Structure based comprehensive modelling, spatial fingerprints mapping and ADME screening of

22 Pages·2017·2.29 MB·English

RESEARCHARTICLE
Structure based comprehensive modelling,
spatial fingerprints mapping and ADME
screening of curcumin analogues as novel
ALR2 inhibitors
SantKumarVerma,SureshThareja*
SchoolofPharmaceuticalSciences,GuruGhasidasCentralUniversity,Bilaspur,C.G.,India
a1111111111
a1111111111 *[email protected]
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a1111111111 Abstract
Aldosereductase(ALR2)inhibitionisthemostlegitimateapproachforthemanagementof
diabeticcomplications.ThelimitedtriumphinthedrugdevelopmentagainstALR2ismainly
becauseofitsclosestructuralsimilaritywiththeothermembersofaldo-ketoreductase
OPENACCESS
(AKR)superfamilyviz.ALR1,AKR1B10;andlipophilicityproblemi.e.poordiffusionofsyn-
Citation:VermaSK,TharejaS(2017)Structure
theticaldosereductaseinhibitors(ARIs)totargettissues.Theliteratureevidencedthatnatu-
basedcomprehensivemodelling,spatial
fingerprintsmappingandADMEscreeningof rallyoccurringcurcumindemonstratesrelativelyspecificandnon-competitiveinhibition
curcuminanaloguesasnovelALR2inhibitors. towardshumanrecombinantALR2overALR1andAKR1B10;howeverβ-diketonemoiety
PLoSONE12(4):e0175318.https://doi.org/
ofcurcuminisaspecificsubstrateforliverAKRsandaccountableforit’srapidinvivometab-
10.1371/journal.pone.0175318
olism.Inthepresentstudy,structurebasedcomprehensivemodellingstudieswereusedto
Editor:GiovanniMaga,IstitutodiGenetica
mapthepharmacophoricfeatures/spatialfingerprintsofcurcuminanaloguesresponsiblefor
Molecolare,ITALY
theirALR2specificityalongwithpotencyonadatasetofsyntheticcurcuminanaloguesand
Received:January11,2017
naturallyoccurringcurcuminoids.Thedatasetmoleculeswerealsoscreenedfordrug-like-
Accepted:March23,2017 nessorADMEparameters,andthescreeningdatastronglysupportthatcurcuminana-
Published:April11,2017 loguescouldbeproposedasagooddrugcandidateforthedevelopmentofALR2inhibitors
withimprovedpharmacokineticprofilecomparedtocurcuminoidsduetotheabsenceofβ-
Copyright:©2017Verma,Thareja.Thisisanopen
accessarticledistributedunderthetermsofthe diketonemoietyintheirstructuralframework.
CreativeCommonsAttributionLicense,which
permitsunrestricteduse,distribution,and
reproductioninanymedium,providedtheoriginal
authorandsourcearecredited.
DataAvailabilityStatement:Allrelevantdataare
withinthepaperanditsSupportingInformation Introduction
files.
Diabetesmellitus(DM),acommonmetabolicdisorderdesignatedbythehyperglycaemic
Funding:SKVhasreceivedsupportfromtheIndian
state,adverselyaffectsthehomeostasisofvariousorgansystems[1].Long-termhyperglycae-
CouncilofMedicalResearch(ICMR)intheformof
miacausesacutereversibleandchroniccumulativeirreversiblechanges,includesdamageto
aSeniorResearchFellowship(SRF,No.45/54/
bloodvesselsandperipheralnerveswhicheventuallyleadstodiabeticcomplicationssuchas
2014-PHA-BMS).Thefundershadnoroleinstudy
design,datacollectionandanalysis,decisionto vasculopathy,nephropathy,neuropathy,retinopathy,andcataracts;greatlyincreasingtherisk
publish,orpreparationofthemanuscript. ofatherosclerosis,heartattack,stroke,blindness,amputation,andkidneyfailure[2,3].World-
Competinginterests:Theauthorshavedeclared wide,387millionpeoplesarelivingwithdiabeteswiththeprevalenceof8.3%i.e.oneperson
thatnocompetinginterestsexist. intwelveissufferingfromdiabetes.Intheyear2014,4.9millionindividualsdiedfromdiabetes
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
withthedeathrateofonepersonpersevenseconds.Thediabetesexpenditurehasreached612
billionUS$inthesameyear[4].Ifthecurrentdemographicpatterncontinues,thediabetic
populationswillincreasemorethan592millionuptotheyear2035[5].Thedirecteconomic
costofdiabetesisabout10%ofthetotalhealthcarebudgetofNationalHealthService(NHS)
andisprojectedtoaccountforaround17%in2035/2036;furthermore,approximately90%of
thetotaldirectcostisneededforthetreatmentofthedevastatingdiabeticcomplications[6].
