INTRODUCTION:

Heat shock proteins (Hsps) are a class of chaperone proteins that shield the normal cells against stimuli which influence injury to the cell and maintain the intracellular protein homeostasis by promoting the proper folding and thus stabilizing the protein environment ^[1]. In deadly disease condition like cancer, the elimination of Hsps function contributes to drastic protein damage in the cells. Cancer cells can cope up with different strenuous environment that can likely lead to adverse outcomes, this unfavourable stress constitute acidosis, hypoxia, high interstitial pressure and nutrient deprivation ^[2].

Received on 17.06.2015 Modified on 24.06.2015

Research J. Pharm. and Tech. 8(9): Sept, 2015; Page 1199-1204

DOI: 10.5958/0974-360X.2015.00219.X

Hsp90 significantly displays an extensive possibility of the proliferation of the malignant cells in cancer and devoid the tumor cells of the apoptotic death ^[3]. Various signal transduction pathways are hindered by the inhibition of Hsp90 functions, which are pivotal for the malignant cell progression and survival ^[4]. According to certain research studies, targeting Hsp90 for its inhibition in pancreatic cancer cells has shown to decrease tumor proliferation ^[5,6]. Statistics indicates that pancreatic cancer displays increased mortality and is one among the top five causes of death from cancer ^[7]. In the epidemiological analysis, the regional differences have an impact on the distributions of the disease. Comparing the status of Alcohol related pancreatitis, it is prevalent in the West and Japan, than other Asian countries. Chronic pancreatitis is mostly restricted to tropical countries with the statistics of 20-125 per 100,000 persons reported in 2 parts of South India^[8,9]. Depending on the exocrine or endocrine functions of the pancreas the cancer types varies. In the present investigation, an attempt has been made to study the in silico efficacy of phytochemical constituents for the chemoprevention and action on Hsp90 inhibition to tackle pancreatic cancer ^[10].

Computer Aided Drug designing is a diverse discipline playing a major role in the field of research and drug discovery. The conventional method of development of molecules by synthesis is preceded by the use of drug designing techniques computationally^[11]. Various imperative steps are involved in drug designing such as structure based and ligand based drug designing. In silico advancement based on ligand drug designing have been performed for the lead molecule development. The necessary molecular characteristics of the ligand have been generated computationally. The various interactions between the proteins and the ligand are analyzed with the protein target 4L94 ^[12]. The 35 phytochemicals used for the present in silico analysis were Curcumin, Allicin, Pinene, Tetrahydrocannabinol, Astragalin, Arginine, Scopoletin, Baicalin, Stylopine, Carvacrol, Quercetin, Thymol, Guaiaretic acid, Coumarin, Chlorogenic acid, Taraxacin, Ascorbic acid, Protocatechuic acid, Withaferin A, Triptolide, Ursolic acid, Cucurbitane, Thymoquinone, D-Carvone, Eugenol, Ellagic acid, Lapachol, Genistein, Emodin, Chelidonine, Caffeine, Catechin, Geraniol, Myrcene and Niacin. The standard drug used for the analysis was a nucleoside analog Gemcitabine.

Drug likeness is a parameter that helps to characterize compounds based on the varied molecular properties like hydrophobic character, electron distribution, hydrogen bond formation, size and adaptability of the molecule in conjunction with the presence of various pharmacophores. The lead molecule affects the bioavailability, protein binding property, absorption mechanism, toxicity and stability of the compound^[13]. Molinspiration was accustomed to calculate parameters like relative molar mass, log P, number of hydrogen bond donors or acceptors which are essential to eliminate non-drug like molecules. Sophisticated Bayesian statistics was employed in Molinspiration for the calculation of the physicochemical properties of the lead molecules. Log P (octanol/water partition coefficient) and Total Polar Surface Area (TPSA) was calculated by the sum of fragment based contributions and correction factors^[14]. TPSA is observed to be an indispensable descriptor influencing the drug absorption, CaCO₂ permeability and blood-brain barrier penetration. Molecular Volume calculation is predicted based on cluster contributions and are fully optimized by the semi empirical AM1 technique. Rule of 5 is a set of molecular descriptors used by Lipinski in formulating the Rule of 5. The rule states that most "drug-like" compounds have log P <= 5, molecular weight <= 500, number of hydrogen bond acceptors <= 10 and number of hydrogen bond donors <= 5 ^[15]. Molecules that do not pursue more than one of these rules may have problems with bioavailability. Topological parameter used for measuring molecular flexibility is the Number of Rotatable Bonds (nrotb) ^[1^6]. Rotatable bond is defined as any single non-ring bond, bounded to non-terminal heavy (non-hydrogen) atom. The rule elucidates the molecular properties like absorption, distribution, metabolism and excretion (ADME).

