通过结构驱动的计算工作流程从头设计表位特异性抗体 De novo design of epitope-specific antibodies via a structure-driven computational workflow

NatureCommunications

/10.1038/s41467-025-67361-9

ArticleinPress

Denovodesignofepitope-specificantibodiesviaastructure-drivencomputationalworkflow

Received:5June2025

Accepted:28November2025

Citethisarticleas:Wu,F.,Zhao,Y.,Wu,J.etal.Denovodesignof

epitope-specificantibodiesviaastructure-drivencomputationalworkflow.NatCommun(2025).

/10.1038/

s41467-025-67361-9

FandiWu,YuZhao,JiaXiangWu,BiaobinJiang,BingHe,LongkaiHuang,ChenchenQin,YangXiao,FanYang,RuboWang,NingqiaoHuang,HuaxianJia,YuyiLiu,HoutimLai,

TingyangXu,FangWang,ZihanWu,YidongSong,ShaoningLi,WeiLiu,YuRong,PeilinZhao&JianhuaYao

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DeNovoDesignofEpitope-SpecificAntibodies

viaaStructure-DrivenComputationalWorkflow

FandiWu1*,YuZhao1,JiaXiangWu1,BiaobinJiang1,BingHe1,LongkaiHuang1,Chenchen

Qin1,YangXiao1,FanYang1,RuboWang1,NingqiaoHuang1,HuaxianJia1,YuyiLiu1,Houtim

Lai1,TingyangXu1,FangWang1,ZihanWu1,YidongSong1,ShaoningLi1,WeiLiu1,YuRong1,

PeilinZhao1*,JianhuaYao1*

1Tencent,AIforLifeSciencesLab,Shenzhen,China.

*Correspondingauthor(s).E-mail(s):

wufandi@;peilinzhao@;

jianhua.yao@;

Abstract

Accuratemodelingofantibody-antigencomplexstructuresholdssignificantpotentialforadvancingbiomedicalresearchandthedesignoftherapeuticantibodies.Comparedtogeneralproteins,progressinantibodystructurepredictionanddesignhasbeenslow,andantibodydiscoveryisstillbasedontime-consuminganimalimmunizationorlibraryscreeningmethods.Here,wepresenttFoldSystem,ahigh-throughputcomputationalworkflowthatintegratesantibodystructureprediction(tFold-Ab),antibody-antigencomplexmodeling(tFold-Ag),structure-guidedvirtualscreening,anddenovoepitope-specificantibodydesign.Usingthissystem,wedenovodesignmonoclonalantibodies(mAbs)againstfourtherapeuticallyrelevantantigens:influenzahemagglutinin(FluA),PD-1,PD-L1,andSARS-CoV-2RBD(SC2RBD).Experimentalvalidationbysurfaceplasmonresonance(SPR)followinghigh-throughputscreeningviaphagedisplayshowsthedesignedantibodiesachievenanomolarbindingaffinitiesandpreciseepitopetargeting,demonstratingtheefficiencyoftheintegratedcomputational-experimentalpipeline.OurresultsdemonstratethattFoldSystemovercomeskeylimitationsofexistingmethodsbyenablingrapid,high-throughputantibodydiscoveryagainstuser-definedepitopes.

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Introduction

Antibodies,producedbyclonalBcells,playacrucialroleinthehumanadaptiveimmunesystembyspecificallyrecognizingandrespondingtoforeignmoleculesorantigens1.Asaleadingclassoftherapeuticproteins,theantibodymarketisprojectedtoexceed$445billionby20282.Despitethesignificantinterestofthepharmaceuticalindustryintherapeuticantibodies,theirdevelopmentstilllargelyreliesontraditionalmethodssuchasanimalimmunizationanddisplay-basedselection(includingphageandyeastdisplay)toidentifycandidatemoleculesthatbindtospecifictargets3.Theseapproachesareoftencumbersomeandtimeconsumingandmaynotproduceantibodiesthatinteracteffectivelywithparticularantigenepitopes4.

