computer approaches to enzyme substrate docking

8/6/2019 Computer Approaches to Enzyme Substrate Docking

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ComputerApproacltestoEnzyme-SubstrateDockirg

BrandonFranzkeCHEM519:McKennaApril14,2004

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0.AbstractSimulations hat model enzyme-substrateocking are increasinglypopular in biochemicalresearch nd hepharmaceuticalndustry. By carefulselection fan algorithm,which ncludessearch unction and scoring function, an immenseconfigurationspace s reduced. Differentassumptionsre made n eachalgorithm hatmightnot be valid for particular nteractions,uchas ligand rigidity or zero electrostaticnteraction. Following the experiment,analysiscandeterminewhich molecule rom a library of thousandss the mostpotent nlibitor or whetheraproposedmechanisms probable. In this paper,a review of docking simulations s presentedfollowedby an llustrativecasestudy.

l. IntroductionComputersimulationsof enzyme-ligand inding are complicated imulations hat occur n thevirtual realityof a computer'smemory. Billions of forces, equiring rillions of calculations reperformedn each analysiswith the goal of determining recise nteractions.Determining hecourse f only a few nanosecondsn reaction imeoften akesminutesofprocessing ime.

The problem of enzyme-ligandbinding is broken into two tasks, predicting ligando.i"nt"tion undbindirrguffinity.-[ idJllgorithm shouldconsider othof the facetsandallowdetermination f both pre- and posrbindingphenomenon.However,such equirements renotyet computationally racticaland studiesusually fall to the most relevantof the two. Leadd_etections oneproblemhatchooseso ignorehe mechanistic"lu,!!j!j!g ltngtng ro""r.dnd ocus nlyon hebinding ffinity. Leaddetectionnvolves hoosing few compoundsrom'------3faslllgyg_pitlntial ligandshataremost ikelyto elicita desiredesponsehen eactedwith heenzyme,nhibitionor instance. fter h;;le;-ion oiJ. .u.,aiout"r.he ocus hifts oelucidating the actua1mechanism of reaction and determine ft" rg-:gt",ty,re!utio$!ig(SAR). From this data, suggestionsor novel speciesnot in the original library that meet heconstraints osedby the SAR (e.g.stedc,electrostatic,romatic onsiderations)s possible.


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2. APPlicationand SignificanceApplication of these methods quickly enteredthe field of pharmaceuticals ecause hedevelopment f drugs s commonly ocuseddirectlyon the actionsof a singleenzyme. often,estimationsof the target enzlT e's structureare enough o facilitate the designof designerligandstailoredto react n an optimal way. For many enzymesstructual information is obtainedfrom crystallographicstudieswhich generateapproximationsof theprotein conformation. onceinitialized n the computer, elaxationmethods ring theprotein nto a physicallymore accurateform, and the simulationsare run. Thousandsof trial compoundscan be acceptedor rejectedwith relatively high confidence n the spaceof the computersmemory and a few hours. Despitethis, successfulsimulationsoften take weeks or months to prepale and program and a workingknowledgeof the tools andthe advantagssanddisadvantages feach is required to implementareliable simulation. In this paper I will focus on the three of the most widely used computersimulation methods: Molecular Dynamics (MD), Monte Carlo (MC), and Fragment Based.others such as genetic and evolutionary algorithms and point-complimentary methodswill bediscussedriefly. At theconclusion, casestudywill be briefly presented hich illustratesheapplication f theseechniques.

3. Overview of Computer ModelingTraditional enzymeJigand modeling algorithms are broken into two separatepieces,.a searchstrategyand scoring firnction. The search unction must determinewhich configurationsare tobe examinedat each ime-stepand ideally must include a guaranteehat the actualconfigurationis among lose selected. For a simple systemcomprising a ligand with four rotatablebondsandsix rigid body alignmentparameters, ngularsamplingat 10oand spatialsamplingat 0.5A, thereare over 2.4 x 10rspossiblealignments.l Searching heseat even l0 configurations secondwould take over 2,000,000 years of computationl If the simulation was to determine thecompoundwith the highest affinity for the protein ftom the 2.7 million known compounds, twould ake rillions of years.The search spacemust be reducedeven farther than allowed by traditional spatial andtemporal sampling. In many simulationsthis involves making the restrictive assumption hat theligand and enzymebehave n a completely rigid manner. This reduces o 6 degreesof rotationaland dimensional freedom of the ligand allowing simulations to be performed in seconds. Formany interactions his assumption s not a limitation becauseprotein conformationchangesittle


