Integrative modelling of cellular assemblies

Integrative

modelling

of

cellular

assemblies

Agnel

Praveen

Joseph

,

Guido

Polles

,

Frank

Alber

and

Maya

Topf

Awidevarietyofexperimentaltechniquescanbeusedfor

understandingtheprecisemolecularmechanismsunderlying

theactivitiesofcellularassemblies.Theinherentlimitationsofa

singleexperimentaltechniqueoftenrequiresintegrationofdata

fromcomplementaryapproachestogainsufficientinsightsinto

theassemblystructureandfunction.Here,wereviewpopular

computationalapproachesforintegrativemodellingofcellular

assemblies,includingproteincomplexesandgenomic

assemblies.Weproviderecentexamplesofintegrativemodels

generatedforsuchassembliesbydifferentexperimental

techniques,especiallyincludingdatafrom3Delectron

microscopy(3D-EM)andchromosomeconformationcapture

experiments,respectively.Wehighlightgeneralconceptsin

integrativemodellinganddiscusstheneedforcareful

formulationandmergingofdifferenttypesofinformation.

Addresses

1_{InstituteofStructuralandMolecularBiology,DepartmentofBiological} Sciences,BirkbeckCollege,UniversityofLondon,MaletStreet,London WC1E7HX,UnitedKingdom

2_{MolecularandComputationalBiology,DepartmentofBiological} Sciences,UniversityofSouthernCalifornia,1050ChildsWay,Los Angeles,CA90089,UnitedStates

Correspondingauthors:Alber,Frank([email protected]), Topf,Maya([email protected])

CurrentOpinioninStructuralBiology2017,46:102–109 ThisreviewcomesfromathemedissueonBiophysicalmethods EditedbyCarolRobinsonandCarlaSchmidt

http://dx.doi.org/10.1016/j.sbi.2017.07.001 0959-440/ã2017ElsevierLtd.Allrightsreserved.

Introduction

Understandingthestructure anddynamicsof the inter-actingbiomoleculescangivegreatinsightsintothecell’s functionand integrity.Thesemacromolecules are orga-nizedatdifferentlevels,fromsmallassemblies(involving 2–3 proteins) to very large ones (tens to hundreds of proteins), some of which are organized further, at the supramolecularlevel:forexample,theassemblyof ribo-somesintopolysomes,theassemblyofviralparticles,and onanevenlargerscaletheorganization ofchromosome structuresandentire organellessuchasthenucleome.

Unfortunately,structure determination of assemblies, in particularlargeones,atresolutionsthatenablemeaningful functioninferenceisnotalwaysviableusingasingle bio-physicaltechnique.Thisproblemcanbeovercomeifthe informationfromseveraltechniques(e.g.,assemblysubunit composition,topologyandoverallarchitectureand dynam-ics)iscombinedefficientlyusingavarietyofcomputational methods.Integrativemodellingbasedoncomplementary experimentaldataisincreasinglybeingusedasthestrategy tocomputestructuralmodelsforlargecellularassemblies. Multiplesourcesofstructuralinformationcanreducethe ambiguityandnoiseassociatedwithlow-resolutionand/or heterogeneousdata. Inthisreview, we focusonpopular experimentaltechniquesandcomputationalmethodsused inintegrativemodellingofcellularassemblies.

Sources

of

spatial

information

High-resolution structures of cellular assemblies have beentraditionallycharacterizedinvitrobyX-ray crystal-lography, the success of which is often limited by the abilitytoformcrystals(Figure 1).Electroncryo micros-copy(cryoEM)techniqueshavebeengenerallyusedto determinethestructuresoflargeassemblies(above
150 to200kD)thatcouldnotbecrystallised(withthe advan-tageofbeingcarriedoutatnear-nativeconditions)though atsubstantiallylowerresolutions.However,recent devel-opments (including direct electron detectors and advanced image processing methods) have brought a substantial increase in subnanometer and near-atomic resolutionstructures,evenofsmallersizemolecules[1]. Althoughatmuchlowerresolutions,electroncryo tomog-raphy (cryo ET) allows in situ visualization of cellular architecturesaswellasassemblystructurestheycontain, ifsubtomogram averaging ispossible [2].Using fluores-cently labelled biomolecules, dynamic interactions of assemblies can also bestudied in living cells by super-resolutionmicroscopy,atresolutionsoftheorderof10nm. Small-angle X-ray and neutron scattering (SAXS and SANS)haveprovenusefulinthestructural characteriza-tionofassembliesundernear-nativesolutionconditions, providinginformation onpairwise electron (or nuclear) distances,which canbe usedfor computing 3D shapes and related features [3]. Nuclear Magnetic Resonance (NMR) is typically limited by sample size, but for large assemblies it can provide information such as directinteractionsbetweenmonomersandrelative orien-tations [4]. Electron Paramagnetic Resonance (EPR) allowsdetermination of distance distributions(typically

