蛋白質(zhì)結(jié)構(gòu)分析原理及工具-文獻綜述

蛋白質(zhì)結(jié)構(gòu)分析原理及工具（南京農(nóng)業(yè)大學(xué)生命科學(xué)學(xué)院生命基地111班）摘要：本文主要從相似性檢測、一級結(jié)構(gòu)、二級結(jié)構(gòu)、三維結(jié)構(gòu)、跨膜域等方面從原理到方法再到工具，系統(tǒng)地介紹了蛋白質(zhì)結(jié)構(gòu)分析的常用方法。文章側(cè)重于工具的列舉，并沒有對原理和方法做詳細的介紹。文章還列舉了蛋白質(zhì)分析中常用的數(shù)據(jù)庫。關(guān)鍵詞：蛋白質(zhì)；結(jié)構(gòu)預(yù)測；跨膜域；保守結(jié)構(gòu)域1蛋白質(zhì)相似性檢測蛋白質(zhì)數(shù)據(jù)庫。由一個物種分化而來的不同序列傾向于有相似的結(jié)構(gòu)和功能。物種分化后形成的同源序列稱直系同源，它們通常具有相似的功能；由基因復(fù)制而來的序列稱為旁系同源，它們通常有不同的功能ADDINEN.CITE<EndNote><Cite><Author>Fitch</Author><Year>2000</Year><RecNum>245</RecNum><DisplayText>[1]</DisplayText><record><rec-number>245</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">245</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Fitch,W.M.</author></authors></contributors><auth-address>Fitch,WM UnivCalifIrvine,DeptEcol&EvolutionaryBiol,321SteinhausHall,Irvine,CA92697USA UnivCalifIrvine,DeptEcol&EvolutionaryBiol,Irvine,CA92697USA</auth-address><titles><title>Homology-apersonalviewonsomeoftheproblems</title><secondary-title>TrendsinGenetics</secondary-title><alt-title>TrendsGenet TrendsGenet</alt-title></titles><periodical><full-title>TrendsinGenetics</full-title><abbr-1>TrendsGenet</abbr-1></periodical><pages>227-231</pages><volume>16</volume><number>5</number><keywords><keyword>proteins</keyword><keyword>sequence</keyword><keyword>genes</keyword><keyword>convergence</keyword></keywords><dates><year>2000</year><pub-dates><date>May</date></pub-dates></dates><isbn>0168-9525</isbn><accession-num>WOS:0010</accession-num><urls><related-urls><url><GotoISI>://WOS:0010</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Fitch,2000#245"1]。因此，推測全新蛋白質(zhì)功能的第一步是將它的序列與進化上相關(guān)的已知結(jié)構(gòu)和功能的蛋白質(zhì)序列比較。表一列出了常用的蛋白質(zhì)序列數(shù)據(jù)庫和它們的特點。表一常用蛋白質(zhì)數(shù)據(jù)庫數(shù)據(jù)庫說明鏈接蛋白序列數(shù)據(jù)庫GenPeptTranslationsofGenBankcodingnucleotideentriesPIRInternationalproteindatabaseRefSeqCurated,non-redundantwithexpertannotationUniProt/SwissProtReviewed,manuallyannotatedentriesUniProt/TrEMBLAutomaticallyclassifiedandannotatedentries蛋白質(zhì)分類數(shù)據(jù)庫CATHProteinsclassifiedbasedonclass,architecture,topologyandhomologySCOPStructuralclassificationofproteinsProtClustDBProteinsclassifiedbasedonsequencesimilarity蛋白質(zhì)結(jié)構(gòu)數(shù)據(jù)庫PDBResolved3Dbiomolecularstructures網(wǎng)址可能有更新氨基酸替代模型。進化過程中，一種氨基酸殘基會有向另一種氨基酸殘基變化的傾向。氨基酸替代模型可用來估計氨基酸替換的速率。目前常用的替代模型有PointAcceptedMutation(PAM)矩陣、BLOckSUbstitutionMatrix(BLOSUM)矩陣ADDINEN.CITE<EndNote><Cite><Author>Henikoff</Author><Year>1992</Year><RecNum>246</RecNum><DisplayText>[2]</DisplayText><record><rec-number>246</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">246</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Henikoff,S.</author><author>Henikoff,J.G.</author></authors></contributors><auth-address>Henikoff,S FredHutchinsonCancResCtr,HowardHughesMedInst,DivBasicSci,Seattle,Wa98104,USA</auth-address><titles><title>Amino-AcidSubstitutionMatricesfromProteinBlocks</title><secondary-title>ProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica</secondary-title><alt-title>PNatlAcadSciUSA PNatlAcadSciUSA</alt-title></titles><periodical><full-title>ProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica</full-title><abbr-1>PNatlAcadSciUSA</abbr-1></periodical><pages>10915-10919</pages><volume>89</volume><number>22</number><keywords><keyword>aminoacidsequence</keyword><keyword>alignmentalgorithms</keyword><keyword>databasesearching</keyword><keyword>multiplesequencealignment</keyword><keyword>scoringmatrix</keyword><keyword>databases</keyword><keyword>patterns</keyword><keyword>fasta</keyword><keyword>tool</keyword></keywords><dates><year>1992</year><pub-dates><date>Nov15</date></pub-dates></dates><isbn>0027-8424</isbn><accession-num>WOS:A1992JY87400073</accession-num><urls><related-urls><url><GotoISI>://WOS:A1992JY87400073</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Henikoff,1992#246"2]、JTT模型ADDINEN.CITEADDINEN.CITE.DATA[\o"Jones,1992#247"3]。序列相似性搜索工具。序列相似性搜索又分為成對序列相似性搜索和多序列相似性搜索。成對序列相似性搜索通過搜索序列數(shù)據(jù)庫從而找到與查詢序列相似的序列。分為局部聯(lián)配和全局聯(lián)配。常用的局部聯(lián)配工具有BLAST和SSEARCH，它們使用了Smith-Waterman算法。全局聯(lián)配工具有FASTA和GGSEARCH，基于Needleman-Wunsch算法。多序列相似性搜索常用于構(gòu)建系統(tǒng)發(fā)育樹，這里不闡述。表二列舉了常用的成對序列相似性比對搜索工具表二成對序列相似性比對搜索工具工具說明鏈接BLASTBasiclocalalignmentsearchtoolFASTAGlobalalignmentsearchtool;GGSEARCHGlobalalignmentsearchtoolindex.html?program=GGSEARCHSSEARCH-ProteinLocalalignmentsearchtoolagainstproteinsindex.html?program=SSEARCH網(wǎng)址可能有更新2蛋白質(zhì)一級結(jié)構(gòu)分析（含保守結(jié)構(gòu)域）蛋白質(zhì)結(jié)構(gòu)的基本信息來源于它的一級結(jié)構(gòu)，分析蛋白質(zhì)一級結(jié)構(gòu)的第一步是將它們分成其組成部分，然后處理每個部分的結(jié)構(gòu)ADDINEN.CITE<EndNote><Cite><Author>Paliakasis</Author><Year>2008</Year><RecNum>248</RecNum><DisplayText>[4]</DisplayText><record><rec-number>248</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">248</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Paliakasis,C.D.</author><author>Michalopoulos,I.</author><author>Kossida,S.</author></authors></contributors><auth-address>BiomedicalResearchFoundation,AcademyofAthens,Athens,Greece.</auth-address><titles><title>Web-basedtoolsforproteinclassification</title><secondary-title>MethodsMolBiol</secondary-title><alt-title>Methodsinmolecularbiology</alt-title></titles><periodical><full-title>MethodsMolBiol</full-title><abbr-1>Methodsinmolecularbiology</abbr-1></periodical><alt-periodical><full-title>MethodsMolBiol</full-title><abbr-1>Methodsinmolecularbiology</abbr-1></alt-periodical><pages>349-67</pages><volume>428</volume><keywords><keyword>AminoAcidSequence</keyword><keyword>Databases,Protein</keyword><keyword>Humans</keyword><keyword>*Internet</keyword><keyword>MolecularSequenceData</keyword><keyword>ProteinStructure,Tertiary</keyword><keyword>Proteins/chemistry/*classification/genetics</keyword><keyword>Proteome/chemistry/*classification/genetics</keyword><keyword>Proteomics/*statistics&numericaldata</keyword><keyword>Software</keyword></keywords><dates><year>2008</year></dates><isbn>1064-3745(Print) 1064-3745(Linking)</isbn><accession-num>18287782</accession-num><urls><related-urls><url></url></related-urls></urls></record></Cite></EndNote>[\o"Paliakasis,2008#248"4]。