1Combinatorial complexity and compositional drift in protein interaction networks Eric J Deeds1 Jean Krivine2 Jerome Feret3 Vincent Danos4 Walter Fontana5

Description

Niveau: Supérieur, Doctorat, Bac+8
1Combinatorial complexity and compositional drift in protein interaction networks Eric J. Deeds1, Jean Krivine2, Jerome Feret3, Vincent Danos4, Walter Fontana5,? 1 Center for Bioinformatics and Department of Molecular Biosciences, The University of Kansas, Lawrence KS 66047, USA 2 Laboratoire PPS de l'Universite Paris 7 and CNRS, F-75230 Paris Cedex 13, France 3 Laboratoire d'Informatique de l'Ecole normale superieure, INRIA, ENS, and CNRS, 45 rue d'Ulm, F-75230 Paris Cedex 05, France 4 School of Informatics, University of Edinburgh, Edinburgh, UK 5 Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston MA 02115, USA ? E-mail: Abstract The assembly of molecular machines and transient signaling complexes does not typically occur under circumstances in which the appropriate proteins are isolated from all others present in the cell. Rather, assembly must proceed in the context of large-scale protein-protein interaction (PPI) networks that are characterized both by conflict and combinatorial complexity. Conflict refers to the fact that protein interfaces can often bind many different partners in a mutually exclusive way, while combinatorial com- plexity refers to the explosion in the number of distinct complexes that can be formed by a network of binding possibilities. Using computational models, we explore the consequences of these characteristics for the global dynamics of a PPI network based on highly curated yeast two-hybrid data.

• binding capabilities


• protein interaction


• networks lack detailed


• interaction networks


• interaction


• unique molecular


• molecular speciesplain


• throughput experiments


• proteins

Sujets

• France 3
• Sin-le-Noble
• France 4
• Protein?protein interaction

Informations

• Publié par: profil-zyak-2012
• Nombre de lectures: 17
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Combinatorialcomplexityandcompositionaldriftinprotein interactionnetworks EricJ.Deeds 1 ,JeanKrivine 2 ,Je´roˆmeFeret 3 ,VincentDanos 4 ,WalterFontana 5, ∗ 1 1CenterforBioinformaticsandDepartmentofMolecularBiosciences,TheUniversityof Kansas,LawrenceKS66047,USA 2LaboratoirePPSdel’Universite´Paris7andCNRS,F-75230ParisCedex13,France 3Laboratoired’Informatiquedel’E ´ colenormalesupe´rieure,INRIA,E ´ NS,andCNRS,45 rued’Ulm,F-75230ParisCedex05,France 4SchoolofInformatics,UniversityofEdinburgh,Edinburgh,UK 5DepartmentofSystemsBiology,HarvardMedicalSchool,200LongwoodAvenue, BostonMA02115,USA ∗ E-mail:[e-mail] Abstract Theassemblyofmolecularmachinesandtransientsignalingcomplexesdoesnottypicallyoccurunder circumstancesinwhichtheappropriateproteinsareisolatedfromallotherspresentinthecell.Rather, assemblymustproceedinthecontextoflarge-scaleprotein-proteininteraction(PPI)networksthatare characterizedbothbyconflictandcombinatorialcomplexity.Conflictreferstothefactthatprotein interfacescanoftenbindmanydifferentpartnersinamutuallyexclusiveway,whilecombinatorialcom- plexityreferstotheexplosioninthenumberofdistinctcomplexesthatcanbeformedbyanetworkof bindingpossibilities.Usingcomputationalmodels,weexploretheconsequencesofthesecharacteristics fortheglobaldynamicsofaPPInetworkbasedonhighlycuratedyeasttwo-hybriddata.Thelimited molecularcontextrepresentedinthisdata-typetranslatesformallyintoanassumptionofindependent bindingsitesforeachprotein.Thechallengeofavoidingtheexplicitenumerationoftheastronomically manypossibilitiesforcomplexformationismetbyarule-basedapproachtokineticmodeling.Despite imposingglobalbiophysicalconstraints,wefindthatinitiallyidenticalsimulationsrapidlydivergeinthe spaceofmolecularpossibilities,eventuallysamplingdisjointsetsoflargecomplexes.Werefertothis phenomenonas“compositionaldrift”.SinceinteractiondatainPPInetworkslackdetailedinformation aboutgeometricandbiologicalconstraints,ourstudydoesnotrepresentaquantitativedescriptionof cellulardynamics.Rather,ourworkbringstolightafundamentalproblem(thecontrolofcompositional drift)thatmustbesolvedbymechanismsofassemblyinthecontextoflargenetworks.Incaseswhere driftisnot(orcannotbe)completelycontrolledbythecell,thisphenomenoncouldconstituteanovel sourceofphenotypicheterogeneityincellpopulations. Introduction Alargefractionofcurrentdatainmolecularbiologyhasbeenderivedfromthecollationandcuration ofpredominantlystatictypesofdata,suchasgenomicsequencesandproteinstructures.However,at increasingrate,proteomichigh-throughputmethods,suchasyeasttwo-hybridassays,proteincomplemen- tationassays,affinitypurificationwithmassspectrometry,peptidephagedisplay,andproteinmicroarrays areyieldingdataaboutprotein-proteininteractions(PPI)whosesignificanceresidesinthesystembe- haviortheycollectivelygenerate[1–5].Inconjunctionwithmorethoroughbiochemicalmeasurements, theseinteractiondatayieldmechanisticstatementsrangingfromlessdetailed,asin“aphosphoepitopeof EGFRbindsstronglytotheSH2/PTBdomainsofGrb2,Nck1,PI3KαandweaklytotheSH2domains ofGrb10,Grb7,Nck2,Shp1”,tomoredetailed,asin“axin1bindsaregioninthearmadillorepeatofβ-