Althoughtightcontrolofbloodglucosereducestheincidenceofdiabeticcomplications,a
significantfractionofdiabeticpatientswithgoodglycaemiccontrolstillshowsthedevastating
complicationsassociatedwithdiabetes[7].Despiteadvancesinthetreatmentofdiabetes,itis
stilldifficulttopreventthedevelopmentandprogressionofmanyofthedisablingcomplica-
tionsassociatedwiththisdisease[2].Severalmechanismsforthepathogenesisofdiabetic
complicationshavebeenproposedsuchasthepolyolpathway[8],non-enzymaticglycation
[9],proteinkinaseC(PKC)[10],hexosamine[11],andoverproductionofsuperoxidebythe
mitochondrialelectrontransportchain[12].Evidenceshavedemonstratedalinkbetween
enhancedmetabolismofglucosethroughthepolyolpathway(Fig1)andtheonsetandpro-
gressionoflong-termdiabeticcomplications[7].
Innormalglycaemiccondition,glucoseentersintoglycolysiscycle;leadingtotheproduction
ofpyruvateandenergy.Inhyperglycaemiccondition,theexcessofglucoseentersintopolyol
pathwayviaaldosereductase(AR,ALR2)enzyme.Aldosereductase,akeymemberofthealdo-
ketoreductase(AKR)superfamily,isthefirstandrate-limitingenzymeofthepolyolpathway,a
glucose-shuntthatchannelsexcessglucosetoformfructosethroughsorbitolinhyperglycaemic
condition[13].Inpolyolpathway,ARinitiallycatalysesthestereospecifictransferofahydride
fromNADPHtothealdehydeformofglucosetoformsorbitol.Sorbitoldehydrogenase,in
turn,utilizesNAD+andoxidizesthisintermediatepolyoltofructose(Fig1).
Fig1.ALR2mediatedPolyolpathway.
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Diabeticcomplicationsarisemainlyduetoprolongedexposureofthebodytohighcon-
centrationsofglucose.Duringhyperglycaemia,thereisanincreasedfluxofglucoseinpolyol
pathway.Morethan30%oftheglucoseismetabolizedbypolyolpathwayduringdiabetescon-
ditions(lessthan3%innormoglycaemicconditions)[14].Underhyperglycaemia,increased
polyolpathwayactivationleadstotheproductionofexcesssorbitolwhichisimpermeable
throughbiologicalmembranes,accumulatesinsidethecells,andcausesosmoticstressleading
tosecondarydiabeticcomplications[15].Further,inhyperglycaemia,increasedutilizationof
NADPH(reducedformofnicotinamideadeninedinucleotidephosphate)byARcouldresult
indecreasedsupplyofNADPHco-factortoglutathionereductasethatconvertsglutathione
disulfide(GSSG)toglutathione(GSH)leadingtodecreasedGSHreductaseactivityandin
turndecreasedGSHlevels(Fig1).SinceitiswellknownthatdecreasedGSHlevelscontribute
tooxidativestress,AR-mediatedincreasesinNADPHconsumptioncouldalsoleadtooxida-
tivestress[16].Thehyperglycaemicinjuryisinpartduetoosmoticandoxidativestress,
inducedbyAR-mediatedreductionofglucosetosorbitol.Further,supportforacriticalroleof
ARinmediatingthetoxiceffectsofglucoseisprovidedbythedemonstrationthatoverexpres-
sionofARinthelensoftransgenicmiceacceleratesdiabeticcataracts[17].Ithasalsobeen
demonstratedthathighglucoseindiabetesleadstotheup-regulationofARinseveraltissues
andthetreatmentwithspecificARinhibitorspreventshyperglycaemia-inducedhyperplasia
andhyperproliferationofvascularsmoothmusclecells[18].Hyperglycaemiacausesprolifera-
tionofvascularsmoothmusclecellsandapoptosisofvascularendothelialcells.Theseobserva-
tionsindicatethatARinhibitioncouldbeusefulinpreventingthepro-vascular-proliferative
effectsofdiabetes,whichisstillremainthemajorcauseofmorbidityandmortalityassociated
withthisdisease.