這種拆分常常是根據(jù)蛋白質(zhì)具有的相互作用的結(jié)構(gòu)域進行的ADDINEN.CITEADDINEN.CITE.DATA[\o"Ponting,2002#249"5,\o"Holland,2006#250"6]。蛋白質(zhì)結(jié)構(gòu)域或蛋白質(zhì)家族數(shù)據(jù)庫對分析未知蛋白質(zhì)的功能是很有用的，這些數(shù)據(jù)庫通常被稱為“特征數(shù)據(jù)庫（signaturedatabases）”。“基序（Motifs）”通常指沒有間隔的多序列隊列，通常由10-20個氨基酸構(gòu)成。一系列基序構(gòu)成的蛋白質(zhì)域家族叫做“指紋（fingerprint）”。使用它們的優(yōu)勢是可以檢測遠距離的序列關(guān)系A(chǔ)DDINEN.CITE<EndNote><Cite><Author>Attwood</Author><Year>2000</Year><RecNum>251</RecNum><DisplayText>[7]</DisplayText><record><rec-number>251</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">251</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Attwood,T.K.</author></authors></contributors><auth-address>Attwood,TK UnivManchester,SchBiolSci,OxfordRd,ManchesterM139PT,Lancs,England UnivManchester,SchBiolSci,ManchesterM139PT,Lancs,England</auth-address><titles><title>Thequesttodeduceproteinfunctionfromsequence:theroleofpatterndatabases</title><secondary-title>InternationalJournalofBiochemistry&CellBiology</secondary-title><alt-title>IntJBiochemCellB IntJBiochemCellB</alt-title></titles><periodical><full-title>InternationalJournalofBiochemistry&CellBiology</full-title><abbr-1>IntJBiochemCellB</abbr-1></periodical><pages>139-155</pages><volume>32</volume><number>2</number><keywords><keyword>bioinformatics</keyword><keyword>similaritysearch</keyword><keyword>sequencealignment</keyword><keyword>patternrecognition</keyword><keyword>functionannotation</keyword><keyword>retrieval-system</keyword><keyword>gappenalties</keyword><keyword>sensitivity</keyword><keyword>resources</keyword><keyword>alignment</keyword><keyword>programs</keyword><keyword>motifs</keyword><keyword>genome</keyword><keyword>blast</keyword></keywords><dates><year>2000</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1357-2725</isbn><accession-num>WOS:0004</accession-num><urls><related-urls><url><GotoISI>://WOS:0004</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Attwood,2000#251"7]?；虻牡湫屠邮俏恢眉訖?quán)矩陣（position-specificscorematrix，PSSM）。PSSM計算基序中每一位置的分數(shù)。任何一個保守位置的信息被縮小到一個叫“序列模式（sequencepatterns）”的共同序列結(jié)果。“序列譜（sequenceprofiles）”用來描述一個較長的可能含有有用信息的保守序列片段。它們被用來較大結(jié)構(gòu)域的檢測。隱馬爾可夫模型（HiddenMarkovModels，HMMs）即是一種和序列譜有關(guān)的模型。表三列舉了主要的蛋白質(zhì)特征數(shù)據(jù)庫。表三常用蛋白質(zhì)特征數(shù)據(jù)庫數(shù)據(jù)庫特征類型外部來源網(wǎng)絡(luò)鏈接BLOCKSBlocksCDDHMM,MSAPfam,SMART,COGs,ProtClustDBGene3DHMMCATHInterProIntegratedsignaturetypesofitsmemberdatabasesGene3D,PANTHER,Pfam,PIRSF,PRINTS,ProDom,PROSITE,SMART,SUPERFAMLY,TIGRFAMsPfamHMM,MSAUniProtKB,GenPept,metagenomicsdatasetsPRINTSFringerprintsProDomUniProtKB,SCOPPROSITEPatterns,profilesUniProtKB/SWISS-PROTSBASEBLOCKS,Pfam,PRINTS,ProDom,PROSITESMARTHMMSUPERFAMILYHMMSCOPMSA：多序列比對；CDD：保守結(jié)構(gòu)域數(shù)據(jù)庫值得一提的是，CDD數(shù)據(jù)庫包含了蛋白質(zhì)保守結(jié)構(gòu)域分析。