Int J of Com Bio and Bioinfo

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Please cite this article as: OgunwaTomisin Happy, Molecular interaction and inhibitory potential of selected plant-derived polyphenolic compounds with

Human α-amylase, Int J Com Bio & Bioinfo, (2018); 1(1), 21 - 26.

Molecular interaction and inhibitory potential of selected plant-derived polyphenolic

compounds with Human α-amylase OgunwaTomisin Happy 1, 2*

AdekunleAjasin University, Akungba - Akoko, Ondo State, Nigeria

A b s t r a c t

In thi s study, i n si l i co experi mental approach was used to assess mol ecul ar i nteract ion and i nhibi t ory pot ent i al of pl ant -derived

sc i rpusi n B , cassi garol E , epi cat echingal l ate and sarcovi ol in on human α-amyl ase and compared t o st andard drugs used for

t reat ment of t ype 2 di abet es. Comput at i onal l igand docking revealed t hat these compounds po ssessed h i gher bi nding affi ni t y

(-9.2Kcal /mol , -9.0Kcal /mol , -8.9Kcal/mol and -8.3Kcal/mol respect i vel y) and t hus, hi gher i nhibi t ory pot ent i al s on humanα-amyl ase

as compared with reference compounds (acarbose and mi gl i t ol ) having -7.5Kcal /mol and -5.1Kcal /mol binding energy

respect i vel y. Al l the pol yphenol ic compounds ent ered i nt o t he bindi ng pocket found i n t he act ive s i te regi on where t hey

int eract ed with ami no aci d resi dues. Observat i on of the l igand -receptor int eract i on showed t hat hydrogen bond, hydrophobic,

π -π i nt eract i ons and van der waal forces are i nvolved. Ami no aci d resi dues ASP -197, HIS-305, GLN-63, ARG-195, GLU-233, HIS-201,

ASN-105, HIS-101 and THR-163 found wit hin t he4Å regi on on the bi nding si te st abi l i zed t he int eract i on of phenol ic compounds wit h

α-amyl ase as t hey mai nl y cont ribut e t o t he bonds format i on. The ADM E t est al so reveal ed t hat t he compounds demonstrat ed t he

propert ies requi red t o ful fi l Li pinski rul e, mak ing t hem promi si ng t herapeut ic agent s. Thi s work hence val idat es t hese natura l

compounds as pot ent ial natural inhi bi tors of human α-amyl ase rel at ed t o t ype 2 di abetes, cont ribut es t o t he understandi ng of

mol ecul ar mechani sm of i nhibi t i on exerted by natural phenol ic compounds found in plant s and al so suggest s t hat t hey coul d be

useful as al t ernat ive t herapeut i c candi dat e for management of post prandial hypergl ycemi a.

*Cor responding A uthor

K eywords L icense Ar t i c l e Info

OgunwaTomisin Happy 1Centre for Bio-computing and Drug

Development, AdekunleAjasin University,

Akungba-Akoko, Ondo State, Nigeria

2Department of Biochemistry, AdekunleAjasin

University, Akungba-Akoko, Ondo State,

Nigeria

tomisin.ogunwa@aaua.edu.ng,

ogunwatomisin@gmail.com

Polyphenolic compounds

type 2 diabetes

in silico

α-amylase

molecular interaction

Received:13 Oct 2018

Accepted:30 Oct 2018

Published: 15 Nov 2018

INTRODUCTION

Medicinal plants serve as a depot of phytochemicals whose

various therapeutical potentials have been exploited by

ethnopharmacologists and researchers worldwide. The phenolic

compounds are a major class of phytochemical often claimed to

possess diverse biological properties such as antidiabetic,

antioxidant, anti-inflammatory, antiobesity and antitumor

effects[1-3]. Evidence abound that phenolic compounds

obtained from various medicinal plants are effective in

management of diseases and contribute immensely to

pharmacological status of most medicinal plants commonly used

across the globe[4,5]. Interestingly, the inhibitory potentials of

plant extracts on enzymes associated with various human

diseases (including diabetes) have been widely observed over

the years. Worthy of note also is the recent frantic efforts by

researchers seeking for safer and cheaper antidiabetic

therapeutic agents. This search became pertinent and continues

till date simply because the known antidiabetic drugs are

associated with undesirable side effects and high cost. Attention

of researchers are now drawn to the medicinal plants and natural

products that demonstrate antidiabetic potentials as a possible

source of cheap therapeutic agents with less or no adverse

effect[6,7]. From such medicinal plants, bioactive ingredients like

Scirpusin B, Cassigarol E, sarcoviolin and epicatechingallate have

been isolated[8].