Invivoanimalstudiesperformedbydifferentresearchersusingsyntheticandnaturalcom-
poundsasARinhibitorsfavourthatARinhibitioncouldbeeffectiveformanagementofdia-
beticcomplications,andsomeofthemhavebeenevaluatedinclinicaltrials[19,20].During
thelastdecade,numbersofaldosereductaseinhibitor(ARI)havebeendeveloped(Fig2)
whichmainlyincludehydantoins,e.g.Fidarestat(1)andSorbinil(2);carboxylicacidderiva-
tives,e.g.Epalrestat(3),Tolrestat(4)andZopolrestat(5);andmoleculeofnaturalorigin,e.g.
Quercetin(6).
Fig2.ARIsofsynthetic(1–5)andnaturalorigin(6)developedduringlastfewdecades.
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Todate,Epalrestatisthesingledrugmoleculeavailableinthemarketforthetreatmentof
diabeticperipheralneuropathy[21,22].FidarestatandRanirestatareothermoleculeswhich
havereachedtoadvancedphaseofclinicaltrials[23,24].Further,mostARIsdevelopedsofar
haveacquiredlimitedtriumph,amongtheminparticularsyntheticARIswerefacinglipophili-
cityproblemi.e.poordiffusiontotargettissuessuchasretinaandnerve,alongwithlinked
harmfulsideeffects[20,25].
Naturalproductscontaininginherentlyvaststructuraldiversitythansyntheticcompounds
arethemajorsourcesofbioactiveagents,andwillcontinuallyplayleadingroleindiscovering
newdrugs.Phytochemicalsareconsideredprivilegedstructuresastheyhavethediversity
spaceinwhichchemicalscaffoldsembodycharacteristicsthatpromotebindingtomultiple
proteintargets.Ananalysisoftheoriginofthedrugsthatwerelaunchedinthelast25years
showedthatbothnaturalproductsandtheirderivedsemi-syntheticcompoundscomposed
34%ofallnewchemicalentities,while18%ofthemweresyntheticmimicsofnaturalcom-
pounds[26,27].