2 catenin,ifβ-cateninisunphosphorylatedatcertainN-terminalresidues.”Unlikestructuralandgenomic datatypes(“molecularnouns”),interactionfragmentsofthiskind(“molecularverbs”)arefundamentally aboutprocess,andtheirbroadermeaningresidesinthedynamicbehaviorofthelargenetworksthey generate. High-throughputassays,suchasyeasttwo-hybrid(Y2H),typicallyprobeforpairwisebindingbetween proteinsinahighlyimpoverishedcontext,lackingexcludedvolumeandothereffectsthatmightinfluence interactionswhentheproteinstestedareboundtomultipleothers[2,6].Interactiondataofthiskindare oftenrenderedasalargegraphinwhichnodesrepresentproteinsandedgescorrespondtopairwisebinding interactionsreportedbytheassay.Thesegraphshavebeenshowntopossessstatisticalproperties,such asbow-tiestructure[7,8],approximatelyscale-freedegreedistributions[9]andsmall-worldcharacteristics [10].Yet,unlikeroadnetworks,theedgesinPPInetworksdonotrepresentpersistentphysicalconnections betweennodes,butrathersummarizeinteractionpossibilitiesthatmustberealizedthroughphysical bindingevents.Thecumulativeeffectofsucheventsresultsinadistributionofproteincomplexesthat ultimatelydeterminescellularbehavior.SignificantpropertiesofPPInetworksmaythereforebecome apparentonlybystudyingthebehaviortheyinduceinapopulationofproteins,whichrequiresthe developmentandanalysisofdynamicmodels. wi s t i h t   e c   o gr n a fl p ic h ts ? ? site graph without conflicts g p r l a ai p n h   molecular species Figure1.Bindingsurfacesandcomplexformation. Center:Thetraditionalplaingraph representationofaPPInetworkrepresentsthebindingcapabilitiesofahubprotein(red)through severalincidentedges.Thediversityofmolecularspeciesgeneratedbythesepotentialinteractions dependsontheextenttowhichtheycompeteforbindingsurfaces(whitecircles),towhichwereferas “sites”.Theseconflictsarebestrepresentedasa“sitegraph”,derivedfromadomain-levelresolutionof protein-proteininteractions.Wedepicttwoextremecases.Top:Allinteractionpartnerscompetefor thesamesite.Bottom:Allinteractionsoccuratdifferentsitesandaremutuallycompatible.Inthe languagewedeploytorepresentprocessesbasedonprotein-proteininteractions,asitedenotesa distinctinteractioncapability.Acomparisonbetweenthescenariosdepictedatthetopandthebottom illustrateshowcombinatorialcomplexityisaffectedbybindingconflicts.