Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm

8/6/2019 Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm

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Study of a llighly Accurate and FastProtein-Ligand Docking AlgorithmBasedon Molecular DynamicsM. Tauferl,2,3, . Crowley2,D. Price2,A.A. chien l, C.L. Brooks II2'3

Dept. of ComputerScienceandEngineering 2 Dept.of Molecular Biology (TPC6)University of California at SanDiego TheScrippsResearch nstituteLa Jolla,Califomia92093,U.S. La Jolla,California92037,[email protected] taufer,crowley,priced,[email protected] or Theorctical Biological PhysicsLa Jolla.Califomia92093.U.S.

AbstractFew melhodsusemoleculardynamicssimulationsbasedon atomically detqiled orce fields to study theprctein-ligand dockingprccessbecaasehelt are considered ootime demanding despite ,heir accuracy. In this paperwepresenta docking algorithn basedon molecular dy-nauics simulations which ha a highlyfexible compu-tqtionql granulafity, Wecomparcthe accuracy ond thetime requircd with well-known, conmonly rsed doch-ing methods ike AutoDock, DOCK, FlqX, ICM, andGOLD. We show that our algorithm is accurate, fastand, becouseof its lexibility, applicable even o looselycoupleddistributed systems ikc deskap grids for dock-ing.Ket'rtrords: Force eld based methods,docking accu-racy, desbtopgrid computing.

1 IntroductionA vast number of the essential oles that proteins playrequire small molecules o bind to speaificspots n theprolein struchLrre. or instance, he small molecules canact as switches to tum on or o1f a protein function, orare tlle substates for the particular chemical reactionthat a protin enzym catalyzs. Obtaining the atomiclevel details ofthe protein-ligand interactions s a valu-able tool in the developmentof novel pharmaceuticals.Conventionalexperimental och[iques for obtaining de-tailed structural iDformation aboutproteitr-ligand com-

plexesare ime and rssourcenttrsive.As aresult, muchresearch ffort hasbeen ocusedon computationalmeth-ods or thepredictionofftis dimcuh-to-obtain structuralinformation. In general, his process s called docking.Current docking algorithms typically use a fast, simpli-fied scoring function to direct the conformational searchand select he best skuctures.However, ecent v{ork hasdemonstrated hat there are significant inaccuraciesas-sociatedvrith these algorithms ul. Fudhermore, thereare indications that inaccuraciescan be reduced by us-ing algorithms that use more sophisticatedphysics. Forexample, CDOCKER, a docking algori&m based onmolecular dynamics MD) and a conventionalmolecularmechanics orce field, indeedprovides better accuracythan other methods. However, t is still amoug hc morecompute-intensivemethods. Our aim is to adapt theCDOCKER method to improve its performance with-out sacrificing the acculacy. n pafiicular, v/e would l iketo takeadyantageof advancesn computer echnologiesand the establishmentof new disaibuted architcctures.such asdesktopgrids.Desktop grids provide a viable aud inexpensive solu-tion to the hiiherto uncompetitive computational costand time for the force field methods. New algorithmswith finer computationalgranularity need to be deyel-oped,especially algorithns more suitable for the highlyvolatile nodesof desktopgrids. In this paper ve prosentan algorithmfor the dockingprocessbasedon MD simu-lationsas n [2], but characterizedby a higher floxibilitythat makos t adaptablc o any computingplatform, even