Recentadvancesinhigh-throughputBcellsequencinghavegeneratedalargeamountofdataessentialtoinvestigatethecomplexmechanismsunderlyingtheadaptiveimmuneresponse,pavingthewayfordata-drivenantibodyresearch5.Inrecentyears,computationalmethodsforantibodydesignhaverapidlyevolved6,7,includingtechniquessuchascomplementarity-determiningregions(CDR)graftingtoenhanceaffinities8-11,energy-basedsequencedesigntooptimizeinteractingregions12-14,antibodyinversefolding15-17,andPLM-basedmutationsforbindingaffinityoptimization18-21.However,progressintherealmofdenovoantibodydesignhasbeenrelativelyslow.WhiletheRFantibody22(RFdiffusionspecializedforantibodydesign),hasdemonstratedpotential,itprimarilyvalidatedthedesigncapabilitiesforsomescFvsandVHHs,whichexhibitedrelativelylowaffinity.

Anidealantibodydenovodesignshouldbecapableoftargetingspecificepitopesofanyantigensequence.Givenaframeworkregion(FR)—mostoftenahumanframework—itshouldenablethedesignofCDRswhilemaintainingtheintegrityoftheFR,therebygeneratinghigh-affinity,functional,developable,andepitope-specificantibodies.Theentireprocesscanbebrokendownintofourcomponents:CDRsrecovery,antibodyprediction,antibody-antigencomplexpredictionandvirtualscreeningofantibodies,eachofwhichpresentssignificantdifficultiesthatcontributetotheoverallchallenge.

Thechallengesassociatedwithdenovoantibodydesignprimarilystemfromtwofactors:datascarcityandthestructureofantibody-antigeninteractions.Thereisarelativelackofexperimentalstructuresforantibodies,withapproximately9,000structuresavailableinSAbDab23comparedto230,000generalstructuresinthePDB.ThisdisparitycomplicatesthetrainingofAImodelsandincreasestheriskofoverfitting.Additionally,theinterfacebetweenantibodiesandantigensispredominantlycomposedofdisorderedloopsandbeta-sheets,ratherthanthemorestraightforwardalpha-helicalstructurescommonlyfoundinbinderdesign.Predictingthestructuresofantibody-antigencomplexespresentsfurtherchallenges.Whilerecentdenovoproteinbinderdesignmethods,suchasRFDiffusion24,BindCraft25,andAlphaProteo26,havedemonstratedhighsuccessrates,theyaresupportedbyrobustproteinstructurepredictiontechniqueslikeRoseTTAFold27,AlphaFold-Multimer28,andAlphaFold-329.However,inthefieldofantibodies,theperformanceofAlphaFold-Multimerhasnotbeensatisfactory30.AlthoughAlphaFold-3hasachievedsignificantimprovements,itrequiresextensivesamplingandreliesonmultiplesequencealignments(MSA)asinput,necessitatinglengthysearchesforhomologoussequences.Thesefactorscollectivelyhindertheadvancementofeffectivedenovoantibodydesign.

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Inthiswork,wepresenttFoldSystem,acomputationalworkflowfortherapidandaccuratemodelinganddesignofantibody-antigencomplexes,capableofdenovodesigninghigh-affinity,functional,developable,andepitope-specificantibodies.Equippedwithapre-trainedlargeproteinsequencemodel(ESM-PPI)andtransformer-basedstructurepredictionmodules,thetFoldSystemenablesefficientend-to-endatomic-resolutionpredictionsofantibodystructures(tFold-Ab)andantibody-antigencomplexes(tFold-Ag)directlyfromtheirsequences.OurinsilicotestsdemonstratethecapabilitiesofthecomponentsofthetFoldSystemforstructure-guidedvirtualscreeningofbindingantibodies,andfortheco-designofstructureandsequenceforepitope-specificantibodies.Utilizingthissystem,wesuccessfullydesignmAbstargetingspecificepitopesforfourdistinctantigens:FluA,PD-1,PD-L1,andtheSC2RBD.SPRexperimentsconfirmthatourdesignedantibodiesexhibitnanomolaraffinities(3.22nMforFluA,81.6nMforPD-1,0.045nMforPD-L1and2.0nMforSC2RBD)andpreciseepitopetargeting-asevidencedbycompetitivebindingassays.Integrationofcomputationaldesignwithhigh-throughputexperimentalscreening,highlightsthepotentialofthetFoldSystemtostreamlinetheearlydevelopmentprocessofantibodytherapeuticsandaddresspreviouslychallengingtargetsinthefield.