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duringbindingeven f changes fterbinding esult. Many ligandsalsohold theirconformatronssteadyuntil completelybound o the proteinor exhibit freedom n only one or two predictabretorsionaldimensions. However, this assumptions invarid in some casesand onry ..hard,,chemistryor other techniquescan be usedto identify them. More powerful algorithmsareabreto maintainconsideration f flexible rigandsand the most powerful even incrudesupport orflexibleenzymebindingsitesby makingcompromising ssumptionslsewhere r usingheuristicapproacheso eliminate ,.dead_end,'branchesof the search ree.A nearlyuniversalsimplification hat is made n the simulationss an ln vacuo(withinvacuum)environment.This has he mportant onsequencefremovingaqueousmoreculesuchas water that sometimespray criticar rores in the binding process. This limitation wasdocumented uring the study of HIV-I transcriptasenhibitorswhich requiresseveralwarermoleculeso successfurydock with the protein.3In the absence f water, he reactiondid notproceedas expected.Newer simulationmodelsare designedo incrude he effectsof soiventmoleculeso somedegree, ut their nclusions somewhatd hoc,After successfur ompletion of the simulation some degree of measure s usua'yrequired, for instance-b-l4d!4g tlniry or inhibitory activity (e.g. pIC5e,the amount of rigandrequired o inlibit 50% to obtain50% nhibition). In simulationsike these he scoring unctionsare very complicated and may require millions of calculations per compound. Often intemalsimplifications by considering only the most important subsetof intramolecular forces such ashydrogenbondsor van-der waars interactions. Becauseof this high cost,simprerfunctions suchas RMSD (root mean-squared istanceor Euclideandistance)are chosen(Figure 1). Thisscoring function has an obvious disadvantage hat the actuar placement of the ligand in thepeptidecomplex must be known a priori. calculations ike theseare used very often tobenchmarkone algorithm to anotherby consideringa set of test compoundswith known finalconfigurations. comparisonof the RMSD valuesthen gives some idea of their resoectiveperformance.


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Figure 1: SampleRMSD measurencom.p,exefinis,cil6ffi;ii"jli,il14!,!l:'iliTi;?Jff.^"::il5#l,f,l;fl:T:i:::#,,:i:nosen nd somemeasure f their displacementss made. This ii a usefulmethod n cases herea sDecil iconfiguration f reactiveatoms drives th e interacti; (i"*". *t*trr A tempralemorecures usedan dJ'"ff:::';:;l1ffuasainst it,carredaximum;;;" i;;;;;"ili" rr..r. rhishas ppricarionncases4. SpecificMethodsOverviewI. Molecular Dynamics (MD)

Molecurardynamics imulationsarea brute orce echniquen enzymeJigand ockingproblemsthat can historicalrybe considered he first introducedsimulationmethods Figure 2). Thealgorithmsequirecalculationof the sorutionso Newon,s equations f motion at each imestep.From the sorutiona gradientdescents chosen n an attempt o minimize the energyat eachframe' The gradientoperatorassociates ith eachpoint on the energysurfacea vector n thedirectionofsteepest escent. terativelystepping long hesegradient ectorsdrives hesolutrontowardminimas. with ruck, hesevalleysw'l conespondo globalminimas, hose hat are hemlnlmum of the entire surface' However,nost often locar-mrnimas re encountered.Lesssophisticated D algorithmsarepragued y these esultswhiremorerobustmethods reable o"step"out ofthese ocarminimasby,addingaberrationso the solution argeenough o stepover


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the nearest eak. Becausehe MD simulationsare depea.denttheirresultsdepend eavilyon the nitial molecular tate.on iterativeminima searching,