Curcumin,anaturalpolyphenolicdiarylheptanoidobtainedfromthedriedrhizomeof
theherbCurcumalongaLinn.Itisevidentfromtheliteraturethatcurcuminisamulti-target
pleiotropicagent,showingabroadrangeofbiologicalactivities.Turmeric(Curcumalonga)
hasbeenwidelyusedinIndiaandChinaasaspice,dietarypigmentandintraditionalme-
dicine,suchasremediesagainstthediabeticcomplications[28–30].Naturallyoccurring
curcumindemonstratesrelativelyspecificandnon-competitiveinhibitiontowardshuman
recombinantALR2overanotherstructurallysimilarmembersofAKRsuperfamilyi.e.alde-
hydereductase(ALR1)andhumansmallintestinereductase(HSIR,AKR1B10)withIC
50
value6.8μM[31],whichisnearly5-foldlesserthanthatofquercetin(IC =37.6μM)[32],a
50
well-knownARIofnaturalorigin.Moreimportantly,curcuminalsopreventstheaccumula-
tionofintracellularsorbitolunderhyperglycaemicstate,inturn,diminishestheosmotic
cellularstress,resultingintodelayinprogressionofdiabeticcomplications[31,33,34].Two
cumulativeα,β-unsaturatedcarbonylgroupsworkasalinkerbetweenbotharomaticphenol
ringspresentincurcumin,andbothofthemexhibitketo-enoltautomerizationviaanenolate
intermediate(Fig3).UndertheneutralpHconditions,curcuminpredominantlyexistsasa
ketoform[31];howeverβ-diketonemoietyofcurcuminisaspecificsubstrateforliveraldo-
ketoreductasesandmaybeaccountableforit’srapidinvivometabolism[35].Variousstruc-
turalmodificationsweremadebydifferentresearchersinthehemicalstructureofcurcuminto
improveitspharmacokineticprofile[36–39].
Takingintoconsideration,theALR2selectivityandinhibitorypotentialofcurcumin,
anintegratedmoleculardockingassistedthree-dimensionalquantitativestructureactivity
relationship(3D-QSAR)modelsweredevelopedonadatasetof21moleculescomprisesof
naturallyoccurringcurcuminoidsandsyntheticcurcuminanaloguesactiveagainstALR2.
Moleculardocking(MD)typicallyusesanenergy-basedscoringfunctiontoidentifytheener-
geticallymostfavourableligandconformationwhenboundtothetarget.Itpredictsthebind-
ingaffinityandexploresthebindingmodeofinteractionsofligandswiththekeyaminoacid
residuespresentattheactivebindingsiteofthetarget[5].3D-QSARmodelsareessentialfor
thegenerationofapharmacophorerequiredtofacilitatemolecularrecognitionandbinding.
Fig3.Tautomericformsofcurcumin.
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Theprimaryaimofa3D-QSARtechniqueistoestablishacorrelationofbiologicalactivitiesof
aseriesofstructurallyandbiologicallycharacterizedcompoundswiththespatialfingerprintsof
themajorfieldpropertiesofeachmolecule,suchasstericandelectrostaticpotential[5,40].Fur-
ther,athree-dimensionalpharmacophorewasgeneratedasanoutcomeof3D-QSARstudies,
thegeneratedspatialfingerprintsorpharmacophoricfeaturesmappedandcanbeusedforthe
designinganddevelopmentofnewerALR2selectivecurcuminanalogueswithhighpotencyas
wellasimprovedpharmacokineticprofileforthemanagementofdiabeticcomplications.
Methodology
Datasetandbiologicalactivity
Adatasetof21moleculescomprisesofcurcuminoidsobtainedfromCurcumalonga(com-
pound1–3),andsyntheticcurcuminanalogues(4–21)wasselectedforpresentstudy[32].
Datasetwassplitintotrainingsetof18molecules,andtestsetof3molecules.Thedivision
wasdoneinsuchamannerthatthreecompounds(1,3and12)shuffledinthetestset,repre-
sentingthestructurallydivergentfeaturesofmoleculespresentintrainingsetwithawide
rangeofALR2inhibitorypotential.However,thefinalcompoundsintrainingandtestsets
weredecidedbasedonthehighestQ2andR2valueswith85%compoundsintrainingsetand
theremainingcompoundsintestset.ThereportedIC valuesofdatasetmoleculeswerecon-
50
vertedintopIC (pIC =−logIC )toarrangethedatainascendinglinearmannerforthe
50 50 50
QSARanalysis(Fig4).
