Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass

21 Pages·2017·2.78 MB·English

molecules
Article
Characterization of α-Glucosidase Inhibitors from
Clinacanthus nutans Lindau Leaves by Gas
Chromatography-Mass Spectrometry-Based
Metabolomics and Molecular Docking Simulation
SuganyaMurugesu1,ZalikhaIbrahim1 ,Qamar-UddinAhmed1,Nik-IdrisNikYusoff1,
Bisha-FathamahUzir1,VikneswariPerumal2,FaridahAbas3 ,KhozirahSaari3,
HeshamEl-Seedi4,5andAlfiKhatib1,3,*
1 DepartmentofPharmaceuticalChemistry,KulliyyahofPharmacy,InternationalIslamicUniversityMalaysia,
Kuantan25200,PahangDarulMakmur,Malaysia;[email protected](S.M.);
[email protected](Z.I.);[email protected](Q.-U.A.);[email protected](N.-I.N.Y.);
[email protected](B.-F.U.)
2 FacultyPharmacy&HealthSciences,UniversitiKualaLumpurRoyalCollegeofMedicinePerak,
Ipoh30450,PerakDarulRidzuan,Malaysia;[email protected]
3 LaboratoryofNaturalProducts,InstituteofBioscience,UniversitiPutraMalaysia,Serdang43300,
SelangorDarulEhsan,Malaysia;[email protected](F.A.);[email protected](K.S.)
4 DivisionofPharmacognosy,DepartmentofMedicinalChemistry,BiomedicalCentre,UppsalaUniversity,
Box574,SE-75123Uppsala,Sweden;[email protected]
5 H.E.J.ResearchInstituteofChemistry,InternationalCentreforChemicalandBiologicalSciences,
UniversityofKarachi,Karachi75270,Pakistan
* Correspondence:alfi[email protected];Tel.:[phone]
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:24August2018;Accepted:18September2018;Published:19September2018
Abstract: Background: Clinacanthusnutans(C.nutans)isanAcanthaceaeherbalshrubtraditionally
consumed to treat various diseases including diabetes in Malaysia. This study was designed
to evaluate the α-glucosidase inhibitory activity of C. nutans leaves extracts, and to identify the
metabolites responsible for the bioactivity. Methods: Crude extract obtained from the dried
leaves using 80% methanolic solution was further partitioned using different polarity solvents.
The resultant extracts were investigated for their α-glucosidase inhibitory potential followed by
metabolites profiling using the gas chromatography tandem with mass spectrometry (GC-MS).
Results: Multivariate data analysis was developed by correlating the bioactivity, and GC-MS
data generated a suitable partial least square (PLS) model resulting in 11 bioactive compounds,
namely, palmitic acid, phytol, hexadecanoic acid (methyl ester), 1-monopalmitin, stigmast-5-ene,
pentadecanoicacid,heptadecanoicacid,1-linolenoylglycerol,glycerolmonostearate,alpha-tocospiro
B, and stigmasterol. In-silico study via molecular docking was carried out using the crystal
structureSaccharomycescerevisiaeisomaltase(PDBcode: 3A4A).Interactionsbetweentheinhibitors
and the protein were predicted involving residues, namely LYS156, THR310, PRO312, LEU313,
GLU411,andASN415withhydrogenbond,whilePHE314andARG315withhydrophobicbonding.
Conclusion: Thestudyprovidesinformativedataonthepotentialα-glucosidaseinhibitorsidentified
inC.nutansleaves,indicatingtheplant’stherapeuticeffecttomanagehyperglycemia.
Keywords: α-glucosidaseinhibitors;Clinacanthusnutans;diabetes;GC-MS;partialleastsquare
Molecules2018,23,2402;doi:10.3390/molecules23092402 www.mdpi.com/journal/molecules
Molecules2018,23,2402 2of21
1. Introduction
Diabetesmellitus(DM)isoneofthemostchronicmetabolicdisordersthatoccursduetoabnormal
carbohydratemetabolismthatismainlylinkedwithinsensitivityofthetargetorganstoinsulin[1,2].