MATERIALS AND METHODS:

The X-ray crystallographic structure of Human Hsp90 with S46 (PDB ID 4L94) protein was obtained from RCSB protein data bank at a resolution of 1.65 Å ^[17]. Water molecules, ligands and other hetero atoms were removed from the protein molecule. The hydrogen atoms were added to the protein using CHARMm force field. The protein molecules were subjected to stability studies computationally. The primary and secondary structure of the protein were studied^{[18, 19]}. Parameters studied were Extinction coefficients, estimated half-life, instability index, aliphatic index and Grand average of hydropathicity (GRAVY)^[20]. The Extinction coefficients indicate how much light a protein absorbs at a specific wavelength measured in units of M^-1 cm^-1, at 280 nm in water. The half-life is the prediction of time it takes for half of the amount of protein in a cell to disappear after its synthesis in the cell. The N-terminal of the sequence considered is M (Met). ProtParam is based on the N-end rule which correlates the half-life of a protein to the identity of its N-terminal residue^[21]. The aliphatic index of a protein is defined as the relative volume occupied by aliphatic side chains such as alanine, valine, isoleucine, and leucine. The aliphatic index gives an idea on the thermo stability of globular proteins. The GRAVY value is calculated based on the hydropathy values of all the amino acids in the sequence for a peptide or proteins concerned. Detection of the location of the protein was performed computationally using CELLO v.2.5: subcellular localization predictor which predicts the subcellular localization sites of proteins based on their amino acid sequences ^[22].

The ligand structures of naturally occurring compounds such as Curcumin, Allicin, Pinene, Tetrahydrocannabinol, Lignan, Astragalin, Arginine, Scopoletin, Baicalin, Stylopine, Carvacrol, Quercetin, Thymol, Guaiaretic acid, Coumarin, Chlorogenic acid, Taraxacin, Ascorbic acid, Protocatechuic acid, Withaferin A, Triptolide, Ursolic acid, Cucurbitane, Thymoquinone, D-Carvone, Eugenol, Ellagic acid, Lapachol, Genistein, Emodin, Chelidonine, Caffeine, Catechin, Geraniol, Glaberene, Myrcene and Niacin along with the standard drug Gemcitabine were generated using Marvin Sketchand saved in the PDB format^[23]. The various molecular and bioactivity properties of the ligands were studied using Molinspiration. The ligands and proteins were subjected to energy minimization and finally to understand the interactions between the ligands and Hsp90 protein and to explore their binding mode, docking analysis was performed using Argus lab 4.0.1 software which operates on Lamarckian genetic algorithm^[24].

RESULTS AND DISCUSSION:

The preliminary in silico analysis was performed to check the parameters such as the primary and secondary structure analysis of the protein 4L94. Primary analysis involved the calculation of Extinction coefficient, estimated half-life, instability index, aliphatic index and Grand average of hydropathicity (GRAVY) values. The number of amino acids of the protein were found to be 228 with the molecular weight as 25627.8 g. The theoretical pI was found to be 4.69 with the total number of negatively charged residues (Asp + Glu) as 41 and the total number of positively charged residues (Arg + Lys) as 25. The total number of atoms was calculated as 3595. The Extinction coefficient was analyzed to be 15930 at Abs 0.1% (=1 g/l) 0.622. The N-terminal of the sequence considered is D (Asp). The estimated half-life was 1.1 hours (mammalian reticulocytes, in vitro), 3 minutes (yeast, in vivo) and >10 hours (Escherichia coli, in vivo). The instability index (II) was computed to be 34.25 and this classifies the protein as being in a stable state. The Aliphatic index was found to be 85.57 and therefore the protein demonstrates considerable thermo stability. Grand average of hydropathicity (GRAVY) value of -0.420 indicates that the protein was hydrophilic in nature. The target protein secondary structure analysis was performed. The Alpha helix (Hh) was found to be 93 (40.79%), 3₁₀ helix (Gg), Pi helix (Ii), Beta bridge (Bb) and Bend region (Ss) was 0%. The Extended strand (Ee) was 48 (21.05%), Beta turn (Tt) was found to be 22 (9.65%) and Random coil (Cc) 65 as 28.51%. Secondary structure analysis showed considerable stability of the protein. The 4L94 protein was found in large quantity in cytoplasm.