{, nr ths i&&dr(!| 6c iilp ,, f.11rr$& |. edcr,.q,!{,!nrdi*S n.5dtt.| rn n{tq, mrrir'nmofcmt*rt bt qXf o*c$rnpkx$ &RU:} * nc etnrptcqrFigure 2: One method of morecurardynamics simurationsusing the Qx? force fierd moderand GRrD energyalculation' (1) The target protein is selecteduoa it. iniiiai

"o"nio.ri"fion i, determined, (2) The molecutarnteraction potentials (MIP) are complted in the protein. ta a aj iie MIps are then used o determine thehere the ligand may bind' (5 & 6) calculations to n"a ,rl" "pr'r'."r binding configuration which coincidesith the global energyminimum,r

The earliestmorecurardynamicsimulations elied on making simprifting assumptronsduringthe nteraction. one constraintwas regarding he rigidity of the enzymeor the dockrngligand (seesection3 for description).For mostmoleculardyramicssimulations,he proteinnostis consideredo be a rigid body,while the freedomof the ligand(rigid or flexibre)varies romalgorithmto algorithm. The simprifying assumption n the immob e protein is imperativebecausehe solutionso themotionequations ecome omputationalrynsolvabrewith the addeddegreesof freedom ntroducedby the hundred'sof amino acids and rotatablebonds in theprotein For many interactions his does not pose a serious"onr"ou"*. u***-*---_inconformation hanges renot invorved n theactuarigandbinding. But new.studiesavestartedillustratingclassesof proteinswhere this assumptions less valid. Note that this does not

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discount mechanismsthat result in a post-binding conformational changes such as 02to

hemoglobin, nly thosewhich change uringbinding More modemMD methods' uchas hoseproposed y Wang and Pak,allow flexible ligands o be considered ut the computationalosts

are higher. Becauseof thsse assumptions'simulations basedentirelyon molecular dynamics

calculations have become less relevant as . more powerful (statistical reliance andffiuonrrr, techniquesecomevailable. imulationssingMD progressionsor near-optimal initial conditions are very powerful and are implg-qe^ntedn the software packagesAMBERandCHARMM'

IL Monte CarloMonte Carlo methodsarearguablythe mostwidely implementedmethod' The name tself stemsfrom the gameRoulette (common in Monte Carlo) in which a player placesbets on a subsetofsquareschosenrandomly from the entire event space' The algorithm proceeds n a similarmarrnerbygenelatingasmallsuqsgt'oftlptotalsetofpossibilities.Thissetisgeneratedbyconsidering he currentstateandperttu-bingt in someway. Following the generation,evaluationisperformedbased'onasetofcriteria'suchaspotentialsnergystateortotalslerichindrance.Thesetofthemostlikelymodelsisextracted,andthesearethenprocessedusingMDmethodsto;;e ;*0, local minimas. fne towestmlni-um is selectedas he current state,andthe process

repeats.ThisdesignovercomesthesensitivityoftheMDmodelstotheselectionofinitialstatesbychoosinghundreds(orthousands)ofpossibiestatesandchoosingthebesttoproceedwith.Thechoiceonwhatelementsareallowedtobeselectedduringtherandomassignment

results n many differing algorithms' The mostly widely implemented s called the Metropolis(orstandard)MonteCarlo.ThealgorithmarbitrarilyappliesrandomCartesianmoves(hencethenameMetropolis)tothesystem'Thefitnessofeachmoveisevaluatedusingitsenergy'basedontheBoltznrannprobabilrtydistribution.Thisallowsthealgorithmto..decide''whetlerthestep is likely or not. In the event very highly probable stepsare chosen' the algorithm canimmediatelyaccept his asthe new stepor minimize it using the gradient-descentmethods(e g'AMBER) beforeproceeding o tho next step' knplementationdetails like this havemade heMCmethodsveryatEactivetomolecularchemistsbecausetheyintroduceflexibilitywhichcanbeexercisedinareductionofthecomputationalexpenseatthecostofasmalldegreeofaccuracy.The MC functionality is provided to chemists n the packagesAutoDock and Prodock' amongothers. Each of theseprograms has the capability to deal with flexible ligands' and includes