Results

Inthissection,wefirstdescribethemethodologicalframeworkofthetFoldSystemandpresentexperimentalvalidationacrossfourdistincttherapeutictargets.Wethendelineatefourkey

challengesaddressedbyourdesignsolutionswithinsilicoevaluation:

1.Antibody/nanobodystructureprediction(tFold-Ab),

2.Antibody-antigencomplexprediction(tFold-Ag),

3.Structure-guidedvirtualscreeningofbindingantibodies,and

4.Jointantibodystructurepredictionandsequencerecovery.

tFoldSystemfordenovoantibodydesign

ThetFoldSystemisacomputationalworkflowdesignedtoaddresskeychallengesindenovoantibodydesign.Bycombiningsequence-basedgenerativemodelingwithstructure-awarefiltering,itenablesend-to-endantibodygeneration,withitsworkflowillustratedinFig.1a.

Fromafunctionalperspective,thetFoldSystemoperatesbyacceptingthreeinputs:antigensequence,specifiedepitopesandknownantibodyFRtemplates.Itdynamically“fillsin”theunknownportionsoftheCDRswhileco-predictingantibody-antigencomplexstructures.Iterativerefinementoccursthroughconfidencescoring(ipLDDT),wherelow-scoringdesignsundergomaskedregionrecyclinguntilviablesequencesemerge.Ultimately,thisprocessoutputsFR-specificantibodieswithpreciseepitopetargeting.

Theimplementationinvolvestwocoremodules:(1)ageneratorthatproducesalargenumberofcandidateantibodiesthroughrandomseedsampling,alongwithcorrespondingstructurepredictionswhicharegeneratedbyourdevelopedtFold-Ag-ppiwhenprovidedwithanantigenepitope;and(2)afilterthatperformsstructure-guidedvirtualscreening.Thegeneratorgeneratesantibodysequencesthatcaneffectivelybindtospecificepitopesoftheantigenandpredictstheircorrespondingantibody-antigencomplexes,primarilydrivenbyourdevelopedtFold-Aband

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tFold-Ag.Thefilterassignsascorethatcorrelateswiththeexperimentalviabilityofthecomplex;candidateswithhigherscoresareconsideredexperimentallyviable,whilethosebelowacertainthresholdarefilteredout,ledbythestructuralpredictionsoftwaretFold-AgandAlphaFold-Multimer.

ToexperimentallyvalidatethetFoldSystemdesignperformance,wedesignedantibodiesagainstfourtargets:FluA,PD-1,PD-L1,andSC2RBD.Selectioncriteriabalancedviralpathogenesisrelevance(FluA,SC2RBD)andtherapeuticimportance(PD-1,PD-L1).AhumanizedFR(humangermline:IGHV3-23/IGKV1-5)waschosenforitsadvantagesinstability,expressionyield,andaggregationresistance31,32.DesignparametersincorporatedV-JpairingfrequenciesandCDR3lengthdistributionsfromtheObservedAntibodySpace(OAS)33,withfixedJgenes(IGHJ4/IGKJ1)and36CDR3lengthcombinations(Table1).Wegeneratedalargepoolofcandidateantibodies,whichweresubsequentlyfilteredtoproduce10,000Fabpairspertargetforsynthesis.Noveltywasenforcedthroughsequencedivergencethresholds.