Scirpusin B is one of the stilbene metabolites obtained from

plants such as Cyperus rotundas, Scirpusmaritus and

Callistemon rigidus. Its active biological properties have been

observed in various in vitro experiments. Available reports

showed that Scirpusin B possessed antioxidative, anti HIV and

antidiabetic effects[9-12] as well as protective role in UVB-

irradiated keratinocyte[11]. It is also a strong vasorelaxant

which increases coronary flow, via NO and vasodilating

prostanoids production, thereby preventing atherosclerosis

and cardiac events[13]. Cassigarol E has been shown to exert

antioxidative effect by scavenging free radicals and

antidiabetic properties[14,15]. The sarcoviolins also have the

biological abilities such as antitumor, antioxidative and

glucosidase inhibitory property [8,16,17] while

epicatechingallate has been reported as one of the most

effective cancer chemopreventive polyphenol found in

green tea[18]. Epicatechingallate, like most of the

gallatedcatechins, possesses capacity to inhibit alpha

amylase in in vitroexperiment[19-21]. The compound also

inhibits myeloperoxidase and induces expression of NAG-1, as

a means of growth inhibition and apoptosis in colon cancer

cells[22].

Type 2 diabetes mellitus is described as an array of

dysfunctions resulting from combination of resistance to insulin

International Journal of Computational Biology and

Bioinformatics (IJCOB) www.str ingsjournal.com

Int J of Com Bio and Bioinfo

22

action and characterised by hyperglycaemia. Strategies aimed

at lowering the postprandial hyperglycaemia, associated with

type 2 diabetes, has been a major therapeutic approach in the

management of the disease and this is often achieved via

reduction in rate of glucose absorption. This is achieved by

inhibition of α-amylase and α-glucosidase both of which are

carbohydrate-hydrolysing enzymes. The known α-amylase

inhibitors often completely block access of substrate to the active

site of the enzyme, hence preventing starch digestion. Among the

drugs employed for such treatment are acarbose, voglibose and

miglitol which act as inhibitors of α-amylase and α-glucosidase

[23]. However, the use of these drugs are associated with serious

and undesirable adverse effects such as diarrhea, flatulence,

severe abdominal pain, constipation, etc [7]. Hence, a search for

a relatively safer novel therapeutic agent, with less or no adverse

effects, is still encouraged. Moreover, the mechanism of action of

such novel substances must be completely elucidated. Therefore,

this research seeks to study, at molecular level, the mechanism of

interaction and inhibition exerted by the selected natural

polyphenolic compounds on diabetes type 2 related alpha

amylase.

MATERIAL AND METHODS

Selection and Preparation of Protein Structure

The “FASTA” file (Accession: AAA52280.1 GI:178567) for

humanpancreatic alpha amylase was obtained from Genbank

and used in homology modeling on the Swiss Model Server

(http://swissmodel.expasy.org). The coordinate file of template

from protein data bank (PDB ID: 4GQR) was employedin modeling

the 3D structure of human α-amylase. All water molecules and

ligand (myricetin) crystallized with the protein were deleted

before molecular docking procedures[24].

Structural Evaluation and Validation of the Model

The quality of modeled protein was assessed by PROCHECK

validation[25] and the Ramachandran plot was obtained using

Pdbsum database of the European Bioinformatic institute (EMBL-

EBI) (http://www.ebi.ac.uk/). Ramachandran statistic plot values,

Qmean score, LG-score, Maxsub, Z score as well as root mean

square deviation (RMSD) were also calculated using available

online servers.