Figure1

Protein Complexes

Genome Assemblies

Common Experimental

methods

Type of information

Full atomic / partial atomic chromatin fibers / domains

Shape information

Representation

Pairwies contact information

Absolute and relative Positioning of selected loci

3C 3D-FISH Cryo ET Lamina DamID Localization affinity Chip-Seq Cryo SXT Live fluorescence-imaging 4C HiC TCC Single-Cell HiC Chip-Seq Scoring function

Clustering & Model analysis Sampling / Optimization Coordinate model

Orientational information

Interaction data and distance restraints XLMS NOEs, PREs FRET Proteomics EPR Co-evolution 10 nm Crystallography 3D-EM NMR NMR RDC HDX (NMR,MS) Ion mobility MS Mutagensis Chemical footprinting 3D EM SAXS/SANS

Model

Generation

Pleurotolysin 40S-elF1-elF3 Inflammasome

Chromosome Genome

Atomic, domain, protein, shape chromatin fibres, partial models

Restraints: experimental,geometric, physico-chemical

Exhaustive, random,MD,MC,GA

Single solution, ensemble solutions

Current Opinion in Structural Biology

(Top)Examplesofdifferenttypesofinformationfromvariousexperimentalsourcesthatareintegratedforstructuredeterminationofprotein complexesandgenomeassemblies.Theimportantstepsinvolvedinintegrativemodellingprocessarehighlightedinthecentralpanel(model generation).(Bottom)Examplesofrecentlydeterminedintegrativemodelsofcellularassembliesacrossresolutionandsizescales(from subnanometerto
10micronrange).Fromlefttoright:transcriptionrepressivecomplexCLOCK:BMAL1(brainandmuscleArnt-likeprotein1) boundtoCRY1(cryptochrome-1)(adaptedfrom[78]),atomicmodelbuiltbasedondatafromSAXS,NMR,SizeExclusionChromatographyand X-raycrystallography;structureofthefungaltoxinPleurotolysin(adaptedfrom[38]),modelbuiltbasedon11A˚ resolutioncryoEMmapandX-ray crystallography;modelof40S-eIF1-eIF3translationinitiationcomplexbuiltusingdatafromXL-MSandX-raycrystallography(
₂₅A˚ negativestain EMmapusedforvalidation)(adaptedfrom[9]);modelofflagellin-inducedNAIP5/NLRC4inflammasomebuiltbasedondatafrom4nmresolution subtomogramaverageandX-raycrystallography(adaptedfrom[79]);EnsembleofmouseXchromosomeconformationsfromsinglecellHiCat 500kbresolution(adaptedfrom[74]);exampleofagenomemodelforhaploidmouseembryonicstemcellsfromHiCexperimentsusingchain particlesrepresenting100kbDNA(adaptedfrom[75]).

1–10nm) that are generally more precise compared to fluorescencebasedmethods[5].