AfterthecandidateantibodiesweredesignedusingthetFoldSystem,theyunderwentexperimentalvalidation.Giventhelowsuccessrateassociatedwithantibodydesign,weemployedatwo-phaseexperimentalpipeline(Fig.1b).Thebinderidentificationphaseconstructedphagedisplaylibrariesfollowedbybiopanning—usingsequentialsolid-phaseandliquid-phaseselectiontoisolatebindingclones.Successfulcandidatesadvancedtothebindercharacterizationphase,wheremonoclonalantibodiesunderwentproduction,purification,andfunctionalevaluation.SPRquantifiedbindingaffinities,andcompetitionassaysverifiedfunctionalepitopespecificity.

tFoldSystemcandesignhighaffinityantibodies

Bindingaffinity(KDvalues)servesasacriticalmetricforevaluatingantibody-antigeninteractionstrength,reflectingbindingtightness.Antibodiesderivedfromnaturallibrariestypicallyexhibitnanomolaraffinities34whileFDA-approvedtherapeuticantibodiesoftenachievelow-picomolarKDvaluesthroughmultipleroundsofexperimentalaffinitymaturation35,36.

Weproduced10Fc-fusionantibodiesselectedthroughthebinderidentificationpipeline,eachcontainingdistinctCDR-H3.SPRmeasurementsoftheseantibodiesrevealednanomolar-levelaffinitiesformostdesigns,withdistinctaffinitydistributionsacrossantigens:PD-L1antibodiesshowedthestrongestoverallbinding(includingsub-100pMcandidates),whilePD-1antibodiesexhibitedweakeraffinities(Fig.1d,left).Notably,thesubstantialdifferencesinKDvaluesacrosstargets(e.g.,1000-foldbetweenthebestPD-L1andPD-1antibodies)areprimarilylinkedtotheintrinsicpropertiesoftheantigensthemselves.

Toprioritizeleadsfortherapeuticdevelopment,wefocusedonthehighest-affinityantibodyforeachtarget.Notably,thebestPD-L1antibodyachievedapicomolarKDvaluesof45pM.ForFluAandSC2RBD,thetopantibodiesdemonstratedlow-nanomolaraffinitiesof3.22nMand2.0nM,respectively,whilethebestPD-1antibodyexhibitedamoderateaffinityof81.6nM.(Fig.1c).

Fig.1(e-h)presentsthepredictedantibody-antigenstructuresandSPRbindingcurvesforthehighest-affinityantibodiesdesignedagainstfourtargetproteins:FluA(e),PD-1(f),PD-L1(g),andSC2RBD(h).Whilepanels(f-h)meetthecriteriaforthehighestaffinity,panel(e)displaysthesecond-highest-affinityantibodyforFluA.Thisexceptionoccursbecausethehighest-affinityFluAcandidatebindsoutsidetheintendedepitope.Thedesignatedepitopesarehighlightedin

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yellow,andthehigh-confidencestructurespredictedbytFold-AgandAlphaFold-Multimerconfirmthatourdesignedantibodiescaneffectivelybindtotheircorrespondingepitopes.TheSPRcurvesindicaterapidassociationandslowdissociationkinetics,whicharecharacteristicofhigh-affinitybinding.

TovalidatethedenovonatureofourdesignsratherthanmodificationsofexistingantibodiesorCDRgrafting,werigorouslyassessedsequencenoveltymetricsagainstknownantibodiesinpublicdatabases(SAbDab23,TheraSAbDab37,PLAbDab38,andCoV-AbDab39.Editdistancestotheclosestantibodywerecalculatedseparatelyforeachknownantigen-specificantibodysubset,ensuringunbiasedassessmentofnoveltyacrossallregions,includingFR,CDRs,variabledomains(VH/VL)andCDR-H3.ThedistributionofaffinityandnoveltymetricsforallbindingantibodiessynthesizedbythetFoldSystemacrossthefourtargetsisshowninFig.1d.AnalysisofCDR3andoverallCDReditdistancesrelativetotheclosestknownantibodiesrevealsthat,withtheexceptionofthePD-1-targetedantibody,alldesignsexhibitedclearnoveltyintheirCDR-H3regions(editdistance≥3).AlthoughthePD-1antibodyshowedcomparativelylowernoveltyscores,itsCDReditdistancetotheclosestdatabaseentrystillreached≥5.Havingestablishedthenoveltyofgeneratedantibodies,wefurtherinvestigatedwhetherthisoriginalitypersistsinthehighest-affinitycandidates—acriticalconsiderationfortherapeuticdevelopment.