Preparation of Ligands

A total of six (6) ligands used for docking study were selected from

the literature. Out of these compounds, four (4) were phenolic

compounds isolated from various medicinal plants while two (2)

were known α-amylase inhibitors: acarbose and miglitol which

were used as reference. The chemical structure of these

compounds (acarbose, miglitol, scirpusin B, cassigarol E,

epicatechingallate and sarcoviolin was obtained from PubChem

compound database[26] and prepared using Marvinsketch. The

Pubchem IDs of the compounds were 41774, 441314, 5458999,

5315729, 107905 and 24202820 for acarbose, miglitol, scirpusin B,

cassigarol E, epicatechingallate and sarcoviolin respectively. The

structures of these inhibitors were obtained from NCBI PubChem

Compound (http://www.ncbi.nlm.nih.gov/pccompound). Three-

dimensional optimizations of the ligand structures were done

before use in docking studies. The ligands were saved as MOL SD

format after optimization. These were docked into refined human

pancreatic α-amylase model using “LigandFit” in the AutoDock

4.2.

ADME Screening

ADME (Absorption, Distribution, Metabolism and Excretion)

screening for the polyphenolic compounds was done using

available online servers (http://www.sioc-ccbg.ac.cn/ and

http://www.scfbio-iitd.res.in/). ADME screening helps in detecting

drug likeliness of compounds. According to Lipinski’s rule of five,

the number of rotatable bond, compound molecular weights

(MW), calculated logarithm of the partition coefficient between

n-octanol and water (CLogP), molar refractivity, number of

hydrogen bond acceptors (HBA) and number of hydrogen bond

donors (HBD) were used to assess the “drug-likeness” [27].

Molecular Docking

For molecular docking analysis, AutoDock4.2 was used in this

study[28,29]. All optimized ligand molecules were docked into

the active site of refined human α-amylase.

The rotatable bonds of the ligands were set to be free

however the protein molecule was treated as a rigid

structure[30]. Throughout this insilico experiment, the grid box

size was set at 13.44, 15.14 and 39.44Å (x, y, and z) to include

all the amino acid residues at the binding site while the

spacing between grid points was kept at 0.375 angstroms.

Data Analysis

All protein snapshots were taken using PYMOL.

RESULTS AND DISCUSSIONS

The current study features in silico experimental procedures to

evaluate the molecular interaction, binding mode and

inhibitory potential of specifically selected polyphenolic

compounds, which had earlier been isolated from various

medicinal plants, on α-amylase. Table 1 shows the binding

energy values and molecular interaction properties of the

selected natural compounds in comparison to acarbose and

miglitol. The binding mode of the compounds on human

alpha amylase is given in Figure 4 while the residues involved

in hydrogen bond formation between α-amylase and the

polyphenolic compounds are presented in Figure 5. The

docking study was performed using AutoDock 4.2 with PyMol

Tool. Molecular docking aids in studying the molecular

interactions between ligand molecules and target protein

macromolecule prior to possible in vitro analysis. Protein

structural analysis and ADME assessment were done on

available web servers. Human α-amylase template was

retrieved from PDB Database, modeled and used as a target

for docking simulation. The ligands used in this study were

sketched and prepared for docking studies using

MarvinSketch. The 2D structures of the ligands obtained from

the MarvinSketch are shown in the Figure 1. The computed

ADME results for the compounds are given in Table 2 whereas

the protein structural validation result is shown in Table 3 - 4.

Cassigarol E

Scirpusin B

Epicatechingallate

Int J of Com Bio and Bioinfo

23

Sarcoviolin

Figure 1: 2D structures of selected polyphenolic compounds

According to this study, when acarbose and miglitol were docked

into the active site of modeled protein, binding energy values -

7.5Kcal/mol and -5.1Kcal/mol were obtained for the compounds

respectively. In the same vein, the polyphenolic compounds were

also docked into the active site of the enzyme and energy values

of -9.2Kcal/mol, -9.0Kcal/mol, -8.9Kcal/mol and -8.3Kcal/mol were

obtained for scirpusin B, cassigarol E, epicatechingallate and

sarcoviolin respectively. Based on these results, it is evident that

the polyphenolic compounds have relatively better inhibitory

activity than miglitol and acarbose[7,24]. Miglitol and acarbose

are established antidiabetic drugs employed in lowering

postprandial hyperglycemia [7,31]. Acarbose is the most effective

inhibitors of α-amylase enzyme[23], however it also exhibits

unwanted side effects. As demonstrated clearly in this research,

all the docking results showed that the polyphenolic compounds

can enter a region in the enzyme’s active site where substrate

usually binds with a potential of accurately blocking enzyme

substrate from assessing the site. These results are compatible with

earlier reports obtained in in vitro experiments [10,14,15,17].

