AFsample3：使用 AlphaFold3 生成和选择多种构象状态 AFsample3 Generating and selecting multiple conformational states with Alphafold3

bioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.AFsample3:GeneratingandselectingmultipleconformationalstateswithAlphafold3aDivisionofBioinformatics,DepartmentofPhysics,ChemistryandBiology,LinköpingUniversity,58183Linköping,SwedenbScienceforLifeLaboratory,LinköpingUniversiAccuratelycapturingtheconformationaldiversityofproteinsisessentialforunderstandingtheirmechanismsandregula-tion.However,currentstructurepredictionapproaches,in-cludingAlphaFoldderivatives,arelargelylimitedtomodel-ingonedominantconformation.Improvedsamplingmethodshaveexpandedthistopredictingtwostates.Here,wepresentAFsample3,anenhancedsamplingframeworkbuiltuponAl-phaFold3thatsubstantiallyimprovesthegenerationandselec-tionofdiverseproteinconformations.Acrossabenchmarkof238non-redundantproteinswithmultipleexperimentallydeter-minedstates,AFsample3significantlyoutperformsAlphaFold3anditspredecessorAFsample2byimprovedpredictionsfor28%(67/239)oftargets(ΔTM>0.1)whiledegradingonly3%(8/239),andincreasesthenumberofhigh-qualityalternate-statemodels(TM>0.8)by54%(from54to83;p<0.0001).AF-sample3improvesalternate-stateaccuracyfor28%oftargets(ΔTM>0.1)whiledegradingonly3%,andincreasesthenum-beroftargetswithhigh-qualitypredictions(TM>0.8)byover50%comparedtostandardAlphaFold3.Ensemblediversityisalsomarkedlyenhanced(p<0.0001),enablingaccuratemod-elingofpotentialintermediateandoradditionalstates.TheseresultsdemonstratethattheimprovedsamplinginAFsample3cancapturemultiplenative-likeconformations,representingasignificantadvanceinmodelingofproteinconformationalland-scapes.Code:/AFsample3Sampling|Alphafold|Conformations|Correspondence:bjorn.wallner@liu.seIntroductionProteinarecriticalmacromoleculesthatserveasthebuildingblocksofalllife.Theyplaycrucialrolesinalmostallbio-logicalprocessesincludingmetabolism,cellularcommunica-tion,DNAreplication,amongothers.Ingeneral,functionalaspectsofallproteinsarehighlydependentontheirthree-dimensionalorientation.Insolution,this3Dconfigurationisnotstatic,instead,aproteinsystemassumesdifferentshapes(conformations)dependingonfactorsincludinglocalenvi-ronment,stageofacellularprocess,cell-type,bindingpart-nersandothers.WhilegenerativemethodslikeAlphafold2(AF2)(1)havebeenveryeffectiveinmodelingproteinstruc-tures,theirdefaultimplementationlacktheabilitytogen-erateconformationalheterogeneity.Toovercomethis,sam-plingandperturbinginputdatarepresentationsoftheMul-tiplesequencealignment(MSA)hasproventobeveryef-fectiveininducingthegenerationofalternativeconforma-tions.TheseperturbationsmaybeintheformofMSAsub-sampling(2),clustering(3)ormasking(4,5).RecentlyAl-phafold3(AF3)wasreleasedasanupdatetothepreviousver-sionthatemploysdiffusion-basedarchitecture,amongothersignificantchanges(6).AF3cangenerateanall-atommodelforagivenqueryincludingnucleicacids,smallmoleculesandions,whichisasubstantialimprovementtotheAF2in-ferencesystem.Giventhis,itisimportanttoprobetheAF3inferencesystemforitsabilitytomodelmultipleproteincon-formations.Here,wepresentAFsample3,analternatestrategybasedontheAF3inferencesystemforgeneratingalternatepro-teinstates,andbyextension,morediverseconformationalensembles.LikeAFsample2(5),itworksbyintroducingnoiseintheinferencesystembyrandomlymaskingthein-putMSArepresentationsatruntime.Themethodwastestedon238proteinshavingatleasttwodistinctexperimentallysolvedconformations(seeMethods).Wereportsignificantlyimprovedperformanceinbothgeneratingalternatestates,andalsogeneratinganoverallmorediverseensemblethanbaselinemethods.Forinstance,AFsample3wasabletogen-erateveryhighqualityalternatestate(TM-score>0.80)for83targetsintheCfolddataset,asubstantialimprovementoverbaseline(AF3vanilla:54,AF2vanilla:32),aswellasexistingmethods(MSAsubsampling:67,AFsample2:63).Moreover,AFsample3alsoreportsasubstantiallyincreasedlevelofconformationaldiversity(asquantifiedbyfill-ratio)inthegeneratedensembleswhencomparedtoothermethods.AFsample3alsoimprovesthereference-freestateselectionsystemusingadistance-scoring(DiSco)protocol,which,inprinciple,canidentifyanarbitrarynumberofconformations.Interestingly,theselectionsystemidentifiedpotentialnovelconformationsformultipletargetsthatsatisfyknowntarget-specifichypothesis’intheliterature.Takentogether,AFsam-ple3providesanoptimalsystemtogenerateandselectpro-teinconformations,withoutanyadditionalnetworktrainingontopoftheAF2/AF3architecture.Also,thedevelopedsys-temhaspracticallynocomputationaloverheadthanwhatisalreadyrequiredforthedefaultAF3inferencesystem.GiventhearchitecturaldifferencesinAF3,theseresultsandbenchmarksprovidesimportantinsightintowhetherpertur-bationstrategiesremaineffectiveorrequireadaptationinthenewerAF3model.ThisstudyunderlineskeyimprovementstotheAF3inferencesystemoverAF2,particularlyrelatedtotherobustnessinperformanceonperturbedinputdata.Takentogether,theseresultsalsoserveasareferenceonthepre-dictiveperformanceofbothnetworks(AF2andAF3)onacomprehensivedatasetforthetaskofgeneratingproteinwithmultipleconformations.Kalakoti|bioRχiv|January16,20bioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.n]Fig.1.OverviewofAFsample3protocol.GivenadatasetofproteinswithvaryingdegreeofsequencelengthandTM-score(similarity)betweenknownconformations,theAFsample3protocolrandomlymasksmultiplesequencealignment(MSA)inordertobreakevolutionarysignalsateachiterationoftheinferencerun,makingthesystemmoresusceptibletogeneratingadiverseconformationalensemble.MethodsDatasets.ArecentlycurateddatasetnamedCfold(7)thatcompiledtargetswithalternateconformationsintheProteinDataBank(PDB)wasutilizedinthisstudy.Thedatasetcon-tainstwostatesfor238proteinsthatarenotsimilarthanTM>0.8,representedbythestructureswiththelargestdiffer-enceinTM-score,inthecasetheproteinhasmorethantwostates.Figure1summarizestheoveralldistributionofrefer-encestructuresimilaritiesaswellassequencelengths.TheconformationalchangesinTMscorerangesfromaround0.3to0.8,denotingawiderangeofconformationalvariabilityinthedataset.Also,thereferencestructuresdivergemorewithincreasingsequencelength.Structuregeneration.Standardandnon-standardpipelinesbasedonAF2anAF3wereemployedtogeneratemod-elsfortheanalysis.ThedefaultimplementationofAF3v3.0.1wasmodifiedtohavethefunctionalityofMSAmasking,aswellasMSAsubsampling,byrestrictingthemax_aliparametersimilartowhatwasdoneforsubsam-plinginAF2(2).TheMSAs,whichistheonlyinputtoallmethods,weredirectlyretrievedfromCfoldZenodorecord(/records/10837082)andusedasinputforallpredictionsonalltargets.AFsample2(/afsample2:v1.1)andAFsample3(/afsample3:v1.0)wasusedtogeneratestruc-turesfortheAF2,andAF3networks,respectively,andforallthe24differentcombinationsofsettingsinTable1.Intotal,1000modelsforeachofthe24settingsand238targetsintheCfolddatasetsweregenerated,amountingtoaround6millionmodels.SettingsOptionsAlphaFoldVersion[AF2,AF3]MSAMaskingLevel[0.0,0.1,0.2,0.3,0.4,0.5]MSASubsampling[False,True]Table1.SetofparametersemployedtostudytheeffectofMSApeinthedifferentAlphaFoldneuralnetworksonthetheabilitytogenerateproteinconformations.Endstateanalysis.TMalign(8)wasusedtocalculatethesimilarityofthemodelstotheavailablereferencestatestode-terminewhetherbothconformationswerepresentinthegen-eratedensembles.Themodelsmostsimilartothetwoendstates,representingthebestpreferredandalternateconfor-mation,respectively,werecomparedbetweeneachmethod,settingandensemble.Ideally,agoodmethodshouldbeabletogenerateatleastonegoodmodel(forexamplewithTM-score>0.8)forboththeconformations.Ensemblediversity.Inordertoinvestigateandcompareconformationaldiversityofgeneratedensemblesbetweenmethods,avisualizationschemecalleddiversityplotwasde-fined,whereTM-scoresofallmodelswiththetworeferencestatesinagivenensembleareplottedagainsteachother.Thisvisualizationprovidesanimmediatesenseofconformationalpreferenceforagivenensemble(Figure6d).Toquantifythelevelofdiversity,ourpreviouslydevelopedmetric,fill-ratiowasutilized(5).Inshort,thefill-ratioquantifieshowmuchofthediagonalinthediversityplotispopulatedbymodels.Itworksbyprojectingallpointsinthediversityplottoadiago-nalgoingbetweenstatesandcomputingaweightedfractionofpopulatedtounpopulatedbinsonthediagonal.Themetricwasupdatedsothatitemphasizesthepresenceofbothstatesbyusingaparabolicreweightingalongtheprojectedpoints,givingmoreweighttopointsclosertotheendstates.Samplingexperiment.Todeterminehowmanymodelsthatneedstobegeneratetopredictthealternateconformationalstatesasamplingexperimentwasconducted.GivenTM-scores,s0,and,s1,fortwostatesforeachtarget,thebestpossiblescoreforthealternatestatewasextractedasthemin-imumofthemaximumTM-scorevalues,withathresholdsetat99%ofthisvalue.Randomsamplingwithreplacementwasperformedacrosssamplesizesfrom1to1,000formul-tipleiterations,recordingthesmallestsamplesizeyieldingasimilarityscore(minTM)meetingthethreshold.Theex-perimentwasbootstrappedfor100iteration,estimatingtheminimalsamplingrequirementsforconformationalstatere-coveryacrossmethods.SequencedatabaseMultiplesequencealignmentSequencedatabaseMultiplesequencealignmentN:sequencelengthM:MSAdepth2|bioRχivKalakoti|AFsample3bioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.Fig.2.AFsample3generatesbetterendstatesformosttargetsintheCfolddataset.(a)Comparingthequalityofalternatestate(TMScore)betweenfourmethods,namely,af2vanilla,af3vanilla,afsample2andafsample3.ItisclearthatAFsample3generatesbetterendstatesforamajorityoftargetsintheCfolddataset.(b)Theoveralldistributionofqualityofbothpreferredandalternatestatesaresummarized,andtheimprovementsareclearandsignificant(Wilcoxonranksum-****:p<0.0001,***:p<0.001).AFsample3notonlygeneratesbetteralternatestates(left),italsoslightlyimprovesthealreadygoodpreferredstate(right).ThispropertyisdifferentfromthatofAFsample2wherethequalityofthepreferredstatedeterioratesmarginally.(c)Analyzingtheperformanceoftargetswithvaryinglevelsofconformationalchange.(d)Cumulativefrequencydistributionplotsforgeneratingthe(d)alternateand(e)preferredconformations.Statisticalanalysis.AllpairwisecomparisonsweretestedwiththeWilcoxonranksumtestunderthealternatehypothe-sisthatonedistributionissignificantlyhigherthantheother.SciPyv1.6.1wasemployedtoperformallstatisticalanalysis.ResultsHerewepresentAFsample3,thenextiterationoftheAFsam-plefamilyofmethodsthatareaimedatimprovingtheconfor-mationaldiversityingeneratedproteinensembles.AFsam-ple3isbasedontheAlphafold3inferencepipelineandutilizethesameMSAsamplingstrategy,i.e.randomcolumnmask-ing,asinAFsample2(5).AFsample3generatessignificantlybetterconformationsfortheendstates,whileatthesametimeimprovingtheconformationaldiversityoftheensem-blecomparedtoallothermethodsinthebenchmark.InthefollowingsectionswecompiletheresultsthathighlighttheimprovementswhencomparedtotheAlphaFoldbaselines,AF2vanillaandAF3vanilla,aswellastoexistingstate-of-the-artmethodsAF2conformations(2)(MSAsubsampling)andAFsample2(5).Additionally,wealsocompareensem-blediversity,samplingcharacteristics,anddescribeamethodforreference-freestateselectionthatcanbeappliedtoselectmorethantwostates.AFsample3improvesoverAFsample2andAlphafold3forproteinconformationprediction.Allmethodswereevaluatedontheirabilitytogeneratebothconformationalstatesoftwo-stateproteinsystems.Toeliminatevariabilityduetodifferencesinsampling,eachmethodwasusedtogen-erate1,000modelspertarget.Aspreviouslymentioned,12differentsettingsweretestedacrosstwonetworktypesfor238targetsintheCfolddataset,resultinginapproximately6millionmodels(Table1).ItiswellestablishedthatAlphaFold-basedmethodstendtofavoroneconformationalstateinmulti-stateproteins,atrendalsoobservedintheCfolddataset(Figure2b).Accordingly,foreachtarget,thetwoconformationalstateswerecatego-rizedaspreferredoralternatebasedonpreferenceofthede-faultmethod.Figure2summarizestheabilityofallmeth-odstopredictbothstates.AsshowninFigure2a,thede-faultAF3inferencesystem(AF3vanilla)significantlyout-performsAF2vanillaingeneratingboththepreferredandal-ternateconformations.Whencomparingthequalityofal-ternatestates,AF3vanillaproducedmarkedlybettermodels(∆TMscore≥0.05)for56targetscomparedtoAF2vanilla,whileunderperformingforonly23targets.Theremaining159targets(gray)showedmarginaldifferencesinqualityofthegeneratedalternateconformation(∆TMscore<0.05).Interestingly,AF3vanillaperformsonlyslightlyworsethanAFsample2andgeneratesbetteralternateconformationsfor47targetsbutperformsworsefor52.However,AFsam-ple3,usingMSAmasking,clearlyoutperformsallothermethodsingeneratinghigh-qualityalternatestates.Specif-ically,theimprovement-to-deteriorationratiosforalternatestatepredictionare96:14,67:8,and72:20whencomparedtoAF2vanilla,AF3vanilla,andAFsample2,respectively.ItisimportanttonotethatwhileAFsample2oftenachievesimprovedalternatestatemodelingatthecostofslightlyre-ducedqualityforthepreferredstate(Figure2b),AFsample3doesnotexhibitthistrade-off.Instead,itenhancesthequalityofbothpreferredandalternateconformations,highlightingitsoverallrobustness.Sincethesimilarityofreferencestatesvariesconsiderablyacrosstargets,withTM-scoreinthe[0.3,0.8]range(FigureKalakoti|AFsample3bioRχivbioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.4|bioRχivKalakoti|AFsample3Fig.3.EffectofMSAperturbationontheabilityofAlphafold2/3togenerateproteinconformationsforthepreferredandalternatestatesintheCfolddataset.Averagequalityfor(a)thebestpreferredstateand(b)bestalternatestate,(c)averagemodelconfidence,distributionofqualityfor(d)thepreferredstatesand(e)alternatestatesatdifferentrandomizationlevels,(f)numberoftargetsthatisabletogenerateamodelwithin0.1TM-scoreofthebestalternatestategeneratedfromanysettingfordifferentrandomizationlevels.1),itwasimportanttoassesshowperformancedependsonthereferencestatesimilarity.Figure2cshowsthedifferencesinmodelqualitybetweenmethods,groupedbybinsofref-erencestatesimilarity.TheresultsindicatethatAFsample3performsconsistentlywellacrossalltargettypes,particularlyforthemajorityoftargetswithreferencestateshavingaTM-score>0.5.Thisisemphasizedevenmoreintheinversecu-mulativedistributionfunctionshowninFigure2d,whereAF-sample3generatesvery-highqualityalternatestate(TM>0.8)for83targets,muchhigherthanAF3vanilla(54)andAFsam-ple2(63).ItshouldbenotedthatAF3vanillaperformsmuchbetterthanAF2vanilla(Figure2d),pointingtoacombinationofimprovedmodelarchitecture,trainingstrategy,orarobusttrainingsetinAlphafold3.EffectofMSArandomization.AFsample3improvessam-plingbyrandomlymaskingcolumnsintheinputMSAtotheAlphaFold3network.However,theoptimaldegreeofran-domizationvariesdependingonthetargetordataset.AswithAFsample2,arangeofrandomizationlevelswastestedforAF3.Figure3a,bsummarizestheaveragequalityofthebestgeneratedstatesasafunctionoftherandomizationlevelfortheCfolddataset.Inshort,20%and40%randomizationlevelforAF2andAF3,respectively,yieldedthebestoverallperformanceforgeneratingbothstates.The20%forAF-sample2agreeswithwhatwaspreviouslyreportedtobetheoptimalforasmallersetof23proteins(OC23)forAFsam-ple2(5).Unlessotherwisestated,allreportedresultsforAF-sample2andAFsample3use20%and40%randomization,respectively.Inaddition,therearestarkdifferencesbetweenAF2andAF3inresponsetoMSArandomization,withAF2beingmuchmoresensitivetoMSAperturbationthanAF3.Forthepreferredstate,bothAF2andAF3performwellwithnoMSAperturbation(averageTM-score0.85).InAF2,per-formanceslightlyimprovesat10%randomizationbutthendropssharply(Figure3b).Bycontrast,AF3showsasmallsteadyimprovementuptoaround40%randomizationanddoesnotexhibitthesameperformancedeclineasAF2.Lookingatthealternatestates(Figure3a),AF3startsoffsignificantlybetterthanAF2withnorandomizationandmaintainsitsadvantageacrossallrandomizationlevels.Asimilardifferenceisalsoobservedintheconfidencescores(Figure3c),theinternalmetricusedtoassessmodelqual-ity,wheretheaverageconfidenceofthegeneratedensemblestaysrelativelystableandflatinAF3whencomparedtoAF2,whichdropsnotably.Remarkably,AF3maintainsahighcon-fidencelevel(>80)evenwhen50%oftheMSAisrandom-ized,thisismightofcourseberelatedtothefacttheAF3modelsatthisrandomizationlevelsarestillquitegoodcom-paredtotheAF2models.ButitalsounderscorethatAF3ismuchmorerobustandtoleranttowardsMSAperturbations.However,asobservedinpreviousstudies,theoptimallevelofMSArandomizationisproteinspecific(5).Whilemostmod-elsachievetheirbestperformanceataparticularrandomiza-bioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.Kalakoti|AFsample3bioRχivbestalternatebestalternatestate(n=238)avg.TMscoreavg.TMscoreofTM-scoreofTM-scoreofbestalternatestatestatemodel0.625methodAFsample2methodAFsample2AFsample30.575log(p-value)log(p-value)p=0.0510402004006008001000SampleSize1.00200400600800Numberofsamplesrequiredtogetbestalternatestate(99%ofbestTM)Fig.4.Theimportanceofsamplingingeneratingalternatestates.(a)Qualityofthebestalternativestatesasafunctionofgeneratedmodels.(b)ScatterplotdepictingtheamountofsamplingrequiredtogeneratethealternatestateforeachtargetintheCfolddataset.tionlevel,thereareoutliersforwhichboththepreferredandalternatestatesarebestgeneratedatdifferentlevelsofpertur-bation.Effectofsamplingmoremodels.Ithasbeenextensivelyshownthatsamplingplaysacriticalroleintheabilityofthemethodstogenerateconformationalstates,anditremainsacentralcomponentacrossallsuchapproaches(6,9,10).Ingeneral,samplingmoreimprovesperformance,althoughwithdiminishingreturnsasthenumberofsamplesincreases.Thus,thereisatrade-offbetweencomputationaltimeandperformance.Toinvestigatethistrade-off,theeffectofvary-ingthenumberofsamplesonperformancewasexamined.Asdiscussedpreviously,bothAF2andAF3haveaninher-entbiastowardsapreferredconformationalstate.Therefore,thisanalysisfocusedonhowincreasedsamplingaffectstheabilityofeachmethodtogeneratethealternatestate.AsshowninFigure4,asharpimprovementinthequalityofbestalternate-statemodelsisobservedforbothAFsample2andAFsample3upuntil100models,itstillimproveswithmoresamplingbutatamuchslowerrate.Ingeneral,AFsample3consistentlyachievesasignificantlyhighermeanTM-score(averagedacrossalltargets)thanAFsample2atpracticallyallsamplinglevels.Again,similartothecaseofMSArandomizationlevel,theoptimalsamplingsizeistarget-specific.AsillustratedinFig-ure4b,althoughthemediansamplinglevelforbothAFsam-ple2andAFsample3clusteraround300,thereareclearout-liersexistrequiresignificantlylessorsubstantiallymoresam-plingtoachieveoptimalperformance.EffectofMSAsubsampling.MSAsubsampling,whereashallowersubsetofthefullMSAisusedforinference,haspreviouslyshownstrongperformancewithAF2(2).Inourearlierwork,itconsistentlyoutperformedmostalternativeapproachesandrankedsecondonlytoAFsample2(5).Mo-tivatedbytheseresults,andtoexploretheimpactofreducedMSAdepthonconformationgenerationinAF3,weimple-mentedtheMSAsubsamplingstrategyinAF3andcomparedtheperformanceofthepreviousmethodswithandwith-outMSAsubsampling.Interestingly,thebehaviorofAF3withsubsamplingisalmostidenticaltoAF2withsubsam-pling,significantlyimprovingthegenerationofboththepre-ferredandthealternatestates(Figure5).ThisindicatesthatareductioninMSAdepthinfluencesconformationalsam-plinginacomparablewayacrossthetwoversions.Al-thoughAF3vanillaisslightlybetterthanAF2vanilla,theim-provementfromsubsamplingislarge:AF2withsubsam-plingoutperformsAF3vanillaandperformsverysimilarlytoAF3withsubsamplingforthebestalternatestate.Sub-samplingalsoimprovesAFsample2,whileitdoesnotim-proveAFsample3.Infact,subsamplingwithAFsample2isonparwithAFsample3,whichhighlightsthatcombiningdif-ferentwaysofMSAperturbationsimprovesperformanceus-ingtheAF2network.TwopossiblereasonsforwhyAFsam-ple3doesnotbenefitfromsubsamplingcouldbethat,theMSAtrackissmallerintheAF3networkandthattheroomforimprovementislesssinceitisalreadythebestperform-ingmethod.Takentogether,thesefindingsshowthatsub-samplingsubstantiallyimprovesAF2vanilla,AF3vanilla,aswellasAFsample2,buthasnosignificanteffectonAFsam-ple3,whichwithoutsubsamplingremainstheoverallbest-performingstrategyforgeneratingboththepreferredandal-ternatestates.Comparingensemblediversity.Theanalysisthusfarhasprimarilyfocusedontheabilityofthemethodstogeneratedistinctconformationalstates.However,evaluatingensem-blecharacteristics,particularlystructuraldiversity,isalsoim-portant.Toquantifyensemblediversity,weemployedanen-hancedversionofthefill-ratiometric,originallydevelopedforAFsample2(5),thatfocustheattentiononhavingendbioRxivpreprintdoi:/10.64898/2026.01.16.699904;thisversionpostedJanuary17availableunderaCC-BY-NC4.6|bioRχivKalakoti|AFsample3Fig.5.ComparingtheeffectofemployingMSAsubsamplingonAF2vanilla,AF3vanilla,AFsample2,andAFsample3,for(a)bestalternativestateand(b)bestpreferredstate,(c,d)pairwiseWilcoxonteststoascertainifmethodsonthey-axisgeneratebetterstatesthanthoseonthex-axis.00plot0Fig.6.Quantifyingandcomparingensemblediversityamongmethods.(a)Briefsummaryofthefill-ratiometricthataimsatquantifyingthediversityofagivenproteinensemblewhileprioritizingthegenerationofendstates.(b)EmpiricalcumulativedistributionsforallmethodsclearlydemonstratetheimprovementsinducedbyAFsample2/3.(c)Theoveralldistributioncanalsobevisualizedasabox-plotwhereAFsample3issignificantlybatterthanallothermethodsforgeneratingdiverseensembles.(d-f)Diversityplotsandtheassociatedfill-ratiosforthreetargetshavebeensummarized.Theoverallchangeintheconformationalsignatureisevidentfromboth,thedistributionofmodelsintheensemble,aswellastheassociatedfill-ratio.statesbeforemeasuringthediversity.WhileAF3vanillashowedimprovedstatepredictionaccu-racyrelativetoAF2vanilla,thecorrespondinggaininen-sembl