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1、HansJournalofComputationalBiology计算生物学,2017,7(2),13-22PublishedOnlineJune2017inHans.http://www.hanspub.org/journal/hjcbhttps://doi.org/10.12677/hjcb.2017.72002APredictorofProteinSecondaryStructureBasedonaContinuouslyUpdatedTempletLibraryPengjieZhou1,MingWen2,PeishengCong1*,TonghuaLi
2、1*1SchoolofChemicalScienceandEngineering,TongjiUniversity,Shanghai2CollegeofChemistryandChemicalEngineering,CentralSouthUniversity,ChangshaHunanththndReceived:May30,2017;accepted:Jun.19,2017;published:Jun.22,2017AbstractProteinsecondarystructurepredictionisanimportantfieldofcomputat
3、ionalbiology.Althoughtheaccuraciesoftheexistedstate-of-the-artapproachesaremorethan80%butthesemethodshaveacommonlimitation.Theycouldn’tlearnnewstructureknowledgeofcurrentlymeasuredproteins,andcouldn’tchangetheusedmodelandtheirparameters.Thus,theycouldn’tsatisfyourexpectinginthechang
4、ingworld.Here,wepresentapredictorofproteinsecondarystructurebasedonacontinuouslyupdatedtempletlibrary:SIPSS.Thebasicstoneofourapproachisstruc-turalsimilaritybasedonsequencehomology.First,acontinuouslyupdatedtempletlibraryiscon-structed,whichcanautomaticallydownloadthemeasuredprotein
5、structuredatafromPDBper-month.Afterscreening,thenewinformationofproteinsequencesandstructuresaresuppliedintothetemplatelibrary.ThenaquerysequenceisalignedagainstthetemplatelibrarybyusingPSIBLAST,andanewvariable-SPSSMvariableisobtained.Last,theSPSSMvariableisusedinaconditionalrandomf
6、ieldalgorithmformodellingandprediction.OurexperimentsshowedthatSIPSScanonlinelearnnewproteinstructureinformationanditspredictionaccuracy(80.6%)ofproteinsecondarystructuremeasuredinrecenttimesissignificantlybetterthanthestate-of-theartapproaches.SIPSSisavailablefreeofchargeathttp://c
7、heminfo.tongji.edu.cn/SIPSS/.KeywordsProteinSecondaryStructure,Prediction,Continuouslyupdated,SPSSMVariable,ConditionalRandomField*通讯作者。文章引用:周鹏杰,文明,丛培盛,李通化.基于可持续更新模板库的蛋白质二级结构预测器[J].计算生物学,2017,7(2):13-22.https://doi.org/10.12677/hjcb.2017.72002周鹏杰等基于可持续更新模板库的蛋白质二级结构预测器121*1*周鹏杰，文明，丛培
8、盛，李通化1同济大学化学科学与工程学院
