外文翻译-- VAMA a versatile web-based tool for variability.PDF

VAMA:aversatileweb-basedtoolforvariabilityanalysisinmultiply-alignedaminoacidsequencesVAMA:VariabilityAnalysisofMultipleAlignmentsAditiGupta,AridamanPanditandSomdattaSinhaMathematicalModelingandComputationalBiologyGroup,CentreforCellularandMolecularBiology(CSIR)Hyderabad500007,IndiaEmail:sinhaccmb.res.inAbstractQuantifyingresiduevariabilityateachcolumninamultiplesequencealignmentofaminoacidshelpsinindicatingtheirsimilarities,andisusefultohighlightinformationaboutthesignificancesofeachpositionfromtheperspectiveoftheirstructure,function,andevolution.Itisbecomingincreasinglyclearthatthegroupsofaminoacidsthatallowconservedreplacementvarywiththepositionoftheresidueintheprotein.Mostmultiplealignmentalgorithmscatertogeneralusersandhencedonotaddressthisspecificfeature.Atoolforscoringvariabilityinmultiply-alignedaminoacidsequences,thatallowsdifferentconservationgroups,ishighlydesirable.VAMA(VariabilityAnalysisofMultipleAlignments)isasimpleyetversatileprogramthatcalculatesandplotsresiduevariabilityinagivensetofalignedsequencesbasedonknownconservationgroupsspecificfordifferentfunctionallyimportantregionsofaprotein,andalsoallowsuser-definedgroupsfornewdiscoveries.VAMAisavailableathttp:/203.200.217.184/VAMA/Overview.htmlKeywords-MultipleSequenceAlignment,VariabilityAnalysis,ResidueConservationGroupsI.INTRODUCTIONAlignmentofaminoacidsequencesiswidelyappliedtoidentifyconservationofresidues1.Variabilityisameasureoftheextentofvariationofaminoacidsatapositioninmultiplesequencealignment.Functionallyimportantresiduesareknowntoexhibithigherconservation,orlowervariability.Multiplesequencealignment(MSA)toolsalignsequencesbasedonsomepredeterminedclassificationofaminoacids,dependingontheirphysicochemicalproperties2.Recentstudieshaveshownthatthesamegroupofaminoacidsmaynotalwaysbeuseful,assequenceconservationclassificationsvarywiththestructureandfunctionaloftheprotein.Forexample,differentclassificationshavebeenshowntoexistforresiduesinvolvedinligandbinding3,inprotein-proteininteractioninterfaces4,inmaintainingstructure5,andindeterminingproteinfunctionalspecificity6-8.ClassificationbyMirnyandShakhnovich(MS)wasintendedforproteinstructurecores6；Williamsons(W)wastailoredtodealwithtransporterproteins8,whileGuharoyandChakrabarti(GC)classificationwasmeantforthestudyofinterfacialaminoacids4.ThissuggeststhatdifferentsubstitutionclassificationsshouldbeappliedinMSAbaseduponstructure/functionoftheproteins.Programmesavailableforstudyingconservationorvariabilityinmultiplealignmentsbasedondifferentscoringmethodsarerathercomplex9,andnoneofthemconsideralltheabove-mentionedposition-specificfeaturesinproteins10-15.Atoolforscoringvariabilityinmultiply-alignedaminoacidsequences,thatissimplebutaddressthisspecificfeatureofallowingdifferentconservationgroups,ishighlydesirable.Wehavedevelopedaweb-basedprogram(VAMA)thatquantifiesthevariabilityoftheresiduesateachalignedpositioninMSAusingasimplesymboldiversityscore16.Here,alldifferentaminoacidresiduespresentinaparticularcolumnofthealignedsequencesareconsidered,andthescore,v,iscalculatedusingthesumofthefrequencyofeachresidueas()1211iinNvN=(1)where,niisthefrequencyofeachresidue,andNthetotalnumberofresiduesatthisposition.Thesumisoverallthedifferenttypesofaminoacidspresentinthecolumn.ForcompleteconservationofaminoacidsataparticularcolumnintheMSA(i.e.,n=N),thescore,v=0,indicatingnovariability；whereas,fornoconservationortotaldiversity,thescoreisv=1.Since,vvariesbetween0and1,anormalizedscoreisobtained,whichisusefulforcomparisonofdifferentMSAs.BecauseofthegenericnatureofscoringinVAMA,whichisnotbasedonanystereochemicalpropertyoftheaminoacids,vcanbeusedtocalculatetherelativefrequenciesofaminoacidsforanygivenclassification.Here,alongwiththebasicscoringmethodologyadoptedbycommonlyusedMSAprogrammeCLUSTAL17,severaloptionsareprovidedforfunction-specificclassificationsasmentionedearlier.Thus,VAMAprovidestheuserwithflexibilitytoquantifyvariabilityusingthesedifferentclassificationsasrequired.II.FEATURESOFVAMAFig.1showstheVAMAinterface.TherearethreewaysinwhichvariabilityanalysisisdoneinVAMA:Basic,GroupBasedandReferenceSequenceBased.TheinputforVAMAcanbemultiplealignmentfilesinCLUSTALandFASTA978-1-4244-4713-8/10/$25.00©2010IEEEFigure1.VAMAinterface.formats.ThesefilescanbepastedontotheVAMAworkwindow,ormaybeuploadedusingthe“Browse”button.VAMAalsoaddressesthecommonproblemofexistenceof“gaps”inthealignment.TheusercandefineaGapcutoffforincludingresiduepositionshavinggapsinthealignment.Forcolumns,havingmoregapsthanGapcutoff,variabilityisnotcalculatedandablankspaceisdisplayedintheoutput.ForcolumnshavinggapslessthanorequaltothedefinedGapcutoff,thevalueofNisadjustedbysubtractingfromN,thenumberofgapsatthatposition.Thesepositionsareindicatedbya#intheoutput.VAMAcalculatesthevariabilityscoreforeachcolumninthealignmentbasedontheconservationgroupschosenbytheuser,andtheoutputfileconsistsofthefollowingparts-(i)thevariabilityscoreateachalignedposition；(ii)statisticsofthevariabilitydatadisplayingthemean,standarddeviationandrangeforthesame；and(iii)theplotofvariabilityvaluesversusalignmentpositions.ThedatacanbesavedbothintextandEXCELformatforfurtheranalysis.ThevariabilityanalysisinVAMAcanbedoneinthefollowingthreewaysA.BasicVariabilityAnalysisHereallaminoacidsareconsideredtobeinseparateclasses.Hence,itdoesnottakeintoaccountanysubstitutions,andanynon-identitycontributestothevariability.B.GroupBasedVariabilityAnalysisConservativesubstitutionscanbeaccountedforbyclassifyingaminoacidsaccordingtotheirphysicochemicalpropertiesandpositionalattributes.Variabilityisassigned0foraparticularpositioniftheaminoacidsbelongtoagroupinthespecificclassification.VAMAprovidesthefollowinggroupbasedanalysisoptions:i.DefaultClassification:ThisclassificationissameastheoneusedinCLUSTAL17.AminoacidsareclassifiedintostrongandweakgroupsbasedonthephysicochemicalpropertiesandtheGonnetPam250matrix18.ii.MS,GC,andWClassifications:Severaldifferentclassificationsareproposeddependinguponthefunctionalconstraintsapplicable.MSclassificationisapplicabletoproteinstructurecores6,Wclassificationisapplicabletotransporterproteins8,andGCclassificationisapplicabletotheinterfaceaminoacids4.TheclassificationsaredescribedintheUserGuide.iii.UserDefinedClassification:Variousotherclassificationschemeshavebeenproposed7.VAMAoffersuserstheoptiontouseanyortheirownclassification.C.ReferenceSequenceBasedVariabilityAnalysisReferencesequenceisthesequencewithrespecttowhichtheresiduesarenumbered.Herex=0inthevariabilityplotcorrespondstofirstresidueofthereferencesequence.Thisisusefulwhenthe3-dimensionalstructureofthereferencesequenceisavailabletohelpanalyzetheresultsforposition-specificchangesinotheraminoacidsequencesinthealignment.Thecalculationisfirstdonebasedupondifferentclassifications,andthentheresiduesarenumberedaccordingtotheReferenceSequencegivenbytheuser.BythismethodtheusercanaccessthealignmentagainsttheReferenceSequence.AnExampleofanalysis,usingVAMA,isshowninFig.2.Asetof25aminoacidsequencesrepresentingtheRosmannfold19wereextractedfromProteinDataBank20.CLUSTAL-alignedsequenceswerepastedasinputtoVAMA.WeusedMSclassificationtocalculatethevariability,andcompareditwiththeDefault(CLUSTAL)classification.Fig.2AisthevariabilityplotforMSandDefaultclassifications,showinglowerscoreforMSclassificationthanthatoftheDefaultclassification.InFig.2A,Gapcutoffof0isusedtocalculatethescores.Thus,forpositionswith1ormoregaps,variabilityisnotcalculated.Rosmannfoldbeinganexampleofproteinstructurecore,theresultswithMSaremorereliable.Fig.2BalsoshowsthattheMSclassificationgivestheloweststatisticswhencomparedtoothers.Hereweshowonlythefirstsubsetofpositions(92-100)forwhichthevariabilityscoreswerecalculated.Clearly,theminimumvariabilityscoregivenbyMSclassificationadvocatestheapplicabilityofthistool.Importantly,incaseofaproteinlackingwell-definedfunction,VAMAallowscalculationofthevariabilityscoreusingthegivenclassificationstoidentifyfunctionallyandstructurallyimportantresiduesbasedontheircomparativescore.Thisfeature,whereseveraldifferentclassificationscanbeusedtocalculatethevariabilityinMSA,isuniquetoVAMA.Figure2.AnalysisofVAMAoutputfor25aminoacidsequencesoftheRossmanFold.(A)VariabilityplotcomparingMSandDefaultclassificationscores,(B)Variabilityvaluesofresidues92to100,usingdifferentclassifications,alongwithmeanandstandarddeviation(SD).VAMAincludesseveralusefulfeaturesfortheuser.The“UserGuide”explainsallfeaturesclearlywithexample.The“RelatedLinks”provideslinkstootherMSAtools(e.g.CLUSTALW,T-Coffee,CINEMA,etc.),andusefulwebsitessuchas,NCBI,PDB,Swiss-Prot,andKEGG.A“Search”buttonallowssearchwithinVAMAandtheWorldWideWeb.III.DISCUSSIONVAMAisasimple,user-friendly,yetversatile,toolforcalculatingthevariabilityinmultiplyalignedproteinsequences.VAMAsupportsbothbasicandgroupbasedanalysis,byconsideringthephysicochemicalpropertiesofaminoacids,aswellastheirdifferentialusagefordifferenttopologicaldeterminantsintheprotein.Itquantifiesvariabilityinthesequencesbasedonaminoacidclassificationgroupsdependingontheabovefactors.Gap-cutofffeatureactsasanadditionaltooltoscorethesequences.Statisticalanalysisperformedonthevariabilitydata,likemean,standarddeviationandrange,canalsobehelpfulinfurtheranalysis.VAMAis,thus,ausefulfunction-specificvariabilityanalysistoolthatallowsacomparativeanalysisafeaturelackinginothersimilartools.ACKNOWLEDGMENTSSthanksDepartmentofBiotechnology,Indiaforfinancialsupport.AGthankstheIndianAcademyofSciencesforasummerfellowshiptoworkattheCCMB.APthankstheCouncilofScientificandIndustrialResearch(CSIR)forfellowship.REFERENCES1T.F.Smith,andM.S.Waterman,“IdentificationofCommonMolecularSubsequences.”J.Mol.Biol.,vol.147,pp.195-197,1981.2D.J.Lipman,S.F.Altschul,andJ.D.Kececioglu,“Atoolformultiplesequencealignment.”Proc.Natl.Acad.Sci.USA,vol.86,pp.4412-4415,1989.3T.J.Magliery,andL.Regan,“Sequencevariationinligandbindingsitesinproteins.”BMCBioinformatics,vol.6,p.240,2005.4M.Guharoy,andP.Chakrabarti,“Conservationandrelativeimportanceofresiduesacrossprotein-proteininterfaces.”Proc.Natl.Acad.Sci.USA,vol.102,pp.15447-15452,2005.5O.Schueler-Furman,andD.Baker,“Conse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