ForPD-1andPD-L1,whereestablishedtherapeuticantibodiesshareidenticalVgenes(e.g.,D1240forPD-1andGNC-03841forPD-L1),ourdesignsexhibitedsubstantialdivergenceinCDR-H3.ThePD-1antibodyexhibitedaCDR-H3editdistanceof5(withD12’sCDR-H3lengthof9comparedtoourdesign’s8residues),whilethePD-L1antibodydisplayedaCDR-H3editdistanceof6(withGNC-038’sCDR-H3lengthof11comparedtoourdesign’s12residues).ForFluAandSC2RBD,noantibodieswithidenticalVgenesexistinpublicdatabases.Notably,theFluAdesigndemonstratedanFR-Heditdistanceof26relativetotheclosestknownantibody,indicatingcompleteframeworknovelty.DespiteCoV-AbDabcontainingover4,000SC2RBD-targetingantibodies,ourdesignshowednohomologyinCDR-H(editdistance=10)andCDR-H3(editdistance=5).TheseresultsconfirmthatthetFoldSystem’sabilitytogenerateantibodiesevenagainstwell-characterizedtargets.

tFoldSystemcandesigndiverseantibodiesfortargetingspecificepitopes

Unlikeantibodyengineering,denovoantibodydesigngeneratesdiversecandidateantibodieswithsignificantvariability,particularlyintheCDR-H3.Fig.2aillustratestheCDRsofourexpressedPD-L1antibodies.WeobservedthatwhileCDR-H1andCDR-H2displayedmoderatesimilarity(dictatedbytheVgene),CDR-H3andCDR-L3demonstratedremarkablediversity.ThiscombinatorialdiversityinCDRregionsdramaticallyexpandsthedruggablesequencespace42,enablingsystematicexplorationofnon-immunogenicparatopesthatwouldotherwiseremaininaccessiblethroughconventionalimmunizationorlibraryscreeningmethods.CDR-3variationsgenerateddistinctantigen-antibodyinteractioninterfaces.Structuralpredictionmodels(tFold-Ag/AlphaFold-Multimer)furthervalidatedthatthesecomputationallydesignedantibodiesbindspecificallyatthePD-1/PD-L1interactioninterface,highlightingtheefficacyofourstructure-drivencomputationalworkflow.

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Antibodiescanexertdifferentfunctionsbybindingtovariousepitopesonanantigen43.Therefore,weselectedtargetsthatpossessmultipledistinctepitopes44-47,withtheaimofdesigningantibodiesthatspecificallybindtocertainepitopestoelicitthedesiredfunctions.Tovalidatetheepitopespecificityofourantibodies,weconductedcompetitionassaysusingSPRtoevaluatetheirabilitytoblockthebindingoftheantigentoitsreceptororligand.

TheinteractionbetweenPD-1andPD-L1elicitsinhibitorysignalingthatsuppressesT-cellactivationthroughnegativeimmunoregulatorymechanisms48.Tocounteractthisinteraction,weengineeredantibodiestargetingspecificepitopeswithinthePD-1/PD-L1interactioninterface,leveragingtheirsuperiorbindingaffinitytostericallydisruptthenativePD-1/PD-L1complex.