The interacting amino acid residues within the 4Å that participate

in stabilising of the receptor-ligand complex formed by α-amylase

and the phenolic compounds are listed in Table 2 and displayed

in Figure 6. These residues were HIS-305, GLN-63, ARG-195, GLU-

233, ALA-106, ASN-105, HIS-101, LEU-165, THR-163, TYR-62 and they

are the main contributors to the α-amylase-ligand interactions. It is

clear from this study that the polyphenolic compounds interacted

with amino acid residues (especially ASP-197 and GLU-233) that

are essential for enzymatic action and hence competitively

blocked catalytic activities[32]. Hydrogen bond interactions exist

between the enzyme and the phenolic compounds as

summarized in Table 1 and displayed in Figure 5. Scirpusin B

established four significant hydrogen bonds with residues ASP-197

O--O-H(2.3Å), ARG-195 N-H--O(3.1Å), HIS-305 N-H--O(3.1Å) and

GLN-63 O--H-O(2.0Å). Six hydrogen bonds were formed by

epicatechingallate with residues GLU-233 O--H-O(2.0Å), HIS-201

NH--O(3.4Å), ASP-197 O--H-O(2.2Å), HIS-101 N-H--O(3.5Å), ASP-197

O--H-O(2.3Å) and GLU-233 O--H-O(2.2Å). Cassigarol E interacted

with the amino acid residues at the active site, forming 4

hydrogen bonds with residues ASN-105 N-H--O(3.2Å), ASN-105 O--

H-O(3.2Å), ARG-195 N-H--O(3.3Å) and ASP-197 O--H-O(2.2Å) while

sarcoviolin participated in hydrogen bonding with THR-163 O-H--

O(2.8Å), GLU-233 O--H-O(2.7Å), GLN-63 O--H-O(2.2Å) and ARG-

195 N-H--O(2.9Å). Intra-hydrogen bonds also existed between

elements in the polyphenolic compounds. Hydrogen bond has a

major role in enzyme catalysis and structural stability of many

biological molecules. Other molecular interaction between α-

amylase and the phenolic compounds include π–π interaction

with the phenolic backbone, hydrophobic and electrostatic

attractions between the natural compounds and amino acid

residues of the protein, possibly responsible for observed binding

energy as well as the better interaction and inhibitory potential of

the phenolic compounds. Apart from establishing hydrogen bond

with the enzyme, the compounds also have a tendency to

interact, via π-π interaction, with aromatic amino acids of the

protein[24].

Table 1: Docking results and hydrogen bond interaction

between human α-amylase and the polyphenolic compounds

Table 2: ADME result for the polyphenolic compounds on

the rule of five formulations

HBA = Hydrogen bond acceptor, HBD = Hydrogen bond

donor, CLogP = The logarithm of the partition coefficient

between n-octanol and water

Figure 2: 3D model of human α-amylase protein

Figure 3: Ramachandranplot generated by PROCHECK. Red

areas correspond to favored region, allowed region are

presented in yellow while light yellow areas correspond to

generously allowed region and disallowed region is shown in

white.