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to very challengingdesktopgrids.Docking many ligaDds o tlle sameprotein followed byscoring hemfor their relative strengthof inkraction hasbeenproposedas a procedure o identiff candidatesordrug development. Screoning arge databases f com-pounds n this manner can potentially provide an alter-native to conventionalhigh-throughputscreening,but itis not cost-efectivc unless he docking algorithm is fastand accurate,Most ofthe well-known, ommonlyused ockingmeth-ods that are not based on MD were comparedand an-alyzed in detail iD [3]. We validate the accuracy ofour algorithm by appllng the tests defined in [3] toour method, and compare o the published rcsults forthe ollowing othermethods: utoDock 4], DOCK [5],Flexx [6], ICM [7], and GOLD [8]. We show hat ouralgorithm is indeedmore accurate han all other meth-ods except ICM. We reach a docking success ate ofover 70olo, onfirming the accuracy eported n [2]. Thetime required or running a completdocking attempt slonger but comparablewith the time of the other meth-ods. Thefne computationalgranularityofour algorithmis trivially parallel and each simulation attempt is de-composable nto independentsub-jobs. This flexibilitymakesour accuratedockhg simulations fastwheD hereare rnany independentcompute nodes,and thus, appli-cable to a wide range ofplatforms from traditional su-percomputerso loosely coupleddishibuted systemsikedesktopgrids.In Section 2 lve present our docking protocol as wollassome well-known and commonly used docking algo-rithms that we will compate with our method. In Sec-tion 3 we define the motrics of accuracyand time thatwe use to validate our method while in Sectiotr4 wecompareour methodwith the other docking algorithmsbased on those menics. Finally, in Section 5, $'e dis-cuss he applicability ofour dockingprotocol to deskopgrids, and future work comfug out oftlis research.2 The MD-based Docking Algorithm2.1 The CHARIVIM Scientific ConputationCodeWe use CHAFMM to perfom MD simulationsand investigate he protein-liganddocking process.CHARMM s aprogramor simulating iologically el-evantmacromoleculesproteins,DNA, RNA)andcom-plexes hereof 9]. It allows he invcstigation f thestructure Dd ynamics f largenolecules n solvent rcrystals.CHARMMcanbeusedocalculatereeenergydiferonces ponmutations r ligandbinding 0]. Oneof themostoommon pplicationsf CHARMM s MD,

in which the NeMonian equations of motion are dis-cretized and solved numerically by an inicgration pro-cedure such as the Vcrlet algorithm. The force on theatoms s the negativegndient ofthe CHARMM poten-tial energy unction u ll.2.2 ModelingProtein-LigandlnteractionsAdvances n energycalculation techniquesmake it vi-able to use a gid-based reprcsentationof th protein-ligaDd potential intemctions to calculate our sconngfunction. A grid potential allolvs us to rcpresenta rigidprotein binding site as a potential field magnitude ateach grid poirt. Protein interactions with the ligandin the binding site are interpolated from the interactionstrength of the gdd points neareachatom of the ligand,ratler thanftom computing hc intenctions ofall ligandatoms with all proteiDatoms individually, resulting inordeB of magnitude fewar floating-point computation$than in the tnditional molecular mechanicsmethod.In a prcliminary phaseof the docking simulations,weoalculate hree dimosionalgrid maps or eachof the 20potentialatomtypscomposing he igandsuoder nves-tigation.Eachgrid map oonsists f a threedimensionallattice of regularly spaoodpoints surrounding and cen-teiedon the active siteofa prolein. Eaahpoint within thegrid map storcs the potential energy of a 'probe' atomdue to its interaction with the macromolecule. For ex-ample, in a carbongrid map, the value associatedwitha gdd point represents he potential energy of a carbonatom at that ocation due o its intoractionswith all Etomsof the protein reccptor, We have chosen agrid spacingof lA based on previous work that showed no signifi-cant di{ferences n docking accuracyfor grid spacingsbetween .25Aand I A [2].To facilitate thepenehationof small igands nto thepro-tein sites and allow larger oonfigurationalchanges,vander Waals (vdW) and electrostaticpotentialswith softcore repulsions [ 2] were utilized iNtead of the traditionalpotentials.A soft core epulsion educeshe po-tential barier at vanishing nteratomicdistances o a fi-nite limit. In this case, igands canpassbetweenconfor-mational minima with arelatively smallpotentialbarderthat would normally be infinite and impassiblewith anunmodifedpotential.23 MD Docking ProtocolAs in most of the existing methods, we model theprotein-ligand complex as composedof a rigid prcteinstructure atrd a flexible ligand. A flexible ligand hasthree translationaldegreesof fteedom, three rotationaldegreesof freedom and one dihedral rotation for each