Commonly, it is characterized by hyperglycemia in which the level of blood sugar is significantly
elevated,whichinturndisturbsthenormalbodymetabolismrelatedtocarbohydrate,fat,andprotein
breakdown and eventually proves fatal if not treated or managed properly [3]. Cases of DM are
increasingrapidlyworldwideandaffectingallpartsoftheworld. Highincreasingnumbersofcases
have been reported in most developing countries, including Malaysia; caused mainly by inactive
lifestyleandunhealthyfoodconsumption[4]. Around170millionpeopleworldwidehaddiabetesin
theyearof2000anditisprojectedtoincreaseto366millionby2030[5]. Additionally,theprevalence
of DM is also rapidly rising among the middle-class and low-income communities in developing
countries[6].
Theuseofseveraltraditionalmedicinalplantsinthetreatmentofdiabeteshasbeenreported[7,8].
Likewise,Clinacanthusnutans(Burm. F)Lindau;(C.nutans)orSabahsnakegrass(locallyknownas
‘belalaigajah’),isaherbalshrubfoundabundantlyinsomeAsiancountriesincludingThailandand
Malaysia.Thisherbhasbeentraditionallyusedtotreatvariousdiseasesincludinggout,hyperuricemia,
inflammation, fever, skin rashes, and diabetes [9]. Commonly, the plant leaves are used for the
treatmentofburns,allergicreactions,mucositis,skinrashes,andlesionsofvaricella-zostervirus(VZV)
andherpessimplexvirus(HSV)[10,11]. InMalaysiaandThailand,ithasbeenpracticedasaremedy
for scorpion and insect stings as well as venomous snake bites [12,13]. In recent times, the leaves
ofC.nutansarewidelyconsumedbycancerpatientsthroughoutMalaysia[14]whileinIndonesia,
theleavesareusedtotreatdiabetes,hyperuricemia,anddysentery[15,16].
C.nutansleavesarereportedtocontainseveralcompoundsthatpossesshypoglycemicproperties
suchascaffeicacid,chlorogenicacid,quercetin,lupeol,betulin,stigmasterol,andβ-sitosterol[17–20].
Mostoftheexistingstudieswerecarriedoutseparatelytoevaluatethebiologicalactivitiesofsome
individualcompounds. However,thebioactivityoftheindividualcompoundsmaybedifferentwhen
thesecompoundsarewithinthecomplexmatrixoftheleaves,whichcouldbeduetothesynergistic
effect with other components [21]. Thus, the reported antidiabetic compounds may not be solely
responsible for the actual antidiabetic activity of the plant. Therefore, one way of detecting the
compoundsresponsibleforagivenactivityisbyusingthemetabolomicsapproach. Itisaholistic
approachthatincludesthedetectionofallmetabolitesineachsamplethatcanbecorrelatedtothe
tested bioactivity using multivariate data analysis (MVDA) [22]. Metabolomics techniques can be
achievedwiththeaidofchromatographyorspectroscopyinstrumentstoprofileallthemetabolites
present in the analyzed sample. MVDA is a suitable statistical tool for managing large data sets
obtainedusingspectroscopictoolsandisemployedinclassifyingsamplesbasedontheirchemical
constituents[23].
Severalcommonanalyticalinstrumentshavebeenusedasananalyticalplatforminmetabolomics,
includingnuclearmagneticresonancespectroscopy(NMR),gaschromatography-massspectrometry
(GC-MS),andliquidchromatography-massspectrometry(LC-MS)[24]. Inthisstudy, GC-MSwas
utilizedduetothenon-polarnatureofthemostactivehexaneextract. Moreover,GC-MSprovides
highresolutionofseparation,highsensitivity,andthereproducibilityneededtoanalyzeacomplex
biologicalmixture[25]. Hence,theaimsofthisstudyweretoevaluatetheα-glucosidaseinhibitory
activityofdifferentextractsofC.nutansleaves,andtoidentifytherelatedbioactivecompoundsinthe
extractusingGC-MSbasedmetabolomics.