上述數(shù)據(jù)庫都有自帶的搜索引擎供搜索，它們采用的算法也不盡相同，此處不再列舉。3蛋白質(zhì)二級結(jié)構(gòu)分析 蛋白質(zhì)的二級結(jié)構(gòu)是由氨基酸骨架間的氫鍵決定的，通常有三種形態(tài)，螺旋（H），β鏈（E）和卷曲（C）。為了從蛋白質(zhì)原子的結(jié)構(gòu)中獲得更多的信息，蛋白質(zhì)二級結(jié)構(gòu)字典（DPSS)定義了蛋白質(zhì)二級結(jié)構(gòu)的八種狀態(tài)：三種螺旋，H(α-helix)、G(310-helix)和I(π-helix)，β鏈兩種，E(extendedstrandinparalleland/oranti-parallelβ-strandconformation)和B(β-bridge)，三種卷曲，S(bend)、T(turn)和C(coil)。預(yù)測二級結(jié)構(gòu)的第一步是搜索PDB數(shù)據(jù)庫尋找與查詢蛋白質(zhì)同源的蛋白質(zhì)的實驗三維結(jié)構(gòu)，例如FDM（FragmentDatabaseMining）算法首先會對PDB數(shù)據(jù)庫進行搜索。早期的二級結(jié)構(gòu)預(yù)測主要基于單個氨基酸的形成結(jié)構(gòu)的傾向，GOR算法采用這種方式，但現(xiàn)在的GOR算法通過貝葉斯統(tǒng)計等方法改進。CDM算法（ConsensusDataMining）結(jié)合了FDM和GOR的優(yōu)勢。還有很多方法各有其特點：PSIPRED，PSSM等。更現(xiàn)代的方法利用了最新的機器學(xué)習(xí)技術(shù)例如SVMs（SupportVectorMachines）和NNs（NeuralNetworks）。還有一些方法還考慮了氨基酸殘基的相對溶解度（RSA）。表四列舉了常用的蛋白質(zhì)二級結(jié)構(gòu)在線預(yù)測工具。表四蛋白質(zhì)二級結(jié)構(gòu)在線預(yù)測工具工具說明網(wǎng)絡(luò)鏈接CDMFDM+GORFDMPDBminingforstructuralfragmentsGORInformationtheory,Bayesianstatistics,PSSMprofilesJpredHMMandPSSMprofiles;NNs;RSAPHDMultiplesequencealignments;NNsPORTERPSSMprofiles;NNsPSIPREDPSSMprofiles;NNsSABLEPSSMprofiles;NNs;RSASSproPSSMprofiles;NNsandSVMs;RSA;8-stateprediction5蛋白質(zhì)跨膜結(jié)構(gòu)域分析跨膜（TM）蛋白跨過整個脂膜ADDINEN.CITE<EndNote><Cite><Author>Schulz</Author><Year>2002</Year><RecNum>262</RecNum><DisplayText>[8]</DisplayText><record><rec-number>262</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">262</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Schulz,G.E.</author></authors></contributors><auth-address>Schulz,GE UnivFreiburg,InstOrganChem&Biochem,Albertstr21,D-79104Freiburg,Germany UnivFreiburg,InstOrganChem&Biochem,D-79104Freiburg,Germany</auth-address><titles><title>Thestructureofbacterialoutermembraneproteins</title><secondary-title>BiochimicaEtBiophysicaActa-Biomembranes</secondary-title><alt-title>Bba-Biomembranes Bba-Biomembranes</alt-title></titles><periodical><full-title>BiochimicaEtBiophysicaActa-Biomembranes</full-title><abbr-1>Bba-Biomembranes</abbr-1></periodical><pages>308-317</pages><volume>1565</volume><number>2</number><keywords><keyword>beta-barrel</keyword><keyword>beta-helix</keyword><keyword>beta-twist</keyword><keyword>chainfoldtopology</keyword><keyword>channelengineering</keyword><keyword>shearnumber</keyword><keyword>beta-sheetbarrels</keyword><keyword>escherichia-coli</keyword><keyword>crystal-structure</keyword><keyword>3-dimensionalstructure</keyword><keyword>rhodobacter-capsulatus</keyword><keyword>conformational-changes</keyword><keyword>angstromresolution</keyword><keyword>paracoccus-denitrificans</keyword><keyword>salmonella-typhimurium</keyword><keyword>mitochondrialchannel</keyword></keywords><dates><year>2002</year><pub-dates><date>Oct11</date></pub-dates></dates><isbn>0005-2736</isbn><accession-num>WOS:0013</accession-num><urls><related-urls><url><GotoISI>://WOS:0013</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Schulz,2002#262"8]，通常被分為兩類α-helicalTM(AHTM)和TMβ-barrel(TMB)proteins。AHTM定位在細菌細胞膜的內(nèi)膜和真核生物的細胞膜上。它們的跨膜區(qū)域有極性的環(huán)鏈接而成的α螺旋。對TMB蛋白的了解還不多，它們的跨膜域為反向平行的桶裝β鏈通道ADDINEN.CITE<EndNote><Cite><Author>Schulz</Author><Year>2000</Year><RecNum>273</RecNum><DisplayText>[9]</DisplayText><record><rec-number>273</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">273</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Schulz,G.E.</author></authors></contributors><auth-address>Schulz,GE InstOrganChem&Biochem,Albertstr21,D-79104Freiburg,Germany InstOrganChem&Biochem,D-79104Freiburg,Germany</auth-address><titles><title>beta-Barrelmembraneproteins</title><secondary-title>CurrentOpinioninStructuralBiology</secondary-title><alt-title>CurrOpinStrucBiol CurrOpinStrucBiol</alt-title></titles><periodical><full-title>CurrentOpinioninStructuralBiology</full-title><abbr-1>CurrOpinStrucBiol</abbr-1></periodical><pages>443-447</pages><volume>10</volume><number>4</number><keywords><keyword>escherichia-coli</keyword><keyword>crystal-structure</keyword><keyword>paracoccus-denitrificans</keyword><keyword>salmonella-typhimurium</keyword><keyword>transmembranedomain</keyword><keyword>porinscry</keyword><keyword>channel</keyword><keyword>maltoporin</keyword><keyword>ompa</keyword><keyword>identification</keyword></keywords><dates><year>2000</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>0959-440X</isbn><accession-num>WOS:0010</accession-num><urls><related-urls><url><GotoISI>://WOS:0010</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Schulz,2000#273"9]。通過實驗的方法（X-ray和NMR等）來決定TM蛋白的結(jié)構(gòu)，相比較于球狀蛋白，解析的TM蛋白3D結(jié)構(gòu)非常有限。因此，人們開發(fā)了很多的方法用來預(yù)測蛋白質(zhì)的跨膜結(jié)構(gòu)域。這些方法中的大部分都只根據(jù)序列來識別跨膜結(jié)構(gòu)。表五列舉了常用的在線跨膜結(jié)構(gòu)域預(yù)測工具。表五在線跨膜結(jié)構(gòu)域預(yù)測工具工具方法預(yù)測的結(jié)構(gòu)網(wǎng)絡(luò)鏈接DAS-TMfilterDASAHTMMINNOURSA/SSAHTMandTMBPRED-TMMBHMMTMBPRED-TMBB/input.jspPRED-TMRHydrophobicityprofileAHTMandTMBSOSUIHydropathyscaleAHTMTMBETA-NETAminoacidcomposition;NNsTMBTMB-Huntk-NNalgorithmTMBTMMODHMMprofileAHTMTSEGTandemclustersofmembraneproteinsAHTMandTMBtseg_exe.html6蛋白質(zhì)三維結(jié)構(gòu)分析蛋白質(zhì)的三維結(jié)構(gòu)通常比其一級結(jié)構(gòu)更加保守。目前最可靠的蛋白質(zhì)三維結(jié)構(gòu)預(yù)測方法是同源建模法。同源建模通常有三步：1選擇模板，2目標(biāo)模板分析，3三維模型的構(gòu)建。有些同源建模法專注于同源建模的某些特定步驟，例如CPHmodels和DomainFishing專注于模板的選擇；ESyPred3D和Geno3D專注于目標(biāo)模板的分析。SWISS-MODEL是一個高度自動化的同源重組建模綜合服務(wù)ADDINEN.CITE<EndNote><Cite><Author>Kiefer</Author><Year>2009</Year><RecNum>275</RecNum><DisplayText>[10]</DisplayText><record><rec-number>275</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">275</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Kiefer,F.</author><author>Arnold,K.</author><author>Kunzli,M.</author><author>Bordoli,L.</author><author>Schwede,T.</author></authors></contributors><auth-address>Schwede,T UnivBasel,Biozentrum,Basel,Switzerland UnivBasel,Biozentrum,Basel,Switzerland SIBSwissInstBioinformat,Basel,Switzerland</auth-address><titles><title>TheSWISS-MODELRepositoryandassociatedresources</title><secondary-title>NucleicAcidsResearch</secondary-title><alt-title>NucleicAcidsRes NucleicAcidsRes</alt-title></titles><periodical><full-title>NucleicAcidsResearch</full-title><abbr-1>NucleicAcidsRes</abbr-1></periodical><pages>D387-D392</pages><volume>37</volume><keywords><keyword>protein-structuremodels</keyword><keyword>homologymodels</keyword><keyword>structuralgenomics</keyword><keyword>qualityassessment</keyword><keyword>drugdesign</keyword><keyword>database</keyword><keyword>binding</keyword><keyword>casp7</keyword><keyword>environment</keyword><keyword>prediction</keyword></keywords><dates><year>2009</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0305-1048</isbn><accession-num>WOS:0070</accession-num><urls><related-urls><url><GotoISI>://WOS:0070</url></related-urls></urls><language>English</language></record></Cite></EndNote>[\o"Kiefer,2009#275"10]，其他的在線同源重組建模工具如表六所示表六在線同源重組建模工具工具說明網(wǎng)絡(luò)鏈接CPHmodelsPSSMprosearchfortemplatesDomainFishingDomainsplitESyPred3DTarget-templatealignmentgeneratedbydifferentprogramsGeno3DTarget-templatealignmentusingPSI-BLASTSWISS-MODELIntegratedserviceTASSER-LiteIterativethreadingofthePDBfortemplateselection;structureassembly蛋白質(zhì)三維結(jié)構(gòu)預(yù)測還有其他的方法和工具例如折疊識別法和從頭計算法ADDINEN.CITE<EndNote><Cite><Author>Pavlopoulou</Author><Year>2011</Year><RecNum>278</RecNum><DisplayText>[11]</DisplayText><record><rec-number>278</rec-number><foreign-keys><keyapp="EN"db-id="da5sta0e8zrzaneszwax0t5o92tawfd09ra5">278</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Pavlopoulou,A.</author><author>Michalopoulos,I.</author></authors></contributors><auth-address>Michalopoulos,I AcadAthens,BiomedResFdn,CtrImmunol&Transplantat,SoranouEfessiou4,Athens11527,Greece AcadAthens,BiomedResFdn,CtrImmunol&Transplantat,Athens11527,Greece UnivPatras,DeptPharm,SchHlthSci,Rion,Greece</auth-address><titles><title>State-of-the-artbioinformaticsproteinstructurepredictiontools(Review)</title><secondary-title>InternationalJournalofMolecularMedicine</secondary-title><alt-title>IntJMolMed IntJMolMed</alt-title></titles><periodical><full-title>InternationalJournalofMolecularMedicine</full-title><abbr-1>IntJMolMed</abbr-1></periodical><pages>295-310</pages><volume>28</volume><number>3</number><keywords><keyword>proteinstructureprediction</keyword><keyword>multiplesequencealignment</keyword><keyword>pairwisesequencesimilaritysearch</keyword><keyword>phylogeneticanalysis</keyword><keyword>secondarystructureprediction</keyword><keyword>multiplesequencealignment</keyword><keyword>outer-membraneproteins</keyword><keyword>distantlyrelatedproteins</keyword><keyword>hiddenmarkov-models</keyword><keyword>amino-acid-sequence</keyword><k