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rotable bond. The docking search s computed oYera6+ n dimensionalspacewhere n is the numberofrctablebonds n the igand. Figure I shows he MD-basedalgo-rithm used or our docking simulations. One loop con-stitutes a docking trial. Given a protein and a ligandto dock nto its bind ing site (a so-called rotein-ligaDdcomplex), dockingattempt onsists fa sequencefnindependent rials. For each rial, a random configura-tion for the ligand is generatedby running 1000 stepsof MD at the constalt temperature of 1000K in vac-uum, stafiilg from a reasonableshucture with randominitial velocities on each ligand atom. We have ana-lyzed the distribution of torsional anglesgeneratedbythis method, and found that they indeed vary randornlyover the physically reasonable ange for each rotablebond. We are confident that our ilitial configurationsrandomly sample he available configurationalspaceofthe ligand.Starting from the new ligand configuration, a set of10 ditrerent orientations are randomly generatedanddocked into the receptor, that is, moved into tho cen-ter of the grid" Once the randomized igand has boendocked nto the active protein site, we run a MD simu-lation consisting ofa heatingphase rom 300K to 700K,followed by a cooling phaseback to 300K. Finally, werefine the simulation result by running a short energyminimization. In the end, we use he energy of bitldingas the scoring function to rank the docked ligands andretum the lowest energy structure asthe solution to thedocking trial. Iwenty trials were run for each complexto ascertainie optimal number oftrials that should coD-stitute an attempt at docking.2.4 The Other Docking MethodsCoomon search teohniques for predictitrg bindingaffinities and geometries are based on genetic algo-.ithms, chemistry, geometryof atoms,Monte Carlo orMD. Selection of best docked shucturcs is performedusing scoring functions belongingto three different cat-egories; explicit force field scoring furctions (as inour case), empirical scoring functions, or knowledge-based scoring functions. AutoDock, DOCK, FlexX,ICM and GOLD are well-known, commonly usedpro-gramswhich use a variety ofsearchmethodsand scoringfunctions to addresshe study ofproteinligand docking.AutoDock [4] uses the Lamarckian genetic algorithm(LGA) by altemating local search with selection andcrossover The ligands are ranked using an energy-based scoring function and, to speedupthe score cal-culation, agrid-basedprotein-ligand nteraction is used.GOLD [8], like AutoDock, deploys agneticalgorithmand uses a scoring function which is the sum of enelgy

Figure t. Our llD-based prteinligand dockingalgorithm,

terms, some of which reflect the short-rangevdW in-teractionbetweenprotein and ligand as well as the lig-ald intemal eoorgy.The search n DOCK [5] is drivenby the geometry ofthe tigand in the active site. Dif-ferent scoring firnctions can be employed: (l) geomet-ric alignment and shapccooshaints,(2) the elechostaticpotential of the protein-ligand complex using tlle pro-gram DELPHI, or (3) the etrergy of the protein-ligandcomplex under the AMBER force field. FlerX [6] isalso driven by the geometryof the ligand in the activesite like DOCK. Ia Flexx, the scoring usesa vadationof the Bdhm scoring function with trtrls for severalkitrds of intenctions atrdpenalty functions for the devi-ations from ideal interactiongeonetries. ICM [7] usesa Monte Carlo minimization on the nternal coordinatesto find the global minimum ofthe scoring unction. Thescorhg function used o rank placemeutsof ligands rel-ative to one aootler takes into account the force feldenergy of the ligand and the prctein-ligand interactionenergy.3 Metrics3.1 AccuracyThe accuracyof anygiven docking attempt s measuredby the root-mean-square-deviationRMSD) of all non-hydrogen igand atomsbetweetl h owest-energystluc-tule ftom tho docking attempt andthe ligand's positionin the c4/stal sfucture. For many ofthe ligands studiedhere, d dihedral rotation can result in a ligand conforma-tion that is geometricallyand chemically indistinguish-able, but nith a different RMSD relativo to the experi-