Int J of Com Bio and Bioinfo

24

Table 3: RamachandranPlot analysisof f200 receptor (Human α-

amylase)

Table 4: The G-Factor value

Figure 4a: Molecular interaction between scirpusin B and

human α-amylase

Figure 4b: Molecular interaction between cassigarol E and

human α-amylase

Figure 4c: Molecular interaction between epicatechingallate

and human α-amylase

Figure 4d: Molecular interaction between sarcoviolins and

human α-amylase

Figure 5a: Hydrogen bond interaction between amino acid

residues and scirpusin B

Figure 5b: Hydrogen bond interaction between amino acid

residues and cassigarol E

Int J of Com Bio and Bioinfo

25

Figure 5c: Hydrogen bond interaction between amino acid

residues and epicatechingallate

Figure 5d: Hydrogen bond interaction between amino acid

residues and sarcoviolins

Figure 5e: Hydrogen bond interaction between amino acid

residues and acarbose

Figure 5h: Hydrogen bond interaction between amino acid

residues and miglitol

The results obtained for protein model structural analysis, using

PROCHECK, are summarized in Figure 3 and Table 3. The plot

revealed that out of 495 residues, 371 residues (86.9%)

occurred in the most favoured regions and no residue (0%)

was found in the disallowed region. While only 1 residue (0.2%)

occurred in the generously allowed regions, 54 residues

(12.9%) occurred in the additional allowed regions. Based on

these parameters which are the determinants of a good

model, the protein model used in this study is acceptable and

possess an overall good quality[24,33,34].

The drug-like properties of the compounds were checked

against the “Lipinski rule of five” and the results (ADME test)

are summarized in Table 2. It is well known that this rule was

made in 1997 by Christopher A. Lipinski and it is usually used to

investigate whether a specific chemical compounds

possessing certain pharmacological and biological as well as

ADME (adsorption, distribution, metabolism and excretion)

activity demonstrate the ability which would make it an orally

active drug when administered to humans [35]. It evaluates

the ligands based on parameters like LogP value, molecular

weight, hydrogen donors, molar refractivity and hydrogen

acceptors. According to Lipinski’s rule, it is expected that a

drug-like molecule should have not more than one of the

violations given as follows: molecular weight no more than

500; no more than five hydrogen bond donors; LogP no more

than 5 and no more than ten hydrogen bond acceptors [36].

The result of this study showed that the selected polyphenolic

compounds demonstrated the properties required of a drug

based on Lipinski's rule.

CONCLUSIONS

This work elucidates the molecular basis of inhibition exerted

by selected plant-derived polyphenolic compounds on α-

amylase (an enzyme related to type 2 diabetes mellitus). It is

obvious from this research that the compounds bind to

human α-amylase active site, interact with residues at the

substrate binding site via hydrogen bond formation, π–π

interaction, hydrophobic and electrostatic attractions

resulting in a competitive mode of enzyme inhibition. This

validates available reports of blood glucose lowering effects

of these compounds. In addition, the Lipinski’s properties for

these compounds followed all criteria. Considering their high

binding affinity to the enzyme, it is believed that these natural

compounds could be useful as efficacious therapeutic

candidates and may be considered as alternatives to the

known drugs in the management of postprandial

hyperglycemia.

CONFLICT OF INTERESTS

The author declares no conflict of interest.

ACKNOWLEDGMENTS

The author is grateful to all researchers at the Centre for Bio-

computing and Drug Development, AdekunleAjasin

University, Akungba-Akoko, Ondo state, Nigeria for his training

and guidance.

REFERENCES

1.Diaz P, Jeong SC, Lee S, Khoo C, Koyyalamudi SR (2012).

Antioxidant and anti-inflammatory activities of selected

medicinal plants and fungi containing phenolic and flavonoid

compounds. Chinese Medicine 7 (26): 1-9.

2. AsgarA (2013). Anti-diabetic potential of phenolic

compounds: A review. International Journalof Food Properties

16(1): 91-103.

3. Huang WY, Cai YZ, Zhang Y (2010). Natural phenolic

compounds from medicinal herbs and dietary plants:

potential use for cancer prevention. Nutrition and Cancer

62(1): 1-20.

4. Apostolidis E, Kwon YI, Shetty K (2007). Inhibitory potential of

herb, fruit, and fungal-enriched cheese against key enzymes

linked to type II diabetes and hypertension. Innovative Food

Science and Emerging Technology 8: 46-54.

Int J of Com Bio and Bioinfo

26

5. Chethan S, SreeramaYN,Malleshi NG (2008). Mode of inhibition

of finger millet malt amylases by the millet phenolics. Food

Chemistry 111: 187-191.