Thestudyhasalsodepictedthemolecularinteractionbetweentheenzymesandtheinhibitors
(ligands) identified in the plant extracts. With molecular docking, the conformations and binding
affinitiesofthepotentialphytoconstituentscouldbepredicted. Inaddition,thisin-silicotechniqueaids
inthevisualizationoftheligand-proteincomplexformedwhileportrayingthecharacteristicactivity
andbindingsitesofthecompounds[26].
Molecules2018,23,2402 3of21
2. Results
2.1. α-GlucosidaseInhibitoryActivity
The α-glucosidase inhibitory activity of the C. nutans leaves extracts obtained from different
solventextractionaredisplayedinTable1asthehalf-maximalconcentrationvalues(IC ,µg/mL).
50
AlowerIC valueisdesirableforhigherα-glucosidaseinhibitionactivity[27]. TheIC valuesranged
50 50
from3.07to133.57µg/mL.Thehighestα-glucosidaseinhibitoryactivitywasobservedforhexane
fraction,withanIC valueof3.05µg/mL,whichwasfoundcomparableorevenbettertotheIC
50 50
value obtained for quercetin, the positive control. Meanwhile, the methanol extract exhibited the
lowestactivitywiththehighestIC viz.,133.57µg/mL.
50
Table 1. The half maximal inhibitory concentration (IC ) of α-glucosidase inhibitory activity of
50
C.nutansleavesextracts.
Samples IC (µg/mL)
50
Hexane(H) 3.05±0.05d
Hexane:Ethylacetate(HE) 5.54±0.09d
Ethylacetate(EA) 8.42±0.17c
Ethylacetate:Methanol(EAM) 37.45±0.90b
Methanol(M) 133.57±0.30a
5.77±1.01
Quercetin
(19.09µM)
Dataexpressedasmean±SD(n=6).SD=StandardDeviation.Meansthatdonotsharealetteraresignificantly
differentwithpvalue<0.05.
2.2. GC-MSChromatogramofC.nutansLeavesExtracts
Allidentifiedmetaboliteswereconfirmedbyscrutinizingthespectralpatternandcomparison
with GC-MS NIST14 database library. The metabolites identified were primarily plant sterols,
fatty acids, organic acids, and others, including palmitic acid, phytol, hexadecanoic acid,
1-monopalmitin,stigmast-5-ene,pentadecanoicacid,heptadecanoicacid,1-linolenoylglycerol,glycerol
monostearate,alpha-tocospiroBandstigmasterol.Theremainingcompoundsincludesucrose,maltose,
D-gluconic acid and D-glucose, which were abundant in the non-active extract (methanol extract).
Alltheabove-mentionedmetabolitesarelabelledandshowninthechromatogramofeachoftheextract
analyzed,asdisplayedinFigure1. SeveralcompoundsarefoundabundantlyinH,HEandEextracts
especially palmitic acid, phytol, stigmast-5-ene and stigmasterol. The intensity of the metabolites
reducedapparentlyasthepolarityoftheextractsolventincreased. Meanwhile,sucroseandD-glucose
werefoundmoreprominentinthemorepolarextract(EMandM).
Molecules2018,23,2402 4of21
Figure 1. GC-MS chromatogram of metabolites in H, HE, E, EA and M extracts from C. nutans.
Eachlabelledpeakrefersto;1—palmiticacid,2—phytol,3—sucrose,4—hexadecanoicacid,5—maltose,
6—1-monopalmitin, 7—D-glucose, 8—stigmast-5-ene, 9—D-gluconic acid, 10—pentadecanoic acid,
11—heptadecanoicacid,12—1-Linolenoylglycerol,13—Glycerolmonostearate,14—Alpha-TocospiroB
and15—Stigmasterol.