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Protein Proiein-LigardComptexPDB EtrrryllypslnCytochrcme P450.a,NeuraminidaseCa$oxypeptidaseL-Arabinosee-ThrombinThermolysinPenicillopepsinIntestinalFABPCa$onicAnhydrusel

jprb(3).rhg(2), ln j (3) . 1nk(4),r I (5) . t rpp(7).pph( l l )lph(l), lphs(s),2cpp(3)lnsc(I2), nsd(l ), lnnb(l 1)l cbx(s),3cpa(8), cpa(16)I abe(a), abf(s), 5abp(6)let(I5), let(l 3), et(l l )3trnn( 0), 5tln( I 4), 6tnn(20)lapt(30), apu(29)2ifb( 5)lcil(6), I okl(5), cnx(I3)Table1. Dataset ofthe 31 protein-ligandcomplexesused or our erperiments.Thenumberof rotablebonds for each ligand is reportedbeside he complex name.

mentally determinedstructure. That is, the RMSD be-tweena docking attempt alrd the crystal structureusinga one-to-onemapping of atomsmay or may not accu-rately measure he quality ofthe docking attempt. Con-sequently,we have exhaustivelycalculatedthe RMSDof all degenerate onformersrelatedby the rotation ofall symmetry-confedng dihedral angles. The lowestRMSD obtained rom this searchs guaranteedo be thecorect RMSD for the structure.Reference 3] providesan additionalmeasuredescribingthe ftequelcies wherehigh-quatity dockingsolutionsarefound. For many docking attempts, he dockiug accu-racy (DA) can be definedas follows:

D'4 - /R:rD


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ing phases.Table 2 shows he four casesandthe associ-atednumbei of I fsec MD steps.Figure 2 shows heDAof our MD-based methodwith different number oftrialsper attempt and diferent lengihs for the MD simulation(eachcase s reported n Tabte2). In the figure we labeleach attemptwith li where i is the number of itrdepen-dent trials per protein-ligand docking attempt(i rangesftom one to twenty). By looking at the datareported nFigure 2, we concludethat rryeneed about 10 trials peratiemptto rcacha docking accuracyof70%.

bei of MD stepsdudng the heating and cooling phasescausesan almost linear increaseof the simulationtime.

I CAsc r K:.sK I Case :4Kl0KI Casc : K5K F CascD: tKl0K

Figuro 3. Ayerage time in seconds pel trial andwith different number ot ltlD steps Per simula-tion.We have run several experiment! with l0 and 20 trialsand haye confirmed that tie results shown in Figure 2and Figure 3 are repeatable data not shown). For ourcomparison n tie rest of the paperwe use Cdse B as areferelce case or which we run 2000 MD stcpsdudngthe heatingphase,and 5000 MD stepsduring the cool-ing phase. Each MD step coDsistsof I fsec time step.The DA, RMSD and time values or the other mothodsreponed n Section2.4 and used n our comparisonsnthe rest ofthis paperare from the previouswork of ourgroup 3].43 Comparison ofthe Docking Accuracy (DA)Figue 4 compares he DA ofthe well-knom methodswith the DA of our MD-based method for CaseB inwhich eachMD simulationconsistsof2000 heating MDstepsand 5000 coolingMD steps. By looking at thedata reported n Figure 4, we obscrve hat our methodprovides better DA than atl the other methods, oxceptICM. ICM employs an algorithm which improves con-vergenceby usiug an analytical gradientminimizer andrunning multiple Monte Carlo minimizations fiom sev-eral starting configurations. We plan to make a morcdetailedstudy of MD and Monte Carlo simdlations forthe docking process n the near future.

Case lleatingPhase# MD steps CoolnrgPhase# MD slepsCaseA - lK2.5KCaseB - 2K5KCaseC - 4Kl0KCaseD - 8K20K

000400080002000 250050001000020000

Table2. The our different ilD simulations,eachwith a differentnumberofMD stepsforthe heat-ing and cooling phases.

FFFFFFFFF-F-FFF-F-FTnumber oftrials-*- CaseA: lK2.5K --r- CaseC:4K10K--.- CaseB: 2K5K -r- CaseD: 8K20K

Figure 2. Docking Accuracy (DA) for difiercntnumber of trials per docking attempt and withdlfferent number of llD steps per simulation.