MassSpectrometry(MS) approacheshavebeen usedto obtainspatialinformation,stoichiometryand connectiv-ityonentireassembliesinthegasphase(assumingthey maintaintheirinteractions)withtheadvantageofdealing with limited sample amounts.Advances in protein and peptideseparationtechniquesin MS makethemethod toleranttoahighdegreeofsampleheterogeneity[6]and samplesinvolvingsmallproteinmixturesandevenlarge viralassemblies canbe characterized. Ion mobility MS (IM-MS) allows separation of coexisting forms of the same complex. The time taken by an ion to traverse theion mobility cellis related to its mass, charge, and theirrotationallyaveragedcollisioncross-section(which givesameasureoftheoverallshape)[7].Chemical cross-linkingcoupledtoMS(XL-MS)isverypopularasitcan reveal interactions within or between biomolecules by providinganupper-limitdistancebetweenspecific resi-dues[8].Anumberofmethodshavebeenusedto calcu-latetheoptimalcross-linkdistancesonagivenatomistic model and score these against XL-MS data [9–12]. Hydrogen-Deuterium Exchange (HDX) experiments combinedwith eitherNMR or MS provideinformation onbiomolecularinteractionsurfacesandconformational changes[4,13].

Proteininteractiondatacanalsobeinferredusing evolu-tionaryapproaches.Recentadvancesin thedetectionof correlatedmutationsin sequencesacross speciesenable predictionof residuepairs thatinteract notonly within butalsobetweenproteins[14].Experimentalinteraction datacanbecombinedwithevolutionaryinformation,by mappingknowninteractions[15–17]andthe3Dstructure oftherelatedcomplex,ifavailable[18,19].

Informationaboutthenucleomeorganizationcomesfrom recenttechnologies.Forexample,Chromosome Confor-mation Capture (3C) and related technologies(4C, 5C, HiC,insituHiC,TCC)detectthegenome-wide frequen-ciesofspatialco-locationbetweennon-consecutive geno-mic regions in a population of cells [20]. Mapping of chromatin interactions is also possible in single cells, although at relatively lower coverage in comparison to ensemble HiC [21]. Lamina-DamID (DNA adenine methyltransferase identification) experiments provide probabilitieswith which chromatin regionsare exposed tothelamina atthenuclearenvelope.

Multi-colorfluorescenceinsituhybridization(FISH)uses fluorescent probes to detect the positions of targeted genomiclociin single cells; this decadesoldtechnique has lately witnessed impressive improvements in the number ofsimultaneous target loci, making it possible totraceentirechromosomes [22].Finally,imaging tech-nologiessuchassoftX-raycryotomography(cryoSXT)

provideinsightsintospatialdistributionsofchromatinin differentfunctionalstates[23],whilecryoETcanreveal folding patterns of individual nucleosomal chromatin fibers.

Approaches

to

integrative

modelling

Itremains a major challenge to develop computational methods that can generate reliable models of cellular assemblies, ranging from small protein complexes to structuralmapsofentireorganellessuchasthenucleome. Belowwedescribemodellingapproachesdrivenby inte-grationofdifferenttypesofexperimentaldata(Figure1).

Proteincomplexes

Proteinsandtheirassembliesareusuallycharacterizedby one or a few stable observable conformations under specificexperimentalconditions(exceptloopsand disor-deredregions). Atypical strategyfor integrative model-ling of protein assemblies (including domain–domain, protein–protein,protein–RNA or protein–DNA interac-tions) relies on minimizing a scoring function, which measures the agreement between the model and the experimentaldata(aswellas penalizingviolationsfrom known standard geometries).The modelcan be repre-sentedasfull-atomic,coarse-grained(e.g.secondary struc-ture elements, domains, whole protein), or partial (i.e. some parts of the model are missing). Experimental information is typically represented in the form of restraints that help in reducing the sampling space. Thereliability ofindividual experimentalrestraintscan be considered, for example, by adding weights in the scoringfunctionorbyselectivelyusingsubsetsofthemat differentstagesoftheoptimization[24,25].Alternatively, severalinitialmodelsmaybegeneratedbyusingonlya fractionofrestraints(randomsubset)eachtime,thesize ofthefractionbeingacrudeindicatorofthereliabilityof restraints[26].Integrativeapproachesyieldamodeloran ensemble of models that will have minimal restraints violations.The generated models canbe clustered and rankedbasedonascoreor aconsensusamong multiple scoresand/orsizeoftheclusters[27].Forcrossvalidation, partoftheexperimentaldatacanbeexcludedduringthe modellingprocess.