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allowancesor flexibrebinding regions n the enzyme. Additions ike these ncreasingrymakeMC basedprediction superiorto their entirely MD basedcou#marts.III. FragmentBased

Fmgmentbasedmethodsare incorporated ato one of the most successfulmolecular simulahonpackages,DOCK as well as others such as ADAM and FlexX (Figure 3). Fragmentmethodsbreakthe ligand molecule into many smaler sub-pieceswhich arethen sequentiallybound intothe enzyme head to tail. During each docking stage, MD gradient-descentor similar costminimizingmethodsare applied o obtain he time-dependentrocessof the fragmentdocking.The resultsproducedby thesemethodsare astounding ften yielding resultswitrr RMSD Iessthan 1A from experimentallyobtained esults. Thesemethodsare used very often for leadmethodsbecause hey can quickry evaluateonly a few fragmentsand determine if the subjectwill makea viablecompound. In a study elated o searchingor the mostefficientbinding othe streptavidinprotein from a reference ibrary ofg0,000 candidates,biotin (a ligand with a veryhigh affinity for binding to streptavidin)wascorrectryprcdictedas he highest-scoring igand.


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FlexX

Figure3:TheFlexxalgorithmstructure.Majorreactivesubstructurescalledfragmentsarepart i t iondfro-m tre igand. Each o?these s optimally bou;d in turn to the target protein. Many near-oltimal.binding;;;iig;;"ii;;; ;.. averaged ogethir to obtain a more accurate representation. Torsion-angle relationshipsareu.Sedtodterminetheoptimalinter[al-al ignmentofthebou[dl igand'Final ly,theresultsarerunirriougrr aaditional processing o bring the reconstituted-ligand-enzyme omplex to its lowestenergy.

Because nly fragmentsare considered t a time, complete lexibility can be affordedintra-fragment. As the fragmentscome together hey introduceconstraintson the availableorientationof proceeding ragments. This advantagemust be weighed heavily against hesensitivityof the algorithmto the fragmentationmethodology. This problemis severewhenfragmentationesults n functionalunitsof the igandbeing nadvertently isjoined,causinghefunctionalityo be lost in the process.This results n omissions f critical regionsof theenergysurfaceesulting n inaccuratemechanisms.Expertisen the chemistryof the functionalunits srequired or any suchsimulationand oftenmanypossible ragmentationmethodsare simulatedfor results omparison.This processs successfully utomated y the MIMUMBA torsionangledatabase,whichhasbeenincludedintheFlexXpackage'Thishasmadethefragmentbasedmethods ery popular despite he fact that most of the algorithms ack the ability to considerflexibleenzymes.

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OtherMethods , theAside from the threemethodsdiscussedso far' many other methodshave seenacceptancen

field, summarizedn brevity below'

IV. SystematicSearchesSystematicsearchesperformdirecttranslationandrotationoftheligandandenzymetoobtaintheoptimalsolutionateachtimestep.Becauseoftheimmenseconfigurationspaceitissampledto yield a reduction in computation cost' The search s coupled with a fast affrne transformallowing the search o proceed n a realizableamountof time' This searchdiffers from all of theothermethodsbecauset doesnot descendalong somehajoctory in an attemptto find the globalminimum of the energysurfacebut attempts o exhaustivelymapthe configuration space nto thescoringfunctiontoobtaintheminimum.ThismethodwasoriginallydevelopedintheSYSDocpfo$amandthenewerEUDocextendsthecapabi l i t iesoftheear l ierproglam.ThenewestEUDocpfogamisstilllimitedtoasystematicsearchofrigidbodyrotationsandtranslationsofa rigid ligandwithin a rigid activesite'