Figurc 3 shows he averageime in seconds er trial andwith diferent number of MD stepsper simulation as re-porhd in Tabte2. As fipctd, the increase of num-


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Proteirr-l igandornpler # rotablebonds AuloDock DOCK FlexX ICM GOLD 0 t:0lptbI togI tnjI tnkItniItppIpphIphfIphg2cpplnsclnsdI nnbt cbx3cpa6cpaIabeIabf5abpI etrI ets1tt3tmn5tln6tlnnl aptI apu2iIblcilI oklI cnx

32.l45'7

l lI53l2

l l5Itt6456

l5l3l0l4t03029l55l3

0.800.62t.2lt .692.61LttO5. 42.093.s2i.401 40I 200.92t.3i2.228.100.160.480.484.615.06fr_2.+.515.i,1ta.'72I _899.103.095.{.8.5410.9

0.590.1t61.56l_1J75.263,153.911.395.5'72.4r\4.864.514.513. 36.48lt.30| .873.2s3.trg6.663.9i1.331.O9t .39'7.1t\8.067.5uI .432.'785.657.35

1.08I _7it . '102.131.95i.:74.684.8'7o.446.00t.560.92| .32l.5l9.tt30.550.764.681.26t. l I6.245. i06.334.515_958.438.943.526.83

0.490.72.1'72.533.40l .7l| ..t4l .2l0.462.53l_tiot.04L090.ti20.771.600.360.610.8tt0.87(r.-ll0.99l.-16t .421.600.882.021.042.001.032.alg

l . {J9t .n9t.903.01{4.934.2i4.124.203.49|.020.960.84l. t t71.874.960.1tt0.500.595.992.391.303_9 61.60u.54tt.tt210.702.616.043.556.32

0.560.701.42t. r6, 'r'l2.434.001.20t.013.261.471.851.67o.622.224.000.560.680.481.091.970.823.65l.2l11 15.721.322.O91.862.U6.20

0.540.691.50l.14tr)2.530.s31.201.083.271.401.853.9'7o.622.224.000.56o.700.51I .091.970.823.65t.2l2.214.'79tl t5. 91.862.U6.20

Table3. Comparisonof best RI'SD tor different docking methods. The best RitSD is the RtISD ofthe predicted igand rom the Xrayatructure. For eachprotein-ligandcomptex, he best RMSD oundis repoded in bold.

4.4 Compariron of RMSD for the DifferntDocking MethodsThe RMSD's reported in Figure 3 are the root meansquare deviations of the heavy atoms of the predictedligands from the corresponding ignds in their pub-lished complex crystal shuctures. For our MD-baseddocking, we present results of attempts with differentnumbers of trials: I10 with l0 trials per attempt andI20 with 20 trials per attempt. In general,we observethat for both 7 I 0 and I20, we get,on the average, ower

RMSD than the other methods.4.5 Comparison of Simuletion Tlme for theDilferent Docking MethodsThe main question we want to address n Table 4 iswhether the high level of accuracy s also supportedbycompetitiveexecution ime when comparedwith the ex-ecution times of the other docking methods, Table 4shows the averageCPU time to complete a protein-tigand docking for the ten proteins in Table I and for


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Protein AutoDock DOCK l-lexX ICM GOLD 'n 1'10 'r20-lllpsurCytochromeP450".''NeuraminidaseCarboxypptidaseL-Arabinosee-ThrcmbinThermolysinPenicillopepsinIntestinalFABPCa6onicAnhydmsel

39129162062435 31114789tt22560519

5l299888

421l? 0412138))

268272928365772988

654099t413933 623 864592

16527326943728 8676s0084048938 8

8llt4l l l

520 3l4tt2'7614 5107

ti05tt46l l10I1567662036t4832760t4501070

t6l0169322202313l53i40732966552029002 140

Table4, Comparisonof average ime simulations for different protclns and difforent docking meih'ods.