Molecules2018,23,2402 5of21
2.3. MultivariateDataAnalysis
ThebioactivecompoundscanbeidentifiedthroughGC-MSbasedmetabolomicsutilizingMVDA
wherebyapartialleastsquare(PLS)modelwasdeveloped. Automatedfittingresultsintwoprincipal
componentsnamelyPLScomponent1and2. Themasstochargeratio(m/z)ofacertainretention
timewasextractedandtheIC (µg/mL)valuesofeachofthesampleswereconsideredasxandy
50
variables,respectively. PLSwasperformedtodiscriminatetheconstituentsamongtheextractsandto
pinpointthem/zvaluescontributingtothebioactivity[1]. Briefly,themodeldiagnosticofthePLS
modeldevelopedcanbeevaluatedusingseveralparameterssuchasgoodnessoffit,permutationtest,
andcapabilityofthemodeltopredicttheyvalueusingactualtopredictedplot. Thegoodnessoffit
isexplainedbythecumulativevaluesofR2Yindicatingthepercentageofvariationoftheresponse
explainedbythemodel,andthecumulativevalueofQ2Yrepresentingthepercentageofthevariation
of the response predicted by the model according to cross validation. The fitness of model and
predictiveabilityareconsideredgoodifthecumulativevaluesofbothR2YandQ2Yaregreaterthan
0.5[28]. Inthisstudy,theR2YandQ2Yvalueswere0.95and0.87,respectively.
Otherimportantparametersthatareconsideredinmeasuringtheaccuracyandtheperformance
ofthemodelareroot-mean-squareerrorsofestimation(RMSEE)andtherootmeansquareerrorof
cross-validation(RMSECV).RMSEEisameasureofaveragedeviationofthemodelfromthedata,
whileRMSECVisameasureofthequalityofthemodelinpredictingnewsamples. Thelowerthe
RMSECVvalue,thehigherthepredictiveaccuracyfornewsamples. Forthismodel,theRMSEEand
theRMSECVvaluesare0.0286046and0.0441324,respectively.
Figure2showsthescorescatterplotofthePLSmodelwithgoodseparationofthesamplesbased
onthechemicalprofileandbioactivity(IC value)ofthesamples. Thehighactivesamples(H,HE,
50
and EA) with the IC value less than 10 µg/mL were observed at the positive PLS component 1,
50
whilethelessactivesamples(EAMandM)weredistributedatthenegativePLScomponent1.
Figure2.Partialleastsquare(PLS)scorescatterplotofdifferentsolventextractsofC.nutansleaves.
Figure 3 displays the permutation test plot, which depicts the validation of the PLS model
developed. Theplotdisplaystheinterceptsforthefractionofthetotalsumofthesquaresintercept
Y-valueandthepredictiveabilityofthemodelinterceptY-value. TheR2YandQ2Yvaluesinthis
studyare0.407and −0.288, respectively, whichareacceptablesincetheR2 andQ2 isequalorless
than0.4and−0.05,respectively[29]. Thesupplementaryplot,FigureS1displaystheobservedversus
predictedplotthatmeasurestheaccuracyofthePLSmodelwiththeregressionequationthatmeasures
itsvalidationwithR2valueof0.9497,whichisvalidonlyiftheR2valueis≥0.9.
Molecules2018,23,2402 6of21
Figure3.ThepermutationplotwiththeR2Y=0.407andQ2Y=−0.288.
Figure4showstheloadingcolumnplotofthedifferentextractsofC.nutansleaves. Eachpeak
indicatestheidentifiedmetabolitefromtheC.nutansleavesextractsandthecompoundsdetailshave
beentabulatedinTable2. Thepeakslabelledas3,5,7,and9representsucrose,maltose,D-gluconic
acid,andD-glucose,respectively,thatarelocatedoppositetotheperIC50 valueassumedtoinduce
α-glucosidaseactivity. Thesecompoundswerefoundabundantinthemethanolextract,withitsIC
50
valuemorethan100µg/mL,indicatingthatthecompoundswerelessactive.Meanwhile,therestofthe
peaksthatareinthesamepositionastheperIC valueareeffectivetoinhibitα-glucosidaseactivity
50
whichareprimarilyplantsterols,fattyacids,organicacids,andothercompounds,namely;palmitic
acid,phytol,hexadecanoicacid,1-monopalmitin,stigmast-5-ene,pentadecanoicacid,heptadecanoic
acid,1-linolenoylglycerol,glycerolmonostearate,alpha-tocospiroB,andstigmasterol(Figure5).
