简介：Werecentlyreportedtheuseofagene-trappingapproachtoisolatecellclonesinwhichareportergenehadintegratedintogenesmodulatedbyT-cellactivation.WehavenowtestedapanelofclonesfromthatreportandidentifiedtheonethatrespondstoavarietyofG-proteincoupledreceptors(GPCR).TheβlactamasetaggedEGR-3JurkatcellwasusedtodissectspecificGPCRsignalinginvivo.ThreeGPCRswerestudied,includingthechemokinereceptorCXCR4(Gicoupled)thatwasendogenouslyexpressed,theplateletactivationfactor(PAF)receptor(Gq-coupled),andβ2adrenergicreceptor(Gs-coupled)thatwasbothstablytransfected.Agonistsforeachreceptoractivatedtranscriptionoftheβ-lactamasetaggedEGR-3gene.InductionofEGR-3throughCXCR4wasblockedbypertussistoxinandPD58059,aspecificinhibitorofMEK(MAPK/ERKkinase).NeitheroftheseinhibitorsblockedisoproterenolorPAF-mediatedactivationofEGR-3.Conversely,β2-andPAF-mediatedEGR-3activationwasblockedbythep38,specificinhibitorSB580.Inaddition,bothβ2-andPAF-mediatedEGR-3activationcouldbesynergisticallyactivatedbyCXCR4activation.ThiscombinedresultindicatesthatEGR-3canbeactivatedthroughdistinctsignaltransductionpathwaysbydifferentGPCRsandthatsignalscanbeintegratedandamplifiedtoefficientlytunethelevelofactivation.

简介：G-proteincoupledreceptors(GPCRs)representoneofthemostimportantclassesofdrugtargetsforpharmaceuticalindustryandplayimportantrolesincellularsignaltransduction.PredictingthecouplingspecificityofGPCRstoG-proteinsisvitalforfurtherunderstandingthemechanismofsignaltransductionandthefunctionofthereceptorswithinacell,whichcanprovidenewcluesforpharmaceuticalresearchanddevelopment.Inthisstudy,thefeaturesofaminoacidcompositionsandphysiochemicalpropertiesofthefull-lengthGPCRsequenceshavebeenanalyzedandextracted.Basedonthesefeatures,classifiershavebeendevelopedtopredictthecouplingspecificityofGPCRstoG-proteinsusingsupportvectormachines.Thetestingresultsshowthatthismethodcouldobtainbetterpredictionaccuracy.

简介：Circulargenomes,beingthelargestproportionofsequencedgenomes,playanimportantroleingenomeanalysis.However,traditional2Dcircularmaponlyprovidesanoverviewandannotationsofgenomebutdoesnotofferfeature-basedcomparison.Forremedyingtheseshortcomings,wedeveloped3DGenomeTuner,ahybridofcircularmapandcomparativemaptools.Itscapabilityofviewingcomparisonsbetweenmultiplecircularmapsina3Dspaceoffersgreatbenefitstothestudyofcomparativegenomics.Theprogramisfreelyavailable(underanLGPLlicence)athttp://sourceforge.net/projects/dgenometuner.

简介：Microarrayhasbecomeapopularbiotechnologyinbiologicalandmedicalresearch.However,systematicandstochasticvariabilitiesinmicroarraydataareexpectedandunavoidable,resultingintheproblemthattherawmeasurementshaveinherent"noise"withinmicroarrayexperiments.Currently,logarithmicratiosareusuallyanalyzedbyvariousclusteringmethodsdirectly,whichmayintroducebiasinterpretationinidentifyinggroupsofgenesorsamples.Inthispaper,astatisticalmethodbasedonmixedmodelapproacheswasproposedformicroarraydataclusteranalysis.TheunderlyingrationaleofthismethodistopartitiontheobservedtotalgeneexpressionlevelintovariousvariationscausedbydifferentfactorsusinganANOVAmodel,andtopredictthedifferentialeffectsofGV(genebyvariety)interactionusingtheadjustedunbiasedprediction(AUP)method.ThepredictedGVinteractioneffectscanthenbeusedastheinputsofclusteranalysis.Weillustratedtheapplicationofourmethodwithageneexpressiondatasetandelucidatedtheutilityofourapproachusinganexternalvalidation.

简介：Annotationsofcompletegenomesequencessubmitteddirectlyfromsequencingprojectsarediverseintermsofannotationstrategiesandupdatefrequencies.Theseinconsistenciesmakecomparativestudiesdifficult.Toallowrapiddataprepara-tionofalargenumberofcompletegenomes,automationandspeedareimpor-tantforgenomere-annotation.Hereweintroduceanopen-sourcerapidgenomere-annotationsoftwaresystem,Restauro-G,specializedforbacterialgenomes.Restauro-Gre-annotatesagenomebysimilaritysearchesutilizingtheBLAST-LikeAlignmentTool,referringtoproteindatabasessuchasUniProtKB,NCBInr,NCBICOGs,Pfam,andPSORTb.Re-annotationbyRestauro-Gachievedover98%accuracyformostbacterialchromosomesincomparisonwiththeoriginalmanuallycuratedannotationofEMBLreleases.Restauro-GwasdevelopedinthegenericbioinformaticsworkbenchG-languageGenomeAnalysisEnvironmentandisdistributedathttp://restauro-g.iab.keio.ac.jp/undertheGNUGeneralPublicLicense.

简介：~~

简介：Thestudyofsmalldrugmoleculesinteractingwithnucleicacidsisanareaofintenseresearchthathasparticularrelevanceinourunderstandingofrelativemechanisminchemotherapeuticapplicationsandtheassociationbetweengenetics(includingsequencevariation)anddrugresponse.Inthiscontribution,wedemonstratehowthesequence-specificbindingofananticancerdrugDacarbazine(DTIC)tosinglebase(A-G)mismatchcouldbesensitivelydetectedbycombiningelectrochemicaldetectionwithbiosensingsurfacebasedongoldnanoparticles.

简介：UnderstandingthecouplingspecificitybetweenGprotein-coupledreceptors(GPCRs)andspecificclassesofGproteinsisimportantforfurtherelucidationofreceptorfunctionswithinacell.IncreasinginformationonGPCRsequencesandtheGproteinfamilywouldfacilitatepredictionofthecouplingpropertiesofGPCRs.Inthisstudy,wedescribeanovelapproachforpredictingthecouplingspecificitybetweenGPCRsandGproteins.ThismethodusesnotonlyGPCRsequencesbutalsothefunctionalknowledgegeneratedbynaturallanguagepro-cessing,andcanachieve92.2%predictionaccuracybyusingtheC4.5algorithm.Furthermore,rulesrelatedtoGPCR-Gproteincouplingaregenerated.Thecom-binationofsequenceanalysisandtextminingimprovesthepredictionaccuracyforGPCR-Gproteincouplingspecificity,andalsoprovidescluesforunderstandingGPCRsignaling.

简介：AcomputationalsystemforthepredictionandclassificationofhumanG-proteincoupledreceptors(GPCRs)hasbeendevelopedbasedonthesupportvectormachine(SVM)methodandproteinsequenceinformation.ThefeaturevectorsusedtodeveloptheSVMpredictionmodelsconsistofstatisticallysignificantfeaturesselectedfromsingleaminoacid,dipeptide,andtripeptidecompositionsofproteinsequences.Furthermore,thelengthdistributiondifferencebetweenGPCRsandnon-GPCRshasalsobeenexploitedtoimprovethepredictionperformance.ThetestingresultswithannotatedhumanproteinsequencesdemonstratethatthissystemcangetgoodperformanceforbothpredictionandclassificationofhumanGPCRs.

简介：G-proteincoupledreceptors(GPCRs)areaclassofseven-helixtransmembraneproteinsthathavebeenusedinbioinformaticsasthetargetstofacilitatedrugdiscoveryforhumandiseases.AlthoughthousandsofGPCRsequenceshavebeencollected,theligandspecificityofmanyGPCRsisstillunknownandonlyonecrystalstructureoftherhodopsin-likefamilyhasbeensolved.Therefore,identifyingGPCRtypesonlyfromsequencedatahasbecomeanimportantresearchissue.Inthisstudy,anoveltechniqueforidentifyingGPCRtypesbasedontheweightedLevenshteindistancebetweentworeceptorsequencesandthenearestneighbormethod(NNM)isintroduced,whichcandealwithreceptorsequenceswithdifferentlengthsdirectly.Inourexperimentsforclassifyingfourclasses(acetylcholine,adrenoceptor,dopamine,andserotonin)oftherhodopsin-likefamilyofGPCRs,theerrorratesfromtheleave-one-outprocedureandtheleave-half-outprocedurewere0.62%and1.24%,respectively.Theseresultsarepriortothoseofthecovariantdiscriminantalgorithm,thesupportvectormachinemethod,andtheNNMwithEuclideandistance.

简介：~~

简介：Ithasbeenshownthattheprogressinthedeterminationofmembraneproteinstructuregrowsexponentially,withapproximatelythesamegrowthrateasthatofthewater-solubleproteins.Inordertoinvestigatetheeffectofthis,ontheperformanceofpredictionalgorithmsforbothα-helicalandβ-barrelmembraneproteins,weconductedaprospectivestudybasedonhistoricalrecords.WetrainedseparatehiddenMarkovmodelswithdifferentsizedtrainingsetsandevaluatedtheirperformanceontopologypredictionforthetwoclassesoftransmembraneproteins.Weshowthattheexistingtop-scoringalgorithmsforpredictingthetransmembranesegmentsofα-helicalmembraneproteinsperformslightlybetterthanthatofβ-barreloutermembraneproteinsinallmeasuresofaccuracy.Withthesamerationale,ameta-analysisoftheperformanceofthesecondarystructurepredictionalgorithmsindicatesthatexistingalgorithmictechniquescannotbefurtherimprovedbyjustaddingmorenon-homologoussequencestothetrainingsets.Theupperlimitforsecondarystructurepredictionisestimatedtobenomorethan70%and80%ofcorrectlypredictedresiduesforsinglesequencebasedmethodsandmultiplesequencebasedones,respectively.Therefore,weshouldconcentrateoureffortsonutilizingnewtechniquesforthedevelopmentofevenbetterscoringpredictors.