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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,Peish
2、engCong1*,TonghuaLi1*1SchoolofChemicalScienceandEngineering,TongjiUniversity,Shanghai2CollegeofChemistryandChemicalEngineering,CentralSouthUniversity,ChangshaHunanththndReceived:May30,2017;accepted:Jun.19,2017;published:Jun.22,2017AbstractProteinsecondarystructure
3、predictionisanimportantfieldofcomputationalbiology.Althoughtheaccuraciesoftheexistedstate-of-the-artapproachesaremorethan80%butthesemethodshaveacommonlimitation.Theycouldn’tlearnnewstructureknowledgeofcurrentlymeasuredproteins,andcouldn’tchangetheusedmodelandtheir
4、parameters.Thus,theycouldn’tsatisfyourexpectinginthechangingworld.Here,wepresentapredictorofproteinsecondarystructurebasedonacontinuouslyupdatedtempletlibrary:SIPSS.Thebasicstoneofourapproachisstruc-turalsimilaritybasedonsequencehomology.First,acontinuouslyupdated
5、templetlibraryiscon-structed,whichcanautomaticallydownloadthemeasuredproteinstructuredatafromPDBper-month.Afterscreening,thenewinformationofproteinsequencesandstructuresaresuppliedintothetemplatelibrary.ThenaquerysequenceisalignedagainstthetemplatelibrarybyusingPS
6、IBLAST,andanewvariable-SPSSMvariableisobtained.Last,theSPSSMvariableisusedinaconditionalrandomfieldalgorithmformodellingandprediction.OurexperimentsshowedthatSIPSScanonlinelearnnewproteinstructureinformationanditspredictionaccuracy(80.6%)ofproteinsecondarystructur
7、emeasuredinrecenttimesissignificantlybetterthanthestate-of-theartapproaches.SIPSSisavailablefreeofchargeathttp://cheminfo.tongji.edu.cn/SIPSS/.KeywordsProteinSecondaryStructure,Prediction,Continuouslyupdated,SPSSMVariable,ConditionalRandomField*通讯作者。文章引用:周鹏杰,文明,丛培
8、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，丛培盛，李通化1同济大学化学科学与工程学院