Figure 4. PLS loading column plot of different solvent extracts of C. nutans leaves. Assignments:
1—Palmitic Acid, 2—Phytol, 3—Sucrose, 4—Hexadecanoic acid, 5—Maltose, 6—1-Monopalmitin,
7—D-Glucose,8—Stigmast-5-ene,9—D-Gluconicacid,10—Pentadecanoicacid,11—Heptadecanoicacid,
12—1-Linolenoylglycerol,13—Glycerolmonostearate,14—alpha-TocospiroB,and15—Stigmasterol.
Molecules2018,23,2402 7of21
Table2.CompoundsidentifiedintheC.nutansleavesextractsthroughGC-MSanalysis.
Molecular
No. RT(min) %ofArea Probability M+ TentativeMetabolites
Formula
1 17.24 10.50 99 256.24 C H O Palmiticacid
16 32 2
2 26.64 1.42 95 296.31 C H O Phytol
20 40
3 45.47 36.18 93 342.12 C H O Sucrose
12 22 11
4 11.63 0.91 99 270.26 C H O Hexadecanoicacid
17 34 2
5 49.48 0.28 93 342.12 C H O Maltose
12 22 11
6 44.43 1.03 95 330.51 C H O 1-Monopalmitin
19 38 4
7 10.31 19.04 91 180.06 C6H12O6 D-Glucose
8 56.83 4.50 99 398.39 C H Stigmast-5-ene
29 50
9 14.30 3.42 93 196.16 C6H12O7 D-Gluconicacid
10 12.34 0.24 98 242.22 C H O Pentadecanoicacid
15 30 2
11 23.95 0.38 93 270.26 C H O Heptadecanoicacid
17 34 2
12 47.59 0.46 99 352.52 C H O 1-Linolenoylglycerol
17 36
13 47.99 0.40 91 358.31 C H O Glycerolmonostearate
21 42 4
14 49.24 0.07 96 462.37 C H O Alpha-TocospiroB
29 50 4
15 55.59 2.70 99 412.37 C H O Stigmasterol
29 48
RT=RetentionTimeC=Carbon,H=Hydrogen,O=Oxygen,N=Nitrogen.
Figure5.Thebioactivecompoundsthatinhibitα-glucosidaseenzymeactivity.
Molecules2018,23,2402 8of21
2.4. BioactivityConfirmationandQuantificationofSomeBioactiveCompounds
A further investigation was required to confirm the bioactivity of afore-mentioned identified
compounds through testing of the individual compounds on α-glucosidase inhibitory activity.
However,notallthecompoundscouldbetestedandquantifiedduetoalimitationoftheavailability
of some of these compounds. Therefore, three bioactive compounds were procured and tested.
These include palmitic acid, heptadecanoic acid, and stigmasterol. The α-glucosidase inhibitory
activityofthesecompoundswasanalyzed,andtheIC valuesweredisplayedinTable3. Inmany
50
cases,thepurifiedcompoundswereobservedtohavediminishedtheirbioactivitiesafterseparation
or purification from the biologically active extract [30]. However, from the results obtained,
thesecompoundsshowedfairlygoodα-glucosidaseinhibitionactivityasanindividualcompoundin
theirpurifiedstate.
Table 3. The half maximal inhibitory concentration (IC ) of α-glucosidase inhibitory activity of
50
quantifiedreferencecompounds.
IC Value
50
Samples
µg/mL µM
Palmiticacid 24.09±0.44c 93.95
Heptadecanoicacid 26.10±0.77b 96.51
Stigmasterol 65.31±0.37a 158.25
Quercetin 5.77±1.01 19.09
Dataexpressedasmean±SD(n=6).SD=StandardDeviation.Meansthatdonotsharealetteraresignificantly
differentwithpvalue<0.05.