TABLE 1: MOLECULAR PROPERTIES

SI NO	Constituent	LogP	TPSA	nATOM	MW	nON	n OHNH	n Violations	nrotb	Volume
1	Curcumin	3.214	96.223	27	368.385	6	3	0	7	331.831
2	Allicin	2.064	17.071	9	162.279	1	0	0	5	145.506
3	Pinene	3.542	0	10	136.238	0	0	0	0	151.814
4	Tetrahydrocannabinol	7.558	29.462	25	342.523	2	1	1	4	356.467
5	Astragalin	0.125	190.275	32	448.38	11	7	2	4	364.188
6	Arginine	-3.632	125.224	12	174.204	6	7	1	6	164.147
7	Scopoletin	1.329	59.673	14	192.17	4	1	0	1	162.15
8	Baicalin	0.545	187.118	32	446.364	11	6	2	4	358.352
9	Stylopine	3.041	40.174	24	323.348	5	0	0	0	278.446
10	Carvacrol	3.82	20.23	11	150.22	1	1	0	1	158.57
11	Quercetin	1.683	131.351	22	302.238	7	5	0	1	240.084
12	Thymol	3.34	20.23	11	150.22	1	1	0	1	158.57
13	Guaiaretic acid	4.44	58.92	24	328.41	4	2	0	6	316.75
14	Coumarin	2.01	30.21	11	146.15	2	0	0	0	128.59
15	Chlorogenic acid	-0.45	164.74	25	354.31	9	6	1	5	296.27
16	Taraxacin	2.56	43.38	18	242.27	3	0	0	0	220.04
17	Ascorbicacid	-1.4	107.22	12	176.12	6	4	0	2	139.71
18	Protocatechuic acid	-0.69	77.76	11	160.17	4	3	0	1	145.72
19	Withaferin A	3.86	96.36	34	470.61	6	2	0	3	442.38
20	Triptolide	0.57	84.12	26	360.41	6	1	0	1	310.97
21	Ursolic acid	6.79	57.53	33	456.71	3	2	1	1	471.49
22	Cucurbitane	9.13	0	30	414.76	0	1	1	5	470.36
23	Thymoqunone	1.9	34.14	12	164.2	2	0	0	1	161.1
24	D-Carvone	2.51	17.07	11	150.22	1	0	0	1	159.48
25	Eugenol	2.1	29.46	12	164.2	2	1	0	3	162.14
26	Gallic acid	0.59	97.98	12	170.12	5	4	0	1	135.1
27	Lapachol	3.38	54.37	18	244.29	3	1	0	3	230.16
28	Genistein	3.06	50.44	18	238.24	3	1	0	1	208.01
29	Emodin	2.08	115.05	20	272.21	6	4	0	0	214.65
30	Chelidonine	2.31	60.4	26	359.42	6	1	0	0	321.74
31	Caffeine	0.4	64.74	15	208.22	6	1	0	0	184.18
32	Catechin	1.3	11.37	21	290.27	6	5	0	1	244.14
33	Geraniol	3.202	20.28	11	150.22	1	1	0	1	158.57
34	Myrcene	3.994	0	10	136.238	0	0	0	4	162.24
35	Niacin	0.273	50.191	9	123.111	3	1	0	1	106.888
36	Gemcitabine	-1.603	110.61	18	263.2	7	4	0	2	203.356

TPSA: total polar surface area; natoms: number of atoms; MW: molecular weight; nON: number of hydrogen bond acceptors; nOHNH: number of hydrogen bond donors; nrotb: number of rotatable bonds

TABLE 2 : BIOACTIVITY SCORE

SI NO	CONSTITUENT	GPCR LIGAND	ION CHANNEL MODULATOR	KINASE INHIBITOR	NUCLEAR RECEPTOR LIGAND	PROTEASE INHIBITOR	ENZYME INHIBITOR
1	Curcumin	-0.09	-0.39	-0.13	-0.02	-0.09	-0.12
2	Allicin	-2.51	-2.26	-2.95	-2.66	-1.4	-1.52
3	pinene	-0.76	-0.22	-1.37	-0.23	-1.17	0.04
4	Tetrahydrocannabinol	0.27	0.21	-0.24	0.74	0.09	0.31
5	Astragalin	0.18	0.01	-0.13	0.05	0.11	0.41
6	Arginine	0.39	0.83	-0.74	-1.43	0.79	0.5
7	Scopoletin	-1	-0.65	-0.95	-0.81	-1.16	-0.24
8	Baicalin	0.16	-0.08	-0.26	0.36	0.07	0.42
9	Stylopine	0.3	0.06	-0.38	-0.38	-0.16	0.02
10	Carvacrol	1.02	0.51	1.15	0.7	1.25	0.56
11	Quercetin	-0.06	-0.19	0.28	0.36	-0.25	0.28
12	Thymol	1.05	0.53	1.29	0.78	1.34	0.57
13	Guaiaretic acid	0	0.04	0.11	0.18	0.05	0.04
14	Coumarin	1.44	0.86	1.57	1.42	1.43	0.58
15	Chlorogenic acid	0.29	0.14	0	0.74	0.27	0.62
16	Taraxacin	0.43	0.27	0.43	0.4	0.5	0.07
17	Ascorbicacid	0.56	0.28	0.86	0.64	0.61	0.27
18	Protocatechuic acid	0.55	0.01	1.18	0.4	0.17	0.05
19	Withaferin A	-1.4	-0.31	-1.27	-1.47	-1.44	-0.4
20	Triptolide	0.11	0.09	-0.43	0.4	0.24	0.86
21	Ursolic acid	0.25	0.04	-0.37	0.74	0.14	0.58
22	Cucurbitane	0.17	0.07	-0.35	0.55	0.02	0.4
23	Thymoqunone	-1.4	-0.31	-1.27	-1.47	-1.44	-0.4
24	D-Carvone	-1.23	-0.3	-2.51	0.54	-1.21	-0.45
25	Eugenol	-0.86	-0.36	-1.14	-0.78	-1.29	-0.41
26	Gallic acid	-0.77	-0.26	-0.88	-0.52	-0.94	-0.17
27	Lapachol	-0.26	-0.15	-0.09	0.28	-0.34	0.42
28	Genistein	-0.34	-0.64	-0.27	-0.05	-0.89	-0.01
29	Emodin	-0.14	-0.14	0.07	0.18	-0.21	0.21
30	Chelidonine	0.49	0.45	-0.13	0.02	0.15	0.05
31	Caffeine	-0.53	-0.98	-1.07	-2.1	-1.22	-0.22
32	Catechin	0.41	0.14	0.09	0.6	0.26	0.47
33	Geraniol	-0.6	0.07	-1.32	-0.2	-1.03	0.28
34	Myrcene	-1.11	-0.33	-1.51	-0.45	-1.31	-0.07
35	Niacin	-2.51	-1.44	-2.27	-2.69	-2.42	-0.44
36	Gemcitabine	0.58	0.11	0.33	-1.01	0.15	1.06