Aclassicalexampleofintegrativemodellingistoobtain anatomicmodelfromcryoEMdatabyfittingindividual assembly components into the 3D map. This usually involves aglobalsearch to getthebestfit betweenthe model/sandthemap,forwhichmanyprogramshavebeen developed [7,28],suchas the Situs package (whichcan also fit models to SAXS data) [29]. Geometric features from the 3D-EM map can also be used as restraints, instead of directly fitting the subunit models into the EMdensity[30,31].Interactivefittingofatomicmodelsis popular,and is enabledbyvisualization tools likeCoot [32] and Chimera [33]. Chimera provides methods for fitting atomic models in 3D shapes from cryo EM/ET

throughbothglobalandlocalsearchesinauser-friendly and interactivemanner.

TEMPy is a python package for integrative modelling primarilybasedonEMdensity[27](butalsocrosslinking data). Simultaneous fitting of multiple components is carriedoutbyageneticalgorithm,whichusesthemutual informationto accountforthegoodness-of-fitand pena-lizes for steric clashes [34]. This is followed by a real-space refinement in the density using MODELLER/ Flex-EM [35,36]. TEMPy integrates various scoring functions, including correlation-based scores, surface based-scoresandstatisticalscores.Inarecentapplication, low-resolutiondensityfromsubtomogramaveragingwas combined with chemical cross-linking to identify the oligomerizationstateoftheHSV-1glycoproteinB assem-bly in vesicle membranes. Multiple methods of hierar-chical constrained density-fitting were employed for building atomic models for the pre- and post-fusion conformations [37]. Models weregenerated and scored byTEMPyusinganensembleofalternateconformations andvalidatedusinginsertionmutationaldata,revealinga surprisingpre-fusionconformation.Integrativemodelling usingacombinationofdensityfittingwithother experi-mental data has also been useful in the intermediate resolution range[38,39].

Another relatedMonte-Carlo basedmethod—PyRy3D [40]—usesascoringfunctionbasedonacombinationof restraints, fitness to the shape data (EM, SAXS) and penalties for steric clashes. This method was recently used to model the structure of CCR4-NOT complex, based onEMdataand componentinteraction restraints from X-ray and biochemical experiments [41]. POWER employsparticleswarmoptimizationtopredictthe com-ponentarrangementinasymmetricassembly,basedona few geometric or distance restraints [42]. The program uses ensembles of subunits from molecular dynamics simulations or experiments to explore conformational changesofsubunitsduringassemblyoptimization.Using thismethod,intrinsicflexibilityderivedfromX-ray struc-turesofsubunitswasintegratedwithcryoEMdataofthe assembly, tobuild near-atomisticmodelsofaerolysinat differentstagesof poreformation[43].

A few integrative methods are also developed to work primarily onSAXS data[44]. For example,the ATSAS suiteprovidesacollectionofmethodsforprocessingand analysisofbothSAXSandSANSdata,includingtoolsfor integrative modelling and flexible optimization [45]. SAXS,likeEM,isoftenusedincombinationwithother techniquesforintegrativemodelling.Recently,restraints based on data from SAXS, HDX and RNA SHAPE (selective 20-hydroxyl acylation analysed by primer ex-tension) were used to provide a dynamic model of a machinery involved in HIV-1 pro-viral transcription [46]. The SHAPE data highlights RNA segments that

arepotentially involvedin interactionbasedonchanges in reactivityofnucleotides uponassembly.

The Rosetta software suite also uses a Monte-Carlo approachforconformationalsampling,whichrecombines frequentlyoccurringproteinbackbonestructuralfeatures with different types of restraints, such as sparse NMR databasedonNuclearOverhauserEffect(NOE), chemi-calshift,andResidualDipolarCoupling(RDC,as orien-tationrestraint)aswellasXL-MSandcryoEM[25].The reliabilityofNMR restraintsisconsideredbytheir rela-tivecontributionsinthescoringfunction.Distance con-straintscanbeenforced(e.g.,[47])as partofthescoring function involving statistical interaction potentials and evaluation of standard geometry (atoms and secondary structures) and clashes. Ina recentstructure of Shigella flexneritype III secretion system(T3SS) needle several solid-stateNMR(ssNMR)distancerestraintswereused tobuildmodelswitha7.7A˚ cryoEMdensitymap[48]. Intraandinter-subunitdistanceconstraintsfromssNMR experiment were integrated in iterative modelling and thefinalmodelswereidentifiedusingaweightedsumof theRosettascoringfunction,numberofconstraint viola-tionsandcorrelation withEMdensity.