V. GeneticAlgorithmsandEvolutionaryProgtammingGeneticprogrammingrsarelativelynewdesignparadigmthatgeneratesartifrcialcompetitionbetween likely candidates and proceeds using a survival of the fittest heuristic' Salientcharacteristicsofacandidatearecodedintogenes.Duringeachtimestepoftheiterativedockingcalculationnew..offspring,'arecleatedbyrandomcombinationsofthecharacteristicsofthemost successfulmembersof the previous generation previoustimestep). In addition' crossoversandpoint-mutationsareallowed.Byrecombiningthe..fittest,'membersfromeachgenerationnew combinations emerge that drive the mechanism oward the most probable course' Thecrossovers nablepreviouslysuccessful ortions o be repeatedwhile point mutationsenablenovel genes o emerge. Geneticand evolutionarymethodsare also applied o many areasofstudy including transistor circuit layouts andmathematicalN-P complete tlpe problems' Whiletheiraddit iontotheenzyme-l igandsimulat ionf ieldisrelat ivelynew(


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VI. Point-ComplimentaryPoint Complimentarymethodsspana wide rangeof simulation echniques. Thesealgorithmstend o deal specificallywith stericeffectsand elatedcouplingeffects i.e.Hydrogenbonding).The scopeof thesealgorithms s somewhatimitedby the fact that until only recently hey wereconstrained o considerationof rigid receptorsand ligands. This is obviously a seriousdisadvantagewith respect o enzymeJigandsimulation, however the simplifications invoked inthe simulationmake it ideal for studying complexprotein-proteinnteractions. Many point-complimentary methodscan even be applied to the interaction of four or five proteins in super-complexes. The point-complimentarymethodsgenerallyconsider he enzymeand its dockingpartneras space-filling models. Often small cubesare used o generate he volumes because he6 transversesides make calculating force interactionsbetween aces much easier. New methodshave also applied spherepacking algorithms attempting to increase he accuracy. Once spacemodels for each componentare created he algorithm generates he mechanism hat minimizesthe stericcollisions of molecules. The modelsare then fine tuned o maximize he effectsofHydrogenbonds and other coupling forces. New methodshave been developed hat allow forsome "soft docking" to try and compensate or the requirement of rigidity and have beenintegrated nto the packageFLOG (flexible ligand orienting grid). MULTIDOCK and FTDOCKare two software packages that pedorm the more classical form of point-complimentarysimulation.

VII. Taboo SearchesThe taboosearch s a stochastic volutionof the generalized coring unction ChemScore.3 tdescends he gradient curve in a manner similar to the MD methodsbut instead samples hespaceat a few discrete regions. To ensurediversity of the processa FIFO (first-in-first-out)taboo ist is kept with the 25 most recently usedconformationsor states. During the selection aset of typically 100possiblesolutionsare generated nd scored. If the lowestenergysolutionfrom the rankedpopulation is the lowest energyso far, it is always acceptedas the new .currentsolution'. Howeveq f it is not the owestenergyso far,the bestnon-taboo olution s thenused.A move s consideredtaboo' if it is within an RMSD of 0.75Aof any of the solutions tored nthe taboo ist. This process s repeatedduring each teration for a user defined numberof times.At the conclusion of this, the final cunent solution is defined as tlre configuration at that timestep and heprocess s continuedfor eachstep.


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VIII. MultipleMethodsThemost uccessfulimuration ethodsurrentryvairableombine everarf these lgorithmsintoonecohesivenit. Thiswas ntroducednderMontecarlo,whichusesmolecurarynamicinimizationso yierdgrobarminimas.Thegreatestenefitn combiningmethodss to gamerthestrengthsromeachsutcoupredsub-a,gorithms;3:lT;l:"':::ffi:';_T::il'J"?:,T"::Til::i:considering flexibre igandbinding o a flexibleproteinscafford. The gain comes n arowingsimplermethodso computegrossmacroscopicmoders f the mechanismwhichare henrefinedby finer and finer computationswith more powerfi:l argorithms(such as systematicsearchesorirect molecurardynamic calculations). More benefitsare reapedby alrowing pruning ofmprobablemechanismst the computationallyin-expensivemacroscopicevelwhile such .deadends,' annot bepredictedn a detailedalgorithms ntil theyare eached.