AuioDock DOCK Flerx ICM OOIFigure4. Compadson of docking accuracy (DA).The docking accuract/ is tho weighted sum ofthe fraction ofdocked attempts with acceptableacculacy (lower or equal to 2A and 3A).th diffrcnt methodsunder nvestigation.For our MD-baseddocking method,we consider he average ime ofa single trial as well as he time for an attemptof l0 and20 trials. Again we consider he Case B n Table 2 tsa referencecase. We observe hat an atlcmpt of l0 hi-als is campleted n less han onehour svenfor complexproteinJigand docking with a large mrutber of rotabledegreesof freedom. In addition, each rial of each at-tempt is independent, nd herefore, he l0 hials can rutrat the same ime on difrcrent processors n parallel. Ifenoughprocesson are available, he time for completinga protebJigand docking becomes he time for a singletriat, making our algorithm highly competitivewith theothermethods.

5 Computotional Plstforms for our MI)-basedDockingMD simulationsare time-consumingbut are also accu-rute general echniqucsfor the study of protein-liganddocking. The time neededby MD-based algorithmsto screen arg setsof ligands (of th order of 10,000molecules) makes his approachprohibitive evenon ex-pensive supercomputers. On non-dedicatedsystems'even the docking of a single pmteinJigand complexmight result in a time-to-solution on the order ofhoursdue to computingrcsourcecontentiotrs.The motivationto port existing applicationsto morc cost-efrectivedis-tributed systemsike dasktopgrids is not shongfor suchapplicatiotrsunlessmore time-effective algorithms aredesignedand mplemented.The ned or nen, algorithmsthat are more flexible andsuitable or desktopgrids,but still accurate,s the moti-vation behind our search or the docking algorithm pre-sentedn this paper.Docking attemptsofour MD-basedalgorithm consistof sequences f independentrials. Wehaveobservedaudmeasured lat attemptsor evencom-ptex ligands c.ith a large number of rotable bonds arecharacterizedby short simulation times, much shortrthan t hour. By decomposingeachattcmpt into setsofindependent rials, we can fiuther increase he compu-tatiouat granularity of the algorithm. Using availabledesktopPC's simuttaneously o processeach rial, pro-portionally decreaseshetime to solutiotr. Long compu-tation tasks, which are mor probableto be intefiuptedby annoyeddesktopusers,should alsobe avoided. Ourresultshows hat we canensute he time to solution o beequal o the time for a single rial when a argeaumborof


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II

desktopPCs s available.An acceptableaccuracycan beensuredby sending out moro fiials than are needed orthe desired accuracy,and using the flIst trials to com-plete.Therefore, we conclude that our docking algorithm iswell-suited for Intranet desktopgrid platforms (e.9.,En-tropia DCGrid F4l, Intuzion [5]) and on the Intemet(e.g.,Xtremweb[16], BOINC n7l). The conbinationof our algorithm with suchplatforms, which might al-low us to perform fast and accuratescreening of verylarge ligand databases,s currently under our develop-ment and nvestigation.6 ConclusionIn this paperwe preseDta MD and detailed force fieldprotein-liganddocking algoriffn basedon agrid repre-sqtrtatiodofthc prctein-ligand interactious and soft-corepotential. We prove that our docking method providesbetter docking accuracy tlan most of the other well-known and commonly used docking techniques, dis-playinga successful ocking ateof70%.Basedon our time comparisons,we claim that the com-putatiotral time is no longer a justified rcason to avoidusing detailed force field based docking tecbniques.Even for complex ligands, the completion time for aprotein-ligand docking attempt of l0 trials is modest(less than one hour on a 930MHz processor or ligandswith large numbersofrotable bonds). Deshop gdd plat-forms are wg -suitgdfor our accuute, fine-grainedpar-allel algorithm for which each docking trial is shoft andindependent.AcknowledgmentsFinalcial support rom lhe NatioDal nstitutes ofHealth Grant,GM37554, is greatly appreciated. FiDarcial support throughthe NSF sponsoredCenter for Theoretical Biological Physics(grant# PHY-0216576aod 0225630) and the LJIS Fund arcalso ackrowledged. Supported n part by the National Sci-etrce Foutrdation under awaids NSF EIA-9-75020 C'rad6and NSF Cooperative Agreement ANI{225642 (OptlPuter),NSF CCR{331645 (VGTADS),NSF ACI-0305390, ndNSFResea.ch nfiastructure GTa EIA-0303622. Support fromHewlett-Packar4 BigBangwidth, Microsoft, and Intel is alsogrotefilly acknowledged. We wish to thaDk h Sar Diego Su-percomputerCetrter at UCSD for giving us access o the Me-teor cluster.Referencesul P. Fenara,H. Gohlk,D. Price,c. Klebe,and C.L.Brooks tr. Assessing coringFunctions or Protein-Liga[d InteDctions. Med.Chen.,2003.Slubmit],ed.