6. Stumvoll M, Goldstein BJ, van Haeften TW (2005). Type 2

diabetes: Principles of pathogenesis and therapy. Lancet

365(9467): 1333-46.

7. Metibemu DS, Saliu JA, Metibemu AO, Oluwadahunsi OJ, Oboh

G, OmotuyiIO,Akinloye OA (2016). Molecular docking studies of

isorhamnetin from Corchorusolitorius with target alpha-amylase

related to Type 2 diabetes. Journal of Chemicaland

Pharmaceutical Research 8(4): 1262-1266.

8. Yin Z, Zhang W, Feng F, Zhang Y, Kang W (2014). α-Glucosidase

inhibitors isolated from medicinal plants. Food Science and

Human Wellness 3(3-4): 136–174.

9. Yang GX, Zhou JT, Li YZ, Hu CQ (2005). Anti-HIV bioactive

stilbene dimers of Caraganarosea. Planta Medica 71(6): 569-71.

10. Kobayashi K, Ishihara T, Khono E, Miyase T, Yoshizaki F (2006).

Constituents of stem bark of Callistemon rigidus showing inhibitory

effects on mouse alpha-amylase activity. Biological and

Pharmaceutical Bulletin 29(6): 1275-7.

11. Maruki-Uchida H, Kurita I, Sugiyama K, Sai M, Maeda K, Ito T

(2013). The protective effects of piceatannol from passion fruit

(Passiflora edulis) seeds in UVB-irradiated keratinocytes. Biological

and Pharmaceutical Bulletin 36(5):845-9.

12. Maruki-Uchida H, Inagaki H, Ito R. Kurita I, Sai M, Ito T (2015).

Piceatannol lowers the blood glucose level in diabetic mice.

Biological and Pharmaceutical Bulletin 38(4): 629-633.

13. Matsumoto Y, Gotoh N, Sano S, Sugiyama K, Ito T, Abe Y,

Katano Y,Ishihata A (2014). Effects of scirpusin B, a polyphenol in

passion fruit seeds, on the coronary circulation of the isolated

perfused rat heart. International Journalof Medical Research and

Health Sciences 3(3):547-553.

14. Xiang T, Uno T, Ogino F, Ai C, Duo J, Sankawa (2005).

Antioxidant constituents of Caraganatibetica. Chemical

Pharmaceutical Bulletin 53(9): 1204-1206.

15. Tran HH, Le MC, Nguyen TL, Pham TB, Chau VM, Nguyen HN,

Nguyen TD (2014). Inhibitors of α-glucosidase and α-amylase from

Cyperusrotundus. Pharmaceutical Biology 2014 (1): 74-77.

16. Valeria C, Carmela S,Corrado T (2004). Sarcodonins and

sarcoviolins, bioactive polyhydroxy-p-terphenylpyrazinediol

dioxide conjugates from fruiting bodies of the Basidiomycete

Sarcodonleucopus. European Journal of Organic Chemistry (3);

592-599.

17. Ma K, Han J, Bao L, Wei T, Liu H (2014). Two sarcoviolins with

antioxidative and α-glucosidase inhibitory activity from the edible

mushroom Sarcodonleucopus collected in Tibet. Journal of

Natural Products 77: 942-7.

18. Du GJ, Zhang Z, Wen XD, Yu C, Calway T, Yuan CS, Wang CZ

(2012). Epigallocatechin gallate (EGCG) is the most effective

cancer chemopreventive polyphenol in green tea. Nutrients.

4(11):1679-91.

19. He Q, Lu Y, Yao K (2006). Effects of tea polyphenols on the

activities of alpha amylase, pepsin, trypsin, and lipase. Food

Chemistry 101 (3):1178-82.

20. Bhandari MR, Jong-Anurakkun N, Hong G, Kawabata J (2008).

α-glucosidase and α-amylase inhibitory activities of Nepalese

medicinal herb Pakhanbhed (Bergeniaciliata, Haw). Food

Chemistry106: 247 - 252.