5. CaseStudy2Makhija ndKurkarnippliedD-QSARqualitativetructure-activityerationship)ethodsohe screeningof HIV_I integrase nhibitors. The HIV_retrovirusospliceintegrate)tsown eneticateriatith;:trT:J,Hr;#: |L:::of3 Steps:l' 3',processingThe nte$ase roteins leaveswonucreotidesrom each trand fDNA generatedy HIV reverse-transcriptase.Thisexposeseactive H species n each3,end.2' Endoining' Theprocessed'endsare henjoinedo 5, endsn thehostDNA at hesiteofintegration. his s often alled tand ransfer.3 DNA repairsynthesisHostDNA repairasesre ecruitedo rigate hegapsgeneratedduring he ntegrationrocess.Fo'owing his, hevirarRNA is successfurymbeddedin thehostandsynthesiseeins.

Twosimuration oders,oMSIAandcoMFAwereappriedo the nhibition robrem. oMSIAandcoMFA are wo differentmolecular ynamicsype simulations.Eachmakesdifferentassumptionsimprifoinghe force-motionelationso reduce he configurationpace o a


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some of the enzymeJigand interactions, the coMSIA presentedgreatel intemal and externalpredictioncapabilitieswhen comparedwith crystallographicstudy results.

In addition to determining a single number indicative of the ligands inhibitory potential(measuredas pIC5s ligand concentration o reduceenzymoactivity by 50%) the coMFA andCoMSIA are able to generate sensitivity" mapsof the mechanisticbinding between he ligandandenzyme(Figure 5). Powerful mapsare derivedby correlating the characteristicsof eachofthe tested igands with its activity. This gives the ability to include more than just a pictorialrepresentationof the interaction but also indicate regions in the binding complex that dependheavily on the presenceor absenceof electrostatic orces and steric collision. Maps like thesecan beused o extend he initial databaseo novcl compounds,even a novel molecular class, hatmeet heconstraintsoutlined by these equisite nteractions'

Figure5 Maps ndicating patialsensitivity f the nteractiono electrostaticleft)and steric right) factors'Oi tne tett ior two seleitedcompoundsn the HIV-1 Integrasenhibitor casestudy' On the right' blue,"giona"nao-p"a, regionswherean increasen positive harge nhancesffinity, whereasn redcontoureduil"r n"g"ti"" .rt"rgei are avorableor binding. on the eft' gren ontours nclos reaswherestericbulkwill enhanceflinitj andyello]v ontoursndicateegionshat shoutd ekept unoccupied--{therwiseindingaffinitywill decrease.


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6. ConclusionThemethodsf computer imurationppriedo enzymeJigandinetics asbenefitedreatry ytheever-pressingdvancefof seconds. imulation"Oao.out". technology,nablingrillionsof calculationsn a matter

theedge f research,uchas arebecoming oresophisticaledndwell ncludingopics nmathematics,u.r,u,toporog;Tffi :;:lH#ffi'H:."::,#:::;:#;T;verycomprextatisticarperationsoobtain grobarninima ftlrereactionubsurface,ach asimitations. heseimitationswithrhemorecurardyramicsJ::;'"1.ffi;:"?::::'ffi ::IIT"jHJH:';*:::benefactor s he echnologycontinueso mature.This is a resurtof the fact thatthe soration fsingle target protein allows an array of compounds o be virtually interacted with it. Theompounds hat generatethe highest affinity are refined koptimalindingfnciencies.n heend oweveE".r..;"T;'i;J::"*rHt::",'#nlypropose possiblemecluin a specificnteraction--careusm or mostprolific bindingaffinities(perhapshemost ikely)results. UIchemistry ust ollowanysuccessfulimulationoconfirmhe


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7. Referencescarrieri,A., A. carotti,et al. 2002). Bindingmodels f reversiblenhibitono type-Bmonoaminexidase.Journal f Computer-Aidedolecular esign 6(1 ):769'778'Makhija,M. T. andV. M. Kulkarni 2002)."3D-QSAR ndmolecularmodeling f HIV-Iintegrisenhibitors. Journal f Computer-Aidedolecular esign16(3):8l-200.Taylor,R. D., P. J. Jewsbury, r al.(2A02)."A reviewof protein-small olecule ockingmethods.Joumal f Computer-Aidedolecular esign 6(3):151-166.J.

computer approaches to enzyme substrate docking

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