[2] G. Wu, D.H. Rotrertson, C.L. Brooks III, ood M. Veith.Detailed Analysis of Crid-Based Molecular Docking:,A.. ase Study of CDOCKIR-A CHARMm-Based MDDocking Algorithm, J. Conput. chemistry, (24).1549-1562,2003.[3] B.D. Bursuleya, M. Tohov, R. Abagyan, and C.L.

Brooks IL Comperative Study ofseveral Algorithms forFelxible Lig8nd Dockig! . J. Comp.Aided Molecular De-ri8n, 2003. n press.[4] G.M. Moni6, D.S. Goodsell,R.S. Halliday, R. Huey,W.E. Hart, R.K. Belew, atrd A.J. Ol$on. AulomatedDocking Using a Lemarkiatr Gerctic Algoritbm and andEmpirical Binding Free EDergy Function. J. Comp.

Chem..19 1639-1662. 1998.[5] T.J.A. Ewing and I.D. Kuntz. Critical Evaluation ofSesrcl Algorithds for Autometed Molecula. Dockingand Database Screening. ",| Comp. Chem., lEtllT6-1189. 997.[6] M. Raiey, B. Kramer, T. Letrgauer, ad G.A. Klebe.A Fast Flexible Docking Method using an Incrmedel

Construction Algorithrtr. I Mol. Biol., 261:470489,1996.[7] R. Abagyan, M. Totrov, and D. Kuznetsov ICM - ANew Melhod for Proteir Modeling and Design: Applica-tions to Docking snd Shucturc Predictiotr ftom the Dis-torted Native Conformation. J. ComD. Chen.. 15:.488-506,1994.[8] G. Jones,P-Willett, R.C, Glen, A.R. Leach, andR. Tay-lor. Developmentand Validation of a Genetic Algoritbmfor flexible Docking. ./. Mol. 8io1.,2671727-748, 1991.[9] B. R. Brooks, R. E. B ruccoleri,B. D. Olafson,D. J.Stales,S. Swaminathan,andM. Kamlus. CHARMM: AProgram or Macromolecular EnerBy,Mioimizatioo, andDynamicsCalculatiors.J. Comput.Chen., 4:181117,

1983.[10] X. Koog and C.L. Brcoks. lambda-dynamics: A newApproach to Free Energy Calculations. J, Chem.Phys,lI5t24l4-2423, 1996.ll l A.D. MaoKqell Jr. ald et al. All-atom empiricsl PG.tential for Molecular Modeling and DyDamicsStudiesofProteins. I Pt,r. Chen. B,102:.3586-3616, 1998.u2l M. Vieth, J.D. Hirst, A. Kolitrski,and C.L. Brooks tr.Assessing Ener$f Functions for Flexible Docking. ICornp. Chem stry, l9(l 4'1:612-1622, 1998.u3l C.L. Brcoks III and ot al. LPDB: Ligard ProteinDataBase. pdb.Ecripps.edu/.[l4] Entropia. www.eDtropia.com.u5l EnFuzion. wwwaxceleon.cofi .[6] C. Fedak, C. cermain, Ndri V, aDd F. Csppello.Xtremweb: A Generic Global Computing System. InPntc. of CCGRID 2001, Workshopotr Global Cornput-ing on PetaonalDevices, May 2001.[7] BOINC. boioc.berkeley.edu.

Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm

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Transcript of Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm