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VAMAaversatilewebbasedtoolforvariabilityanalysisinmultiplyalignedaminoacidsequencesVAMAVariabilityAnalysisofMultipleAlignmentsAditiGupta,AridamanPanditandSomdattaSinhaMathematicalModelingandComputationalBiologyGroup,CentreforCellularandMolecularBiologyCSIRHyderabad500007,IndiaEmailsinhaccmb.res.inAbstractQuantifyingresiduevariabilityateachcolumninamultiplesequencealignmentofaminoacidshelpsinindicatingtheirsimilarities,andisusefultohighlightinformationaboutthesignificancesofeachpositionfromtheperspectiveoftheirstructure,function,andevolution.Itisbecomingincreasinglyclearthatthegroupsofaminoacidsthatallowconservedreplacementvarywiththepositionoftheresidueintheprotein.Mostmultiplealignmentalgorithmscatertogeneralusersandhencedonotaddressthisspecificfeature.Atoolforscoringvariabilityinmultiplyalignedaminoacidsequences,thatallowsdifferentconservationgroups,ishighlydesirable.VAMAVariabilityAnalysisofMultipleAlignmentsisasimpleyetversatileprogramthatcalculatesandplotsresiduevariabilityinagivensetofalignedsequencesbasedonknownconservationgroupsspecificfordifferentfunctionallyimportantregionsofaprotein,andalsoallowsuserdefinedgroupsfornewdiscoveries.VAMAisavailableathttp//203.200.217.184/VAMA/Overview.htmlKeywordsMultipleSequenceAlignment,VariabilityAnalysis,ResidueConservationGroupsI.INTRODUCTIONAlignmentofaminoacidsequencesiswidelyappliedtoidentifyconservationofresidues1.Variabilityisameasureoftheextentofvariationofaminoacidsatapositioninmultiplesequencealignment.Functionallyimportantresiduesareknowntoexhibithigherconservation,orlowervariability.MultiplesequencealignmentMSAtoolsalignsequencesbasedonsomepredeterminedclassificationofaminoacids,dependingontheirphysicochemicalproperties2.Recentstudieshaveshownthatthesamegroupofaminoacidsmaynotalwaysbeuseful,assequenceconservationclassificationsvarywiththestructureandfunctionaloftheprotein.Forexample,differentclassificationshavebeenshowntoexistforresiduesinvolvedinligandbinding3,inproteinproteininteractioninterfaces4,inmaintainingstructure5,andindeterminingproteinfunctionalspecificity68.ClassificationbyMirnyandShakhnovichMSwasintendedforproteinstructurecores6WilliamsonsWwastailoredtodealwithtransporterproteins8,whileGuharoyandChakrabartiGCclassificationwasmeantforthestudyofinterfacialaminoacids4.ThissuggeststhatdifferentsubstitutionclassificationsshouldbeappliedinMSAbaseduponstructure/functionoftheproteins.Programmesavailableforstudyingconservationorvariabilityinmultiplealignmentsbasedondifferentscoringmethodsarerathercomplex9,andnoneofthemconsideralltheabovementionedpositionspecificfeaturesinproteins1015.Atoolforscoringvariabilityinmultiplyalignedaminoacidsequences,thatissimplebutaddressthisspecificfeatureofallowingdifferentconservationgroups,ishighlydesirable.WehavedevelopedawebbasedprogramVAMAthatquantifiesthevariabilityoftheresiduesateachalignedpositioninMSAusingasimplesymboldiversityscore16.Here,alldifferentaminoacidresiduespresentinaparticularcolumnofthealignedsequencesareconsidered,andthescore,v,iscalculatedusingthesumofthefrequencyofeachresidueas1211iinNvN−⎡⎤⎛⎞−⎢⎥⎜⎟⎝⎠⎣⎦−∑1where,niisthefrequencyofeachresidue,andNthetotalnumberofresiduesatthisposition.Thesumisoverallthedifferenttypesofaminoacidspresentinthecolumn.ForcompleteconservationofaminoacidsataparticularcolumnintheMSAi.e.,nN,thescore,v0,indicatingnovariabilitywhereas,fornoconservationortotaldiversity,thescoreisv1.Since,vvariesbetween0and1,anormalizedscoreisobtained,whichisusefulforcomparisonofdifferentMSAs.BecauseofthegenericnatureofscoringinVAMA,whichisnotbasedonanystereochemicalpropertyoftheaminoacids,vcanbeusedtocalculatetherelativefrequenciesofaminoacidsforanygivenclassification.Here,alongwiththebasicscoringmethodologyadoptedbycommonlyusedMSAprogrammeCLUSTAL17,severaloptionsareprovidedforfunctionspecificclassificationsasmentionedearlier.Thus,VAMAprovidestheuserwithflexibilitytoquantifyvariabilityusingthesedifferentclassificationsasrequired.II.FEATURESOFVAMAFig.1showstheVAMAinterface.TherearethreewaysinwhichvariabilityanalysisisdoneinVAMABasic,GroupBasedandReferenceSequenceBased.TheinputforVAMAcanbemultiplealignmentfilesinCLUSTALandFASTA9781424447138/10/25.00©2010IEEEFigure1.VAMAinterface.formats.ThesefilescanbepastedontotheVAMAworkwindow,ormaybeuploadedusingtheBrowsebutton.VAMAalsoaddressesthecommonproblemofexistenceofgapsinthealignment.TheusercandefineaGapcutoffforincludingresiduepositionshavinggapsinthealignment.Forcolumns,havingmoregapsthanGapcutoff,variabilityisnotcalculatedandablankspaceisdisplayedintheoutput.ForcolumnshavinggapslessthanorequaltothedefinedGapcutoff,thevalueofNisadjustedbysubtractingfromN,thenumberofgapsatthatposition.Thesepositionsareindicatedbyaintheoutput.VAMAcalculatesthevariabilityscoreforeachcolumninthealignmentbasedontheconservationgroupschosenbytheuser,andtheoutputfileconsistsofthefollowingpartsithevariabilityscoreateachalignedpositioniistatisticsofthevariabilitydatadisplayingthemean,standarddeviationandrangeforthesameandiiitheplotofvariabilityvaluesversusalignmentpositions.ThedatacanbesavedbothintextandEXCELformatforfurtheranalysis.ThevariabilityanalysisinVAMAcanbedoneinthefollowingthreeways–A.BasicVariabilityAnalysisHereallaminoacidsareconsideredtobeinseparateclasses.Hence,itdoesnottakeintoaccountanysubstitutions,andanynonidentitycontributestothevariability.B.GroupBasedVariabilityAnalysisConservativesubstitutionscanbeaccountedforbyclassifyingaminoacidsaccordingtotheirphysicochemicalpropertiesandpositionalattributes.Variabilityisassigned0foraparticularpositioniftheaminoacidsbelongtoagroupinthespecificclassification.VAMAprovidesthefollowinggroupbasedanalysisoptionsi.DefaultClassificationThisclassificationissameastheoneusedinCLUSTAL17.AminoacidsareclassifiedintostrongandweakgroupsbasedonthephysicochemicalpropertiesandtheGonnetPam250matrix18.ii.MS,GC,andWClassificationsSeveraldifferentclassificationsareproposeddependinguponthefunctionalconstraintsapplicable.MSclassificationisapplicabletoproteinstructurecores6,Wclassificationisapplicabletotransporterproteins8,andGCclassificationisapplicabletotheinterfaceaminoacids4.TheclassificationsaredescribedintheUserGuide.iii.UserDefinedClassificationVariousotherclassificationschemeshavebeenproposed7.VAMAoffersuserstheoptiontouseanyortheirownclassification.C.ReferenceSequenceBasedVariabilityAnalysisReferencesequenceisthesequencewithrespecttowhichtheresiduesarenumbered.Herex0inthevariabilityplotcorrespondstofirstresidueofthereferencesequence.Thisisusefulwhenthe3dimensionalstructureofthereferencesequenceisavailabletohelpanalyzetheresultsforpositionspecificchangesinotheraminoacidsequencesinthealignment.Thecalculationisfirstdonebasedupondifferentclassifications,andthentheresiduesarenumberedaccordingtotheReferenceSequencegivenbytheuser.BythismethodtheusercanaccessthealignmentagainsttheReferenceSequence.AnExampleofanalysis,usingVAMA,isshowninFig.2.Asetof25aminoacidsequencesrepresentingtheRosmannfold19wereextractedfromProteinDataBank20.CLUSTALalignedsequenceswerepastedasinputtoVAMA.WeusedMSclassificationtocalculatethevariability,andcompareditwiththeDefaultCLUSTALclassification.Fig.2AisthevariabilityplotforMSandDefaultclassifications,showinglowerscoreforMSclassificationthanthatoftheDefaultclassification.InFig.2A,Gapcutoffof0isusedtocalculatethescores.Thus,forpositionswith1ormoregaps,variabilityisnotcalculated.Rosmannfoldbeinganexampleofproteinstructurecore,theresultswithMSaremorereliable.Fig.2BalsoshowsthattheMSclassificationgivestheloweststatisticswhencomparedtoothers.Hereweshowonlythefirstsubsetofpositions92100forwhichthevariabilityscoreswerecalculated.Clearly,theminimumvariabilityscoregivenbyMSclassificationadvocatestheapplicabilityofthistool.Importantly,incaseofaproteinlackingwelldefinedfunction,VAMAallowscalculationofthevariabilityscoreusingthegivenclassificationstoidentifyfunctionallyandstructurallyimportantresiduesbasedontheircomparativescore.Thisfeature,whereseveraldifferentclassificationscanbeusedtocalculatethevariabilityinMSA,isuniquetoVAMA.Figure2.AnalysisofVAMAoutputfor25aminoacidsequencesoftheRossmanFold.AVariabilityplotcomparingMSandDefaultclassificationscores,BVariabilityvaluesofresidues92to100,usingdifferentclassifications,alongwithmeanandstandarddeviationSD.VAMAincludesseveralusefulfeaturesfortheuser.TheUserGuideexplainsallfeaturesclearlywithexample.TheRelatedLinksprovideslinkstootherMSAtoolse.g.CLUSTALW,TCoffee,CINEMA,etc.,andusefulwebsitessuchas,NCBI,PDB,SwissProt,andKEGG.ASearchbuttonallowssearchwithinVAMAandtheWorldWideWeb.III.DISCUSSIONVAMAisasimple,userfriendly,yetversatile,toolforcalculatingthevariabilityinmultiplyalignedproteinsequences.VAMAsupportsbothbasicandgroupbasedanalysis,byconsideringthephysicochemicalpropertiesofaminoacids,aswellastheirdifferentialusagefordifferenttopologicaldeterminantsintheprotein.Itquantifiesvariabilityinthesequencesbasedonaminoacidclassificationgroupsdependingontheabovefactors.Gapcutofffeatureactsasanadditionaltooltoscorethesequences.Statisticalanalysisperformedonthevariabilitydata,likemean,standarddeviationandrange,canalsobehelpfulinfurtheranalysis.VAMAis,thus,ausefulfunctionspecificvariabilityanalysistoolthatallowsacomparativeanalysis–afeaturelackinginothersimilartools.ACKNOWLEDGMENTSSthanksDepartmentofBiotechnology,Indiaforfinancialsupport.AGthankstheIndianAcademyofSciencesforasummerfellowshiptoworkattheCCMB.APthankstheCouncilofScientificandIndustrialResearchCSIRforfellowship.REFERENCES1T.F.Smith,andM.S.Waterman,IdentificationofCommonMolecularSubsequences.J.Mol.Biol.,vol.147,pp.195197,1981.2D.J.Lipman,S.F.Altschul,andJ.D.Kececioglu,Atoolformultiplesequencealignment.Proc.Natl.Acad.Sci.USA,vol.86,pp.44124415,1989.3T.J.Magliery,andL.Regan,Sequencevariationinligandbindingsitesinproteins.BMCBioinformatics,vol.6,p.240,2005.4M.Guharoy,andP.Chakrabarti,Conservationandrelativeimportanceofresiduesacrossproteinproteininterfaces.Proc.Natl.Acad.Sci.USA,vol.102,pp.1544715452,2005.5O.SchuelerFurman,andD.Baker,Conservedresidueclusteringandproteinstructureprediction.ProteinsStructureFunctionandBioinformatics,vol.52,pp.225235,2003.6L.A.Mirny,andE.I.Shakhnovich,Evolutionaryconservationofthefoldingnucleus.J.Mol.Biol.,vol.308,pp.123129,2001.7W.R.Taylor,Theclassificationofaminoacidconservation.J.Theor.Biol.,vol.119,pp.205218,1986.8R.M.Williamson,Informationtheoryanalysisoftherelationshipbetweenprimarysequencestructureandligandrecognitionamongaclassoffacilitatedtransporters.J.Theor.Biol.,vol.174,pp.179188,1995.9W.S.J.Valdar,Scoringresidueconservation.ProteinsStructure,FunctionandBioinformatics,vol.48,pp.227241,2002.10J.A.Capra,andM.Singh,Predictingfunctionallyimportantresiduesfromsequenceconservation.Bioinformatics,vol.23,pp.18751882,2007.11M.Clamp,J.Cuff,S.M.Searle,andG.J.Barton,TheJalviewJavaAlignmentEditor.Bioinformatics,vol.20,pp.426427,2004.12C.D.Livingstone,andG.J.Barton,ProteinSequenceAlignmentsAStrategyfortheHierarchicalAnalysisofResidueConservation.Comp.Appl.Biosci.,vol.9,pp.745756,1993.13D.J.ParrySmith,andT.K.Attwood,SOMAPanovelinteractiveapproachtomultipleproteinsequencesalignment.Comp.Appl.Biosci.,vol.7,pp.233235,1991.14D.J.ParrySmith,A.W.Payne,A.D.Michie,andT.K.Attwood,CINEMAanovelColourINteractiveEditorforMultipleAlignments.Gene,vol.221,pp.GC5763,1998.15M.R.Southern,andA.P.Lewis,JavaShademultiplesequencealignmentboxandshadingontheWorldWideWeb.Bioinformatics,vol.14,pp.821822,1998.16I.P.Crawford,EvolutionofaBiosyntheticPathwayTheTryptophanParadigm.Annu.Rev.Microbiol.,vol.43,pp.567600,1989.17F.Jeanmougin,J.D.Thompson,M.Gouy,D.G.Higgins,andT.J.Gibson,MultiplesequencealignmentwithClustalX.TrendsBiochem.Sci.,vol.23,pp.403405,1998.18G.Gonnet,M.A.Cohen,andS.A.Benner,Exhaustivematchingoftheentireproteinsequencedatabase.Science,vol.256,pp.14431445,1992.19J.E.Donald,I.A.Hubner,V.M.Rotemberg,E.I.Shakhnovich,andL.A.Mirny,CoCadatabaseofuniversallyconservedresiduesinproteinfolds.Bioinformatics,vol.21,pp.25392540,2005.20PDBhttp//www.rscb.org
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