Molecularmodelling,docking,andalignment
Thepresentmolecularmodellingstudieswereaccomplishedwiththeuseofdifferentsoftware
packagesnamelyMolegroVirtualDocker(MVD6.0.02013)[41],VLifeMDS3.5[42],
SOMFA2.0.0[43],TSAR3D3.3[44],andVegaZZ3.0.3.18[45].Firstly,thestructuresofdata
setmoleculesweredrawnusingChemDrawUltra8.0;thenthesesketchedmoleculeswere
convertedinto3Dandsubjectedtoenergyminimizationtoattainthestableconformation
withthelowestenergyusingChem3DUltra8.0.Thegeometricaloptimizationwasperformed
withthesubsequentuseofdualoptimizersviz.molecularmechanics(MM2)followedby
Hamiltonianapproximation(AM1)availableinMOPACmodule.Theimplicitsolventenvi-
ronmentorsolventeffectwastakenforgeometricaloptimizationswhichreplacetheexplicitly
representedwatermoleculeswithamathematicalexpressionthatreproducestheaverage
behaviourofwatermolecules[5].Thegeometricaloptimizationprocesswasruntilltheroot-
mean-square(RMS)gradientvaluereachesavaluelesserthan0.001kcal/molÅinboththe
optimizationtechniquesmentionedabove[1].
ThegeometricallyoptimizedconformerswereimportedintotheworkspaceofMVD
(MVD2013.6.0evaluationversion)alongwiththeALR2(PDBentry:4JIR,ALR2fromHomo
sapiensandco-crystalizedwithEpalrestataswellasNADP+)[46].Whileretrievingtargetmol-
eculefromproteindatabank(PDB),theassociatedwatermoleculeswereeliminated,and
NADP+importedasco-factor.Allthemoleculesintheworkspaceweresubjectedtomolecular
preparationtoassignmissingbonds,bondorders,hybridization,charge,explicithydrogens,
triposatomtypesanddetectflexibletorsionsinligands.Potentialbindingsitesalsoreferredto
ascavitiesoractivesites(1–5)(Fig5)wereidentifiedusingthebuilt-incavitydetectionalgo-
rithm.Duringthiscomputationalprocess,themaximumnumbersofcavitieswerefixedto5,
gridresolution0.80Å,minimumcavityvolume10Å3,maximumcavityvolume10,000Å3and
probesize1.2Å;whiletheotherparameterswerekeptasdefault[1].
MDsimulationswerecommencedintothelargestcavity(1)correspondingtoco-crystal-
izedEpalrestatbindingcavityof4JIR(Fig6)todeterminethebindingaffinityandbinding
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Fig4.Chemicalstructureofcurcuminoids(compound1–3),syntheticcurcuminanalogues(compound4–21)alongwith
theirobservedALR2inhibitoryactivity(IC ),pIC ,predictedpIC ,residualactivity,anddruglikeness/ADMEscreening
50 50 50
Data.TTestsetcompounds;moleculeviolatingdrug-likeness/ADMEscreeningdueto:*molecularweight>500,and#LogP>5.
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Fig5.Predictedbindingcavities(1–5)(green)withinALR2(secondarystructure).