In Haddock, sparseexperimental data canbeadded as ambiguousinteractionrestraintsandrandomsetsofthese are used to generate initial configurations. Rigid-body protein–proteindockingandenergyminimizationis fol-lowedbytheHaddockflexiblerefinement,withan addi-tional density cross-correlation score added to non-bonded interactionterms, for integrating EMdata[49]. For example,chemicalshift perturbationswereusedas restraintstomapthepotentialactive siteandmodelthe interaction between bacterial outer membrane receptor FusAandhostcellferredoxinusingacomparativemodel of FusAandcrystalstructuresofferredoxin[50]. InIMP,assemblycomponentscanberepresentedassets ofparticlesofdifferenttypes,dependingontheavailable information and structural resolution. Such representa-tionscanbeusefulwhensomehigh-resolution informa-tionismissing(e.g.,therearenoatomicmodelsforsomeof theproteins/domainsinthecomplex).Thesecomponents canbe assembled usingdifferentoptimization schemes (such as Monte Carlo, Conjugate Gradients, Molecular dynamics)and manydifferenttypesofspatialrestraints [24],suchasconnectivityandinteractionrestraintsfrom variousMStechniques[51].AnotableexampleofanIMP application is a model of the Yeast Mediator complex (YMC), which performs an essential regulatory role in eukaryotic transcription initiation [52]. Regions of known atomic structure or comparative models were modelledasrigidbodies,whereasunknownregionswere represented as aflexible string of beads. Positions and orientationsof rigid andpolymer beadswereiteratively sampledtominimizeascoringfunctionwhichaccounted

for excluded volume, sequence connectivity, cryoEM, and crosslinking restraints. A Bayesian framework was usedtoassignconfidencetosubsetsofcross-linkingdata basedonrelativeconsistency[9].Finally,asinglecluster withthebestaveragescore waschosen.Themodelwas successfully validated with independent experimental dataonprotein–proteininteractions.XL-MOD[53]also uses a Bayesian framework to reweight conflicting or ambiguousdistancerestraintsfromXL-MS,represented as log-harmonic potentials. An elastic network model based onacoarse-grained representationof thesubunit structuresisusedtosampleconformationalchangesand theirpositionsandorientationsaresampledbya Monte-Carlooptimizationscheme followedbyrelaxationusing moleculardynamics.

Genomeassemblies

Acommonchallengeinresolvingstructuresofgenomesis the wide range of spatial and temporalscales involved compared to protein assemblies: the human genome counts
6 billion basepairs in a diploid cell and its structurearguablyneverreachesthermodynamic equilib-rium[54].Thesecomplexitiesdemandalevelofcoarse graining, ranging from models of the 10nm chromatin fibertochromatindomains.Chromatincanbesegmented intoself-interactingchromatindomains(e.g.topologically associateddomains(TAD)[55]orcontactdomains[56]), oftenbordered bychromatinloops.Chromatin domains withsimilarfunctionalpropertiesformsubcompartments and chromosomes organizein territories with stochastic preferencesintheirnuclearlocations.

Recent physics-based polymer simulations of chromo-somalregionsrevealedpotentialmechanismsinto chro-matin loop and TAD domain formation [57–60]. How-ever,ourfocuswillbebasedondata-driven(or restrain-based)approaches.Thosemethodsuseexperimentaldata (mostlyfromHiCexperiments)asinputinformationfor generatinggenomeassemblystructures.The interpreta-tionofthedataanditsuse differswidelydependingon thechosenapproach.

Someschemesgenerateasinglerepresentativestructure (aconsensus model) fromensemble HiC data. Contact frequenciesareusuallymappedtospatialdistances,used togeneratea3Dstructurebyoptimizingascoring func-tion [61], Bayesian inference (BACH) [62] or multidi-mensional scaling (ShRec3D) [63]. These consensus models represent data averaged over millions of cells and cannot describe the actualphysical nature of indi-vidual genomes, including the considerable structural variabilitybetweencells observedin 3DFISH and sin-gle-cellHiCexperiments[21].