TABLE 3 : DOCKING SCORE

SI NO	Constituent	Docking score
1	Curcumin	-8.91955
2	Allicin	-8.61425
3	pinene	-10.0848
4	Tetrahydrocannabinol	11.5478
5	Astragalin	-6.07642
6	Arginine	-6.01481
7	Scopoletin	-7.09145
8	Baicalin	-5.04062
9	Stylopine	-7.90556
10	Carvacrol	-9.20977
11	Quercetin	-6.47775
12	Thymol	-9.84185
13	Guaiaretic acid	-8.43062
14	Coumarin	-8.64551
15	Chlorogenic acid	-7.44001
16	Taraxacin	-9.69158
17	Ascorbicacid	-5.16834
18	Protocatechuic acid	-6.94978
19	Withaferin A	-8.46136
20	Triptolide	-7.73722
21	Ursolic acid	-9.41651
22	Cucurbitane	-13.3176
23	Thymoqunone	-8.84729
24	D-Carvone	-9.866
25	Eugenol	-8.85002
26	Gallic acid	-6.94853
27	Lapachol	-9.55895
28	Genistein	-7.80456
29	Emodin	-9.12761
30	Chelidonine	-7.42197
31	Caffeine	-4.7599
32	Catechin	-7.23162
33	Geraniol	-9.58232
34	Myrcene	-10.1389
35	Niacin	-6.55211
36	Gemcitabine	-5.15331

It was seen that ligands Cucurbitane, Myrcene and Pinene were comparable with that of the standard drug Gemcitabine. The structures of the final ligands are represented in Fig 1. Other phytochemical ligands with moderate in silico activities as Hsp protein inhibitors were Carvacrol, Thymol, Taraxacin, Ursolic acid, D-Carvone, Lapachol, Emodin and Geraniol.

Cucurbitane: 9,10,14-Trimethyl-4,9-cyclo-9,10-secocholestane

Myrcene:7-methyl-3methylideneocta-1,6-diene

Pinene:6,6-dimethyl-2-methylidenebicyclo[3,1,1]heptane

Fig 1: Structures of phytochemicals

CONCLUSION:

The computational screenings have established to be helpful to analyze and elucidate the drug likeness activity of the phytochemicals for the treatment of certain disease conditions. In the present study the in silico analysis signify the selection of natural compounds such as Cucurbitane, Myrcene and Pinene as they elicited considerable binding interaction in inhibiting Heat shock proteins (Hsps) for targeting pancreatic cancer which was comparable with that of the standard drug like Gemcitabine. Other phytochemical ligands with moderate in silico activities as Hsp protein inhibitors were Carvacrol, Thymol, Taraxacin, Ursolic acid, D-Carvone, Lapachol, Emodin and Geraniol. The inhibition of chaperone proteins can prove to be effective in case of individuals with pancreatic cancer. Further investigations on naturally occurring compounds could act as a source for inhibition of pancreatic cancer. Phytochemical ligands can prove to be essential for the development of potent inhibitors and effective chemical entities for the prevention and treatment of cancer.

ACKNOWLEDGEMENTS:

We are thankful to Department of Chemistry, Amrita School of Pharmacy, Amrita Viswa Vidyapeetham University, for providing necessary facilities for the study.

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