QuantificationofeachidentifiedcompoundthroughGC-MSwascarriedouttodeterminethe
amountoftheidentifiedα-glucosidaseinhibitorsthatispresentintheactiveextract(H)oftheC.nutans
leavesextract. Aninternalstandard,methylnonadecanoatewasusedtocalculatetherecoveryduring
GC-MSanalysis. Theparametersmeasuredintermsofthelinearity,limitofquantification(LOQ),
andlimitofdetection(LOD)aredisplayedinTable4. Besidesthat,thecalibrationcurvesestablished
werefoundtobevalidwiththecoefficientoftheregression>0.99forallthecurvesdevelopedforeach
ofthestandardreferences. Therecoveryofthesecompoundswascalculatedtobe93.07%,basedon
thestandardcurvedevelopedfortheinternalstandard, MNDthatrepresentsthethreequantified
compounds. Overall,therangeofLODvaluesobtainedwasfrom0.31to5.00µg/mg,andtherangeof
LOQwasfrom0.63to10.00µg/mgforallthethreecompounds.
Table4.Calibrationcurves,detectionlimitsandquantificationlimitsmeasuredbyGC-MS.
Concentration LOD LOQ
Analytes R21
(µg/mgSample)2 (µg/mgSample) (µg/mgSample)
Palmiticacid 0.99 433.25±22.96a 2.50 5.00
Heptadecanoicacid 0.99 20.27±1.20c 5.00 10.00
Stigmasterol 0.99 386.48±21.03b 0.31 0.63
LOD=LimitofDetection;LOQ=LimitofQuantification.1R2isthecorrelationcoefficientoftheequationwith
n=3. 2 Dataisexpressedasmean±SD(n=6),calculatedconsidering%recoveryofmethylnonadecanoate
(≥98.0%).Meansthatdonotsharealetteraresignificantlydifferentwithpvalue<0.05.
2.5. MolecularDocking
Molecular docking was carried out in order to understand the protein-ligand interaction at a
molecular level. Generally, protein is comprised of amino acids linked in a sequence to form the
complex. These amino acids have different properties depending on their side chains, which can
behydrophobic(aliphaticoraromatic),polar(neutral)orevenelectricallycharged(acidicorbasic).
Thishasbeenthekeytotheactualpropertiesandbehaviorsofaprotein[31]. Allthecompounds
Molecules2018,23,2402 9of21
identifiedtobeactivelyresponsiblefortheα-glucosidaseinhibitionweredockedtotheSaccharomyces
cerevisiaeisomaltase(SCI)crystalstructure(PDBID:3A4A).Theconformationsdisplayingthelowest
bindingenergyforthecompoundswiththeinteractingresiduesaresummarizedinTables5and6.
Table 5. Molecular interaction results of α-glucosidase enzyme protein with the known inhibitor
(Quercetin)andtheactivecompoundsquantifiedusingGC-MS.
BindingEnergy Other
Compound H-bondInteractingResidues
(kcal/mol) InteractingResidues
ASH69,HIE112,GLN182,
Controlligand −6.00 ARG213,ASH215,GLH277, TYR72
HIE351,ASP352,ARG442
LYS156,THR310,PRO312,
Quercetin −8.15 PHE314,ARG315
LEU313,GLU411,ASN415
Palmiticacid −3.75 LYS156,LEU313,ARG315 HIE280,PHE314
Heptadecanoicacid −3.80 LYS156,SER236,GLN239,SER240 ARG315
TRP15,ILE262,ARG263,
Stigmasterol −8.66 THR290
ILE272,VAL266,HID295
Table6.Molecularinteractionresultsofothertentativemetabolitesdockedontoα-glucosidaseenzyme.