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modeofinteractionsofdatasetmoleculeswithALR2.AsyntheticARIpresentlyavailablein
themarketviz.EpalrestatwastakenasareferencetochecktheaccuracyandreliabilityofMD
simulations,andthebindingaffinitiesofdatasetmoleculeswerealsocomparedwithanARI
ofnaturaloriginnamelyQuercetinintermsofdockingscores.Fortheevaluationofdocking
solutions,gridbasedscoringfunctionMolDockScore[47]wasselectedat0.3Ågridresolu-
tions.MolDockSimplexEvolution(MolDockSE)searchalgorithmwithnumberofruns10
andpopulationsize50wasselectedforperformingMDsimulations[1].Thenumberofruns
specifiesthenumberoftimesthatthedockingsimulationwasrepeatedforeachligandchosen
tobedockedandeachoftheserunswasreturningtoasinglefinalsolutioni.e.pose.Theonly
negativelowest-energyrepresentativeclusterwasreturnedfromeachofthemaftercompletion
ofdocking,andthesimilarposeswereremovedkeepingthebestscoringone.Theclusters
wererankedthroughthesimplecomparisonbetweentheconformationsofthelowestbinding
energyineachcluster.Theotherparameterssuchasmaximumiterations,energythreshold,
bindingradius,SEmaximumstepsandSEneighbouringdistancefactorweresetto1,500,100,
15Å,300and1.00,respectively.Forclustersimilarposesaswellasignoresimilarposes(for
multiplerunsonly),theRMSDthresholdwasfixed1.00Å[1].Theposeorconformationof
eachligandwiththehighestMolDockscorewasselectedfortheanalysisofits’stericand
hydrogenbondinteractionswithALR2.
Further,thelowestbindingenergyconformersofallthedatasetmolecules(1–21)obtained
fromMDsimulationswerealignedseparatelybytwodifferentalignmentapproachesbasedon
molecularweighted(MW)extentandmomentsofinertia(MI)usingTSARpackage.TheMW
andMIalignedstructureswereexportedinto.cssrfileformatandfurtherconvertedto.cTF2
usingfileformatconverterpresentinSOMFApackagebecauseitisreadilytakenbySOMFA
software.
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
Fig6.Cavity1(green)withco-crystalizedepalrestat(red)anditscorrespondingaminoacidresidues.
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Generationof3D-QSARmodels
The3D-QSARmodelswereconstructedonalignedmoleculesobtainedfromMW(model1,
2)andMI(model3,4)alignmentapproaches.Theexternalapplicabilitydomaini.e.testsetof
3D-QSARmodelswasbuiltbycompounds1,3and12.Allthemoleculesin.cTF2fileformat
wereloadedalongwiththeirbiologicalactivity(pIC )againstALR2intotheworkspaceofthe
50
3D-QSARpackage.Onlytrainingsetmoleculeswereusedinthedevelopmentof3D-QSAR
models.The3D-QSARmodelsweregeneratedwith40x40x40Ågridoriginatingat(-20,
-20,-20)withresolutionof0.5Åand1.0Åboth[5,40].Thestericandelectrostaticproperties
ofthedatasetmoleculesgeneratedfrom3D-QSARsoftwareagainstALR2wereusedforthe
developmentof3D-QSARmodels.Thethree-dimensionalstericandelectrostaticmastergrid
mapsweregeneratedfrom3D-QSARatdefinitegridresolutions;representtheareainspace
wherestericandelectrostaticfieldinteractionsareresponsiblefortheobservedbiological
activity.Anindividualcompoundinthedatasetcanbevisualizedinthesegridsandvariation
inactivitycanbebestexplainedbythegrids[5].Further,thegeneratedmodelswereusedfor
predictionoftheALR2inhibitorypotentialofallthedatasetmolecules.
RegressionanalysisbyPLSmethod
Partialleastsquare(PLS)inconjugationwithleaveoneout(LOO)cross-validationtechniques
implementedinVLifeMDSwasusedfortheregressionanalysisofdeveloped3D-QSAR
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
models,inwhichthestericandelectrostaticpropertiesofdatasetmoleculeswereindependent
variableandpIC valueswereusedasdependentvariables.Thesepropertieswerecorrelated
50
withbiologicalactivitytoidentifythethree-dimensionalmolecularpropertiesresponsiblefor
selectiveandpotentALR2inhibitoryactivity[5,40].
Validationofdeveloped3D-QSARmodels
Thestatisticalfitnessofdeveloped3D-QSARmodelswasevaluatedundervariousstatistical
parametersobtainedfromPLSregressionanalysis,suchascross-validatedcorrelationcoeffi-
cient(q2)asaninternalstatisticalindexofpredictivepower(Formula1),correlationcoeffi-
cient(r2)externalpredictivityindicator(Formula2),predictivecorrelationcoefficientr2
pred
(Formula3),standarderrorofestimate(S-value)(Formula4)andFischerstatistics(F-test)
(Formula5)[40].
P
ðY (cid:0) Y Þ2 PRESS
q2 ¼1(cid:0) P cal obs (cid:17)1(cid:0) ð1Þ
ðY (cid:0) Y Þ2 SS
obs average
InFormula(1),Y andY representobservedandcalculatedactivityvaluesrespectively,
obs cal
whileY meansaverageactivityvalueoftheentiredataset.Often,ahighq2value(q2>
average
0.5)isreflectedasanevidenceofhighpredictiveabilityoftheQSARmodel.ThePRESSvalue
isreferredas‘PredictiveResidualSumofSquares’,whichisthedifferencebetweenthepre-
dictedvaluesY andtheobservedvaluesY .TheSumofSquares(SS)denotestothediffer-
cal obs
encebetweentheobservedvaluesY andtheirmeanY [48].
obs average
P
ðYfit(cid:0) Y Þ2 RSS
r2 ¼1(cid:0) P cal obs (cid:17)1(cid:0) ð2Þ
ðY (cid:0) Y Þ2 SS
obs average
InFormula(2),Yfit denotesthefittedvaluecalculatedwiththelinearregression.RSS
cal
refers‘ResidualSumofSquares’whichisdifferencebetweenthefittedvaluesYfit andthe
cal
observedvaluesY .ThenumeratortermSSisthe‘SumofSquares’whichisdifference
obs
betweentheobservedvaluesY andtheirmeanvaluesY .ForthereliableQSARmodel,
obs average
r2valueshouldbegreaterthan0.6[48],andthedifferencebetweenr2andq2shouldnotexceed
0.3[49].
P
½Y (cid:0) Y (cid:138)2
r2 ¼1(cid:0) P calðtestÞ obsðtestÞ ð3Þ
pred ½Y (cid:0) Y (cid:138)2
obsðtestÞ mean(cid:0) obsðtrainingÞ
InFormula(3),Y ,Y andY denotecalculated,observedvaluesof
cal(test) obs(test) mean-obs(training)
thetestsetandmeanvaluesoftrainingsetrespectively.ForastatisticallyfitQSARmodelwith
goodpredictiveability,r2 valueshouldbemorethan0.5.InFormula(4),Y ,Y andn
pred obs cal
representobserved,calculatedactivityvaluesandnumberofcompoundsrespectively[48].
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
P
ðY (cid:0) Y Þ2
S¼ obs cal ð4Þ
n(cid:0) 2
P
ðYcal(cid:0) YmeanÞ2
F ¼ P p ð5Þ
test ðYobs(cid:0) YcalÞ2
n(cid:0) p(cid:0) 1
InFormula(5),Y ,Y ,Y ,nandpdenoteobserved,calculated,meanactivityvalues,
obs cal mean
numberofcompoundsandpredictorvariables,respectively.Furthermore,Svaluecloserto0
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Computationalmodelling,pharmacophoremappingandADMEscreeningofcurcuminanaloguesasALR2inhibitors
andF-testvalueshouldbenearabovethresholdvaluei.e.largertheF-testvalue;greateristhe
probabilitythatQSARmodelsarestatisticallysignificant[48].
