TheminorcapsidproteinL2isapromisingcandidateforthe

TheminorcapsidproteinL2isapromisingcandidatefortheconstructionofananti-humanpapillomavirus(HPV)broadlyprotectivevaccinefortheprophylaxisofcervicalcancer.However,L2-derivedpeptidesareusuallypoorlyimmunogenicandextensiveknowledgeonthemostrelevant(cross)neutralizingepitope(s)isstillneeded.WesystematicallyexaminedtheimmunogenicityandVirusneutralizationpotentialofsixpeptidesencompassingtheN-terminal(aminoacids1-120)regionofHPV16L2(20-38;28-42;56-75:64-81;96-115;108-120)usingbacterialthioredoxin(Trx)asanovelpeptidescaffold.Miceantiserageneratedby19differentTrx-L2peptidefusionshearingoneormultiplecopiesofeachpeptidewereanalyzed.Internalfusiontothioredoxinconferredstrongimmunogenicitytoallthetestedpeptides,withatrendtowardartincreasedimmunogenicityforthemultipeptidevs.themonopeptideformsofthevariousantigens.AllTrx-L2peptidesinducedHPV16neutralizingantibodiesinsomeoftheimmunizedmice,butneutralizationtitersdifferedbymorethantwoordersofmagnitude.Trx-L2(20-38)antiserawerebyfarthemosteffectiveinHPV16neutralizationanddidnotdiffersignificantlyfromthoseinducedbyareferencepolypeptidecoveringtheentireL2(1-120)region.Thesameantiserawerealsothemosteffectivewhenchallengedagainstthenon-cognateHPV18,58,45and31pseudovirions.ThedataidentifyL2(20-38)asthebest(cross)neutralizingepitopeamongthesixthatwereexamined,andpointtothioredoxinfusionderivativesofthispeptideasexcellentcandidatesfortheformulationofalow-cost,broadlyprotectiveHPVvaccine.(C)2009ElsevierLtd.Allrightsreserved.人乳头瘤病毒L2蛋白N末端肽片段与硫氧还原蛋白偶联在体内引起抗多肽片段的抗体，其中L2（20-38）肽段引起抗体作用最强，可能用于制造低价的抗人乳头瘤病毒疫苗。Potentanti-HPVimmuneresponsesinducedbytandemrepeatsoftheHPV16L2(20-38)peptidedisplayedonbacterialthioredoxinVaccinesciencehitsextendedbeyondgenomicstoproteomicsandhascometoalsoencompass'immunomics,'thestudyoftheuniverseofpathogen-derivedorneoplasm-derivedpeptidesthatinterfacewithBandTcellsofthehostimmunesystem.IthasbeentheorizedthateffectivevaccinescanbedevelopedusingtheminimumessentialsubsetofTcellandBcellepitopesthatcomprisethe'immunome.'Researchersarethereforeusingbioinformaticssequenceanalysistools,epitope-mappingtools,microarrays,andhigh-throughputimmunologyassaystodiscovertheminimalessentialcomponentsoftheimmunome.Whentheseminimalcomponents,orepitopes,arepackagedwithadjuvantsinanappropriatedeliveryvehicle,thecompletepackagecomprisesanepitope-basedimmunome-derivedvaccine.Suchvaccinesmayhaveasignificantadvantageoverconventionalvaccines,asthecarefulselectionofthecomponentsmaydiminishundesiredsideeffectsSuchashavebeenobservedwithwholepathogenandproteinsubunitvaccines.Thischapterwillreviewthepre-clinicalandanticipatedclinicaldevelopmentorcomputer-drivenvaccinedesignandthevalidationofepitope-basedimmunome-derivedvaccinesinanimalmodels;itwillalsoincludeanoverviewofheterologousimmunityandotheremergingissuesthatwillneedtobeaddressedbyvaccinesofalltypesinthefuture.AccuratepredictionofB-cellepitopeshasremainedachallengingtaskincomputationalimmunologydespiteseveraldecadesofresearch.Only10%oftheknownB-cellepitopesareestimatedtobecontinuous,尽管讲过十多年的研究，计算机预测B细胞表位的方法仍然不准确。目前只有10%的表位得到预测。yettheyareoftenthetargetsofpredictorsbecauseasolvedtertiarystructureisnotrequiredandtheyareintegraltothedevelopmentofpeptidevaccinesandengineeringtherapeuticproteins.Inthisarticle,wepresentCOBEpro,anoveltwo-stepsystemforpredictingcontinuousB-cellepitopes.COBEproiscapableofassigningepitopicpropensityscorestobothstandalonepeptidefragmentsandresidueswithinanantigensequence.COBEprofirstusesasupportvectormachinetomakepredictionsonshortpeptidefragmentswithinthequeryantigensequenceandthencalculatesanepitopicpropensityscoreforeachresiduebasedonthefragmentpredictions.Secondarystructureandsolventaccessibilityinformation(eitherpredictedorexact)canbeincorporatedtoimproveperformance.COBEproachievedacross-validatedareaunderthecurve(AUC)ofthereceiveroperatingcharacteristicupto0.829onthefragmentepitopicpropensityscoringtaskandanAUCupto0.628ontheresidueepitopicpropensityscoringtask.COBEproisincorporatedintotheSCRATCHpredictionsuiteat细胞表位预测方法分线性表位预测和构象性表位预测两类。目前的绝大部分B细胞表位预测方法都是从抗原蛋白的氨基酸一级结构出发,以线性表位预测为主。近几年来,构象性表位预测也取得了可喜的进展。1.1抗原-抗体相互作用的方式抗原和抗体的相互作用方式包括结构上互补契合、大量的分子间低能量连接(如氢键、疏水作用和范德华力等)、少量的分子间高能量连接(如盐桥)等。参与高能量连接的氨基酸残基构成抗原-抗体的关键性结合残基(Critical-bindingResidues,CBR),CBR上氨基酸的替换将极大地影响其亲和性。若CBR位于较短的连续性多肽序列内,则其形成线性表位;若CBR位于多肽序列间隔较远的位置,通过蛋白折叠聚集在抗原表面,则其形成构象性表位[2]。抗体一般通过深而狭窄的袋状抗原结合槽与多糖、核酸、多肽、半抗原等形成的B细胞表位相结合,在结合槽中的氨基端和羧基端通过范德华力聚合在一起。通过对抗原-抗体复合物晶体的研究发现,抗原与抗体结合表面较为平坦,呈起伏状相互嵌合[3]。目前的绝大部分B细胞表位预测方法都是从抗原蛋白的氨基酸一级结构出发,以线性表位预测为主,其基本思想是综合抗原蛋白理化性质、结构特点、统计显著性度量等指标进行表位预测,如一级序列中的氨基酸柔性(Flexibility)、表面易近(Surfaceaccessibility)、局部亲水性(Localhydrophilicity)、抗原性、突出指数(Protrusionindex)以及二级结构的转角(Turn)与环(Loop)结构等,代表软件有PEOPLE[4]、BEPITOPE[5]、Bcepred等。但是基于上述度量性质的表位预测,预测准确度一般低于60%,难以令人满意。Blythe[6]等的研究指出,除抗原蛋白二级结构的转角、环与其B细胞表位具有较强的关联外,其它属性对B细胞表位预测的贡献仅略强于随机预测。近年来的研究组合了更多的抗原蛋白一级结构属性,采用机器学习方法进行预测,取得了较好的效果。Saha[7]等开发了基于递归神经网络方法的ABCpred软件,从Bcipep[8]和Swiss2Prot[9]数据库中提取非冗余的表位肽和非表位肽作为训练集,采用52折交叉验证,预测灵敏度约为67%,特异度约为64%。Johannes[10]等提取了抗原一级序列近