BindingEnergy H-bond
Compound OtherInteractingResidues
(kcal/mol) InteractingResidues
1-linolenoylglycerol −4.76 LYS156,SER241 TYR158,SER240,PHE314
1-monoplamitin −4.01 TYR158,SER240,ASP242 PHE314,ILE419
LYS156,TYR158,PHE159,
Alpha-tocospiroB −7.99 GLN279,GLU411
PHE178,HIE280,PHE314
LYS156,TYR158,SER240,ASP242,
Glycerolmonostearate −3.95 SER241
PHE314,ARG315,TYR316
Hexadecenoicacid
−4.00 ALA292,ASN259,HID295 VAL266
(methylester)
Pentadecanoicacid −4.42 HID295 VAL266
LYS156,TYR158,VAL232,
Phytol −5.51 GLN239,SER240
LEU313,PHE314
TYR158,LYS156,HIE280,PRO312,
Stigmast-5-ene −9.09 -
PHE314,ARG315,TYR316
AccordingtoAutoDock1.5.6simulationresultshowninTable5, theα-glucosidase-quercetin
(positivecontrol)inhibitorcomplexshowed−8.15kcal/molbindingenergycontainingfivehydrogen
bondswiththeinteractingresiduesviz.,ASH69,HIE112,GLN182,ARG213,ASH215,GLH277,HIE351,
ASP352,ARG442,andhydrophobicinteractionwithTYR72.Meanwhile,thebindingaffinityofpalmitic
acid in complex with α-glucosidase was −3.75 kcal/mol. A total of three hydrogen bonds were
observedinthecomplexinvolvingLYS156,LEU313,andARG315alongwithhydrophobicinteractions
involving HIE280 and PHE314. On the other hand, α-glucosidase-heptadecanoic acid complex
showedabindingaffinityvalueof−3.80kcal/mol. FourresiduesincludeLYS156,SER236,GLN239,
andSER240wereobservedtointeractwithheptadecanoicacidviahydrogenbond,whileARG315
wasinteractingthroughπ-bond. Despite,interactionwithonlyoneresiduebyhydrogenbondwith
itshydroxylgroup,THR290,stigmasterolhascomparablyshownabetterbindingenergyof−8.66
kcal/mol than the positive control quercetin. The hydrophobic contacts seemed to be dominant
in α-glucosidase-stigmasterol complex due to its cyclic skeleton and alkyl groups that preferably
bindstoTRP15,ILE262,ARG263,ILE272,VAL266,andHID295. Thecrystallizedligand,α-D-glucose
(ADG)exhibitedslightlylowerbindingenergy;i.e.,−6.00kcal/mol,comparedtothepositivecontrol,
quercetin,andstigmasterol. However,thehydrogenbondingbetweenADGandtheproteinindicates
Molecules2018,23,2402 10of21
astableinteractionwithatotalofninebonds,involvingASH69,HIE112,GLN182,ARG213,ASH215,
GLH277,HIE351,ASP352,andARG442withahydrophobicinteractionwithTYR72.Unlikethedocked
ADG, the docking results showed that the binding of the tested compounds are majorly assisted
throughhydrophobiccontactswithα-glucosidase. Theresultsarecomparabletothosereportedby
Seongetal.,[32]whotestedtheisoflavoneswheredaidzein,genisteinandcalycosinshowedmore
hydrophobiccontactswithsimilarresidues,suchasILE262,ARG263,ILE272,andVAL266. The2D
diagram showing the interaction between the compounds and the protein residues are shown in
Figure6whilethe3Dsuperimposeddiagramofthequercetin,ADG,andthreereferencecompounds
(stigmasterol,palmiticandheptadecanoicacid)isdisplayedinFigure7.
Figure6.The2Ddiagramshowingtheinteractionbetweentheproteinresiduesandtheinhibitors.(A)
Positivecontrol(Quercetin),(B)Palmiticacid,(C)Heptadecanoicacidand(D)Stigmasterol.
Figure7. Thesuperimposed3DdiagramshowingthebindingsiteoftheADG,Quercetin,Palmitic
acid,HeptadecanoicacidandStigmasterol.
The binding energy and residues involved in the interaction of other tentative metabolites
identifiedaredisplayedinTable6. Asfortheotherα-glucosidaseinhibitorsidentifiedinthisstudy,