21. Sabu MC, Smitha K, Kuttan R (2002). Antidiabetic activity of

green tea polyphenols and their role in reducing oxidative stress in

experimental diabetes. Journal ofEthnopharmacology 83 (1-2):

109-116.

22. Baek SJ, Kim JS, Jackson FR, Eling TE, McEntee MF, Lee SH

(2004). Epicatechingallate-induced expression of NAG-1 is

associated with growth inhibition and apoptosis in colon cancer

cells. Carcinogenesis 25(12): 2425-32.

23. Coman C, Rugina OD, Socaciu C (2012). Plants and natural

compounds with antidiabetic action. NotulaeBotanicaeHortiAgro

botaniciCluj-Napoca40(1): 314-325.

24. Rupanjali BS, Dipak C (2013). Docking studies on quinine

analogs for plasmepsin-II of malaria parasite using bioinformatic

tools. International Journal of Pharmacy and Pharmaceutical

Sciences 5(3): 681-685.

25. Laskowski RA, Rullmann JA, MacArthur MW, Kaptein R,

Thornton JM (1996). AQUA and PROCHECK-NMR: programs for

checking the quality of protein structures solved by NMR.

Journal of Biomolecular NMR 8: 477–86.

26. Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH,

Canese K, Chetvernin V, Church DM, DiCuccio M, Federhen S,

Feolo M, Fingerman IM, Geer LY,Helmberg W, Kapustin Y,

Landsman D, Lipman DJ, Lu Z, Madden TL, Madej T, Maglott

DR, Marchler-Bauer A, Miller V, Mizrachi I, Ostell J, Panchenko

A, Phan L, Pruitt KD, Schuler GD, Sequeira E, Sherry ST,

Shumway M, Sirotkin K, Slotta D, Souvorov A, Starchenko G,

Tatusova TA, Wagner L, Wang Y, Wilbur WJ, Yaschenko E, Ye J

(2011). Nucleic Acids Research 39: 38-51.

27. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997).

Experimental and computational approaches to estimate

solubility and permeability in drug discovery and

development settings. Advanced Drug Delivery. Review 23 (1-

3): 3-25.

28. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK,

Goodsell, DS, Olson AJ (2009). AutoDock4 and AutoDock Tools

4: Automated docking with selective receptor flexibility.

Journal of Computational Chemistry 30: 2785-2791.

29. Trott O, Olson AJ (2010). AutoDockVina: Improving the

speed and accuracy of docking with a new scoring function,

efficient optimization and multithreading, Journal of

Computational Chemistry31: 455-461.

30. Chitranshi N, Gupta S, Tripathi PK, Seth PK (2013). New

molecular scaffolds for the design of Alzheimer׳ s

acetylcholinesterase inhibitors identified using ligand- and

receptor-based virtual screening. Medicinal Chemistry

Research 22: 2328–2345.

31. Duraisamy G, ManokaranK,Chandraseka U (2012). In vitro -

amylase and -glucosidase inhibitory effects of ethanolic

extract of Evolvulusalsinoides (L). International Research

Journal of Pharmacy3(3): 226-229.

32. Miao M, Jiang B, Jiang H, Zhang T, Li X (2015). Interaction

mechanism between green tea extract and human α-

amylase for reducing starch digestion. Food Chemistry 186:

20-25.

33. Gayathri DC, ShanmughavelP,Parthiban M (2008). An in

silico approach to high altitude pulmonary edema-molecular

modeling of human β2 adrenergic receptor and its interaction

with salmeterol and nifedipine. Journal of Molecular Modeling

14: 849-856.

34. Hasan A, Mazumder HH, Chowdhury AS, Datta A, Khan A

(2015). Molecular-docking study of malaria drug target

enzyme transketolase in Plasmodium falciparum 3D7 portends

the novel approach to its treatment. Source Code for Biology

and Medicine 10:7

35. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001).

Experimental and computational approaches to estimate

solubility and permeability in drug discovery and

development settings. Advanced Drug Delivery Review 46(1–

3): 3–26.

36. Sarfaraz A, Feroz K (2014). QSAR and docking studies on

xanthone derivatives8: 183–195.