AmongthecandidatesgeneratedbythetFoldSystem,3top-affinityantibodiesidentifiedthroughquantitativebindingassessmentsdemonstratedcompleteblockadeefficacyincompetitionassaysusingSPR.BothPD-1-directedandPD-L1-targetedantibodyformatseffectivelypreventedPD-1/PD-L1ligation,anoutcomeconsistentwiththepotentialtorestoreT-celleffectorfunctionthroughpreciseinterferencewiththisimmunecheckpointaxis49.Wefocusedonthehighest-affinityanti-PD-1antibody(Anti-PD-1-H965)andanti-PD-L1antibody(Anti-PD-L1-H635).Specifically,theSPRmeasurementsforAnti-PD-1-H965andAnti-PD-L1-H635highlightedtheircompetitivebindingcapabilities:Anti-PD-1-H965exhibitedstrongbindingtoPD-1alone,butitsresponseunits(RU)significantlydecreasedinthepresenceofPD-L1,confirmingitscompetitionwithPD-L1forPD-1binding.Similarly,Anti-PD-L1-H635showedsubstantialbindingtoPD-L1alone,yetitsRUvaluesmarkedlydeclinedwhencombinedwithPD-1,demonstratingitscompetitionwithPD-1forPD-L1binding(Fig.2b).

Structuralalignmentbetweenthepredictedantibody-antigencomplexesandthePD-1/PD-L1complex(PDBID:4ZQK50)revealedthattheepitopestargetedbyourdesignedantibodiescorrespondtoaportionofthePD-1/PD-L1bindingsite.Additionally,thespatialarrangementoftheantibodiesindicateditsclashwiththereceptor,whichalignswithourexpectations.(Fig.2c)

Thehigh-affinitybindingofSC2RBDtotheACE2receptoronhostcellsinitiatesviralmembranefusion,enablingendocytosisandthereleaseoftheviralgenome,whichestablishesinfectionandfacilitatessystemicdissemination51.IncomparisontoPD-1andPD-L1,designinganACE2blockerismorechallengingforseveralreasons:1)SC2RBDhasalongerlengthandastructurallymorecomplexepitope;2)theaffinitybetweenSC2RBDandACE2isinthenanomolarrange52,whichnecessitatesthatantibodiesexhibitsufficientlyhighaffinitytoeffectivelyinhibitthebindingofSC2RBDtoACE2,therebyneutralizingthevirus.

Weselected5top-affinityantibodiesforcompetitionassaysusingSPR.Notably,2outofthe5antibodieswererecognizedaseffectiveblockers.Amongthese,wefocusedontheSC2RBDantibodywithhighest-affinity(Anti-SC2RBD-H330).TheSPRmeasurementsforAnti-SC2RBD-H330showeditscompetitivebindingcapabilitieswithACE2.Furthermore,wefoundthatitsneutralizationcapacityyieldedanIC50of0.42nanomolar(Fig.2d).ByaligningthepredictedstructureofthetFoldSystem-generatedantibody-antigencomplexwiththeSC2RBD/ACE2complexstructure(PDBID:6M0J52),wefoundthattheepitopetargetedbyourdesignedantibodycorrespondstoaportionofthebindingsitebetweenSC2RBDandACE2.Additionally,thespatialarrangementindicatedaclashbetweentheantibodyandthereceptor,whichalignswithexperiments.(Fig.2e)Althoughthestructuresoftheotherthreeantibodiespredictedbyourmodelalsoboundtothecorrectepitopeandexhibitedhighconfidencescores(>0.7),thecompetition

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experimentsrevealedthattheywereunabletocompetewithACE2.ThisobservationhighlightsthelimitationsofthetFoldSystem’sstructuralpredictions,whichstillexhibitinstancesoffalsepositives.

FluApossessesmultipleepitopes44,andFluA-2053isabroad-spectrumantibodythatbindswithhighaffinitytoconservedresiduesintheheadregionoftheFluAHA1.OurobjectivewastodesignantibodieswithepitopessimilartothoseofFluA-20toconferbroad-spectrumproperties.