Incontrast, resamplingapproachesperformmany inde-pendentoptimizationsof asingle scoringfunctionfrom randomstartingconfigurationstoresample anensemble

ofstructures.InTADBit,modelsarecalculatedbyIMP, followedbyaclusteranalysisoftheensemble[64].Ithas beenusedtoinvestigatestructuresofTADsandbacterial genomes[65].Resamplingisalsoappliedinseveralother examples(reviewedin[66]).Chrom3Dreliesoncontact restraintsforthemostsignificantHiCcontactsandlamina DamIDdata[67].

Population-basedmodelling(PM)techniquesdiffer con-ceptuallyfromresamplingones,in thattheyattempt to de-convolve ensemble HiC data into a population of individualstructures. Thesemethods explicitly address thevariability of genomestructures by creating alarge population of models so that the cumulated chromatin contacts of all the structures reconstitutethe ensemble HiC data rather than each structure individually. Such approaches avoid unphysical structures from simulta-neous enforcementof conflicting restraints. One of the first HiC-based PM methods for modelling complete diploid genomes was introduced by Kalhor et al. [68] and refinedin a recentpublication [69](PGS software:

https://www.github.com/alberlab/PGS). PGS has been

applied tocomplete diploidgenomes of human, mouse andDrosophilamelanogastorcells [69,70].

Other PMapproaches use amaximum entropymethod combinedwithmoleculardynamicssamplingtoconstruct effectiveenergylandscapesthatreproduced experimen-talHiCmapsofindividualchromosomes[71].Inanother example,chromatinwasrepresentedbyafewfunctional states and chromosome models were generated using chromatinstatebinding affinitiesas parameters[72].In anearliermethodapolymermodelcombinedwithMonte Carlo sampling was usedto studychromatin conforma-tionswithinTADsfromensemble5C data[73]. Recently, the development of single cell HiC assays enabled the modelling of genomes of individual cells

[21,74,75].InStevensetal.,10individualgenome

struc-turesofhaploidmouseembryonicstemcellswere mod-elledfromsinglecellHiCdataat100kb resolutionand validatedbyfluorescenceimaging[75].

Achallengeremainsin theanalysisofstructure popula-tions,eithergeneratedfromdeconvolutionorsinglecell modelling.Daietal.addressedthischallengeby identi-fyingfrequentlyoccurringchromatinclusters[76],which are often enriched in binding of specific regulatory factors.

Concluding

paragraph

With the developments in biophysical techniques for structuredeterminationof cellularassemblies,theneed tointegratethesedataforsolvingabiologicalproblemis becomingincreasinglyevident.Manyof theapproaches discussed in thisreview reflect recentadvancements in thisfieldsofintegrativemodellingofproteinandgenome

assemblies.Althoughprovidingmodelsofthetwotypes ofassembliesoftenrequiresdifferentrepresentationsand differentexperimentalinformation overall,the method-ology usedfor dataintegration hasmany overlaps.The datausedinintegrative modellingisoftensparse,noisy and ambiguous.Therefore,it isimportant to choose an adequaterepresentationandformulationoftherestraints (and their relative weights) to represent the data as accurately as possible. Thorough sampling is another important requirement to ascertain that optimal or near-optimal models are generated. The final model(s) shouldalsoreflectuncertaintyandcompletenessofinput information, apart from providing relevant structural detailsoftheassembly.Cross-validationagainstan inde-pendent set of data is recommended for model assess-ment and the detection of over-fitting. Finally, large conformational variability in cellular assemblies often affect the consistency between different experimental data and this requires generation of an ensemble of modelstomoreaccuratelyrepresentthedata.New com-munity effort aims ataddressing issues related to stan-dardizationofrepresentation,validationandarchivingof integrative models[77].

Conflict

of

interest

The authorshavenoconflictofinterestto declare.

Acknowledgments

WethankthesupportfromtheMRCprojectgrantMR/M019292/1(MT) andtheArnoldandMabelBeckmanfoundation(BYIprogram),NIH U54DK107981,R01GM096089,NSFCAREER(1150287)(FA).

