.771

4

Cholorogenic Acid

-99.782*

-68.327*

5.

Cassumunins A

-44.556

-25.806

6.

Cassumunins B

-55.462

-23.38

7.

Curcumin

-85.699*

-64.03*

8.

Curcumin dipiperoyl ester

-74.859

-34.066

9.

Cyclocurcmin

-54.515

-44.148

10.

Demethoxy Curcumin

-78.974*

-64.011*

11.

Dehydro Zingerone

-41.759

-32.664

12.

Diaryl Pentanoids

-61.251

-22.691

13.

Diaryl Pentanoids II

-54.224

-22.753

14.

Dihydro guarietic acid

-65.578

-45.978

15.

Eugenol

-37.275

-26.938

16.

Ferulic acid

-46.627

-43.631

17.

Piperic acid

-60.454

-42.662

18.

Quercetin

-67.679

-56.059

19.

Yakuchinone A

-45.20

-47.719

20.

Yakuchinone B

-53.811

-50.084

21.

Zingerone

-40.826

-33.878

*Ligands shows the best binding effect on targets by PMF

Table: 3.2.1 Automated Docking analysis results by Scigress 7.7.0.47. on HPV 16 E6 and E7 Protein

The docking study of protein and ligands were performed by converted from .pdb to .csf format using standard functionality from the Workspace module of Scigress Explorer 7.7.0.47. The both targets (PBD ID: 2FK4 and 2B9D) was docked with ligands (Table: 3.2.1) and was scored on the basis of automatic dock, which scores a ligand in an active site from the workspace application using a potential of mean force (PMF) with a genetic docking algorithm.

The docking results shows highest binding in terms of PMF score for Cholorogenic acid -99.782 and -68.327, curcumin -85.699 and -64.03, demethoxy curcumin -78.97 and -64.011 on 2FK4 and 2B9D respectively.

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