Resultsanddiscussion
Computer-aideddrugdesigningtoolssuchasmolecularmodelling,pharmacophoremapping,
moleculardocking,homologymodellingandQSARmodellingaresomeoftherobustap-
proacheswhichhavebeenwidelyemployedbytheresearchersaroundtheworldinorderto
elucidatethetargetstructureorrecognizetheactivesiteswithinthetarget,constructionofthe
targetstructure,virtualscreening,leadoptimization,ligand-targetbindingaffinityprediction,
establishmentofselectivityofligandsagainstaparticulartarget,generationofpharmacophore,
constructionofmolecularlibrarybearingactivepharmacophore,andmoreimportantlyto
hastenthedrugdiscoveryprocess.Inthepresentstudy,integratedmoleculardockingassisted
3D-QSARwassuccessfullyperformedonadatasetof21compoundscomprisesofcurcumi-
noids(compound1–3)andsyntheticcurcuminanalogues(compound4–21)(Fig4)tofind
outbindingaffinityagainstARL2withtheidentificationofmolecularshapeandelectronicfea-
turesofmoleculesresponsibleforpotentARL2inhibitoryactivity.The3D-QSARmodelswere
builtbyusingself-organizingmolecularfieldanalysis(SOMFA)proposedbyRobinsonand
co-workers[5,40,50].
Initially,thegeometricallyoptimizeddatasetmoleculesweresubjectedtomoleculardock-
ingsimulationswiththeALR2forconformationalsearchaswellasdetectionofmodeand
extentofbindinginteractions.ALR2(EC1.1.1.21)isa36kDaTIM-barrel-shapedaldo-keto
reductaseconsistsofasinglepolypeptidedomainof316aminoacidresidues[46,51].The
polypeptidechaincloggedattheaminoterminusendsintoaβ/α-barrelstructuralmotifcon-
tainingeightparallelβstrandswhichareconnectedtoeachotherbyeightoutlyingα-helical
segmentsrunninganti-paralleltotheβsheet.Theactivesiteislocatedinalargeanddeepcleft
intheC-terminalendoftheβbarrel,andtheNADPHcofactorbindsinanextendedconfor-
mationtothebottomoftheactivesite[52,53].However,itisprospectivethattheactivesite
oftenchangesitsconformationalshapedependingonthebindingconformationsofbound
ligand.Theligand-dependentconformationsoftheALR2indicatearemarkableinducedfitor
flexibilityoftheactivesite[54].Threedistinctbindingpockets(Fig7)intheactivesiteof
ALR2canbeprojectedaccordingtoX-raycrystallographyandmutagenesisstudiesperformed
onALR2[55–59]:(1)‘anionbindingpocket’,madeupofTyr48,His110,Trp20,Trp111
aminoacidresiduesinunionwiththepositivelychargednicotinamideofthecofactorNADP+;
(2)‘hydrophobicpocket’or‘specificitypocket’linedbytheaminoacidresiduesLeu300,Cys
298,Cys303,Trp111andPhe122[55];(3)another‘hydrophobicpocket’constitutedbythe
aminoacidresiduesTrp20,Trp111,Phe122,andTrp219.Thespecificitypocketdemonstrates
ahighdegreeofflexibilityandtheconstructingresiduesofthispocketarenotconservedin
otherAKRsincludingALR1[57].
Moleculardocking(MD)simulationswereperformedintothecavity1havinglargestsur-
facearea(149.76Å2)andvolume(51.712Å3)(Table1)outofatotalfivecavities(1–5)(Fig5)
searchedwithintheARL2(PDBentry:4JIR)[46].Thekeyaminoacidresiduesofcavity1
nearbytheproximityof6.00ÅincludedAla299,Arg296,Cys298,His110,Leu300,Leu301,
Phe122,Trp20,Trp79,Trp111,Trp219,Tyr48,Val47andVal297(Fig6).
MolDock,Re-rank,andH-BondscoreswereobtainedafterthecompletionofMDsimula-
tions(Table2).Thedockingviewofmostactivecompound9withALR2isalsodepictedin
Fig8.TheMolDockscoreshowsthequalityofbindingfitnessandplausibleorientationof
ligandwithintheactivesiteofthetarget.TheRe-rankscoreusesaweightedcombinationof
thetermsusedbytheMolDockscoremixedwithafewadditionterms.Re-rankscoring
PLOSONE|https://doi.org/10.1371/journal.pone.0175318 April11,2017 10/22