Weselectedthetop-4FluAantibodieswiththehighestaffinityandconductedcompetitionassayswithFluA-20usingSPR.Ourresultsindicatedthatthebindingsignalsof3outofthe4designedFluAantibodies(Anti-FluA-H141,Anti-FluA-H169,andAnti-FluA-H551)weresignificantlyaffectedinthepresenceofFluA-20.ThisfindingsuggeststhattheseantibodiessharesimilarepitopeswithFluA-20,whichalignswithourstructuralpredictions(Fig.2fandg).Incontrast,thebindingsignaloftheantibodywiththehighestaffinity,Anti-FluA-H093,remainedunaffected.Despitehigh-confidencepredictionsfromthestructuralmodel,thisantibodyappearstobindtoanincorrectepitope.Notably,theaffinityofFluA-20forFluA(<1nM)issubstantiallystrongerthanthatofourdesignedantibodies(from10to30nM),placingourantibodiesatacompetitivedisadvantage.

Tofurthervalidatetheepitope-specificofourdesignedantibodies,weintroducedantibodyC0554,whichtargetsdistinctregionsofFluAcomparedtoFluA-20.UsingELISA,weconfirmedthattherewasnocompetitionbetweenC05andourdesignedantibodies(Fig.2h).ThisresultisconsistentwiththeirintendedtargetingoftheFluA-20epitope,effectivelyrulingoutinterferencefromneighboringsitesandvalidatingthespecificityofourdesignstrategy.

WehavedemonstratedtheexperimentalvalidationofthetFoldSystemacrossfourdistincttherapeutictargets.Next,wewillreturntothetFoldSystemtoelucidatehowitaddressesvariouschallengesassociatedwiththedenovodesignofepitope-specificantibodies.

tFold-Ab:fastandaccurateantibodystructuresprediction

TheaccuratepredictionofantibodystructuresiscrucialforunderstandingtheirfunctionandisaprerequisiteforthetFoldSysteminantibodydesign.ThecomponentofthetFoldSystemresponsibleforpredictingantibodystructuresisreferredtoastFold-Ab,whichcanindependentlygeneratehigh-resolution,atomic-level3Dstructuresofantibodiesfromtheiraminoacidsequencesinseconds.

ThetFold-Abconsistsoffourmainmodulesthatincludesapre-trainedproteinlanguagemodel(PLM):ESM-PPI,afeatureupdatingmodule:Evoformer-Singlestack,astructuremodule,andarecyclingmodule(Fig.3a).TheESM-PPImoduleextractsbothintra-chainandinter-chaininformationoftheproteincomplextogeneratefeaturesforthedownstreamstructurepredictiontask.WedevelopESM-PPIbyextendingthecurrentESM-2model55throughfurtherpre-trainingusingbothmonomersandmultimers.Thisenhancementenablesthemodeltoeffectivelyextractinter-chaininformation.TheEvoformer-SinglestackupdatesandrefinestheinputfeaturesfromtheESM-PPI,iterativelyupdatethesequenceandpairwiserepresentations.Thestructuremodule,whichperformsSE(3)-equivariantupdatesusinginvariantpointattention56,thenmapstheobtainedrepresentationtopredictedatomic-level3Dstructures.Finally,therecyclingmodule

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allowstFold-Abtoreusethefeaturesandstructurepredictionsofthepreviousiteration,enhancingthefinalstructurepredictionquality.

tFold-Abachievesstate-of-the-artcomputationalefficiencyinantibodystructurepredictionbyutilizingESM-PPIinsteadoftraditionalMSA-basedmethodsforco-evolutionaryinformationextraction.ItfeaturesanoptimizedEvoformer-Singlestackandastructuremoduleforend-to-endpredictionofbackboneandside-chainconformations.Incontrast,existingmethodslikeDeepAb57andIgFold58relyontime-consumingRosetta-basedenergyminimizationforside-chainprediction,whileChai-159requiresextensivesamplingviadiffusion.ThisMSA-freedesignyieldssubstantialspeedgains:comparedtotheheavilyengineeredAlphaFold-3Server,tFold-Aboffersa50-foldadvantage,andasshowninFig.3bandSupplementaryFig.2itis5,000timesfasterthanAlphaFold-Multimer28.EvenwhencomparedtoanAlphaFold-MultimervariantthatomitsMSAandtemplatesearch,tFold-Abdemonstrates