References

and

recommended

reading

Papersofparticularinterest,publishedwithintheperiodofreview, havebeenhighlightedas:

 ofspecialinterest ofoutstandinginterest

1. KuhlbrandtW:Biochemistry.Theresolutionrevolution.Science 2014,343:1443-1444.

2. BeckM,BaumeisterW:Cryo-electrontomography:canitreveal themolecularsociologyofcellsinatomicdetail? TrendsCell Biol2016,26:825-837.

3. TrewhellaJ:Small-anglescatteringand3Dstructure interpretation.CurrOpinStructBiol2016,40:1-7.

4. vanIngenH,BonvinAM:Information-drivenmodelingoflarge macromolecularassembliesusingNMRdata.JMagnReson 2014,241:103-114.

5. SahuID,McCarrickRM,LoriganGA:Useofelectron paramagneticresonancetosolvebiochemicalproblems. Biochemistry2013,52:5967-5984.

6. LosslP,vandeWaterbeemdM,HeckAJ:Thediverseand expandingroleofmassspectrometryinstructuraland molecularbiology.EMBOJ2016,35:2634-2657. 7. ThalassinosK,PanduranganAP,XuM,AlberF,TopfM:

Conformationalstatesofmacromolecularassemblies exploredbyintegrativestructurecalculation.Structure2013, 21:1500-1508.

8. FainiM,StengelF,AebersoldR:Theevolvingcontributionof massspectrometrytointegrativestructuralbiology.JAmSoc MassSpectrom2016,27:966-974.

9. ErzbergerJP,StengelF,PellarinR,ZhangS,SchaeferT, AylettCH,CimermancicP,BoehringerD,SaliA,AebersoldRetal.: Moleculararchitectureofthe40SeIF1eIF3translationinitiation complex.Cell2014,158:1123-1135.

10. KahramanA,HerzogF,LeitnerA,RosenbergerG,AebersoldR, MalmstromL:Cross-linkguidedmolecularmodelingwith ROSETTA.PLoSONE2013,8:e73411.

11. BullockJMA,SchwabJ,ThalassinosK,TopfM:Theimportance ofnon-accessiblecrosslinksandsolventaccessiblesurface distanceinmodellingproteinswithrestraintsfrom

crosslinkingmassspectrometry.MolCellProteomics2016, 9:2491-2500.

12. KahramanA,MalmstromL,AebersoldR:Xwalk:computingand visualizingdistancesincross-linkingexperiments.

Bioinformatics2011,27:2163-2164.

13. HarrisonRA,EngenJR:Conformationalinsightinto multi-proteinsignalingassembliesbyhydrogen-deuterium exchangemassspectrometry.CurrOpinStructBiol2016, 41:187-193.

14. HopfTA,ScharfeCP,RodriguesJP,GreenAG,KohlbacherO, SanderC,BonvinAM,MarksDS:Sequenceco-evolutiongives 3Dcontactsandstructuresofproteincomplexes.Elife2014,3. 15. AshfordP,HernandezA,GrecoTM,BuchA,SodeikB,CristeaIM,

GrunewaldK,ShepherdA,TopfM:HVint:astrategyfor identifyingnovelprotein–proteininteractionsinHerpes SimplexVirusType1.MolCellProteomics2016,15:2939-2953. 16. SeguraJ,Sanchez-GarciaR,Tabas-MadridD,Cuenca-AlbaJ,

SorzanoCO,CarazoJM:3DIANA:3Ddomaininteraction analysis:atoolboxforquaternarystructuremodeling.Biophys J2016,110:766-775.

17. TsujiT,YodaT,ShiraiT:Decipheringsupramolecularstructures withprotein–proteininteractionnetworkmodeling.SciRep 2015,5:16341.

18. MoscaR,CeolA,AloyP:Interactome3D:addingstructural detailstoproteinnetworks.NatMethods2013,10:47-53. 19. KuzuG,KeskinO,NussinovR,GursoyA:PRISM-EM:template

interface-basedmodellingofmulti-proteincomplexesguided bycryo-electronmicroscopydensitymaps.ActaCrystallogrD StructBiol2016,72:1137-1148.

20. DenkerA,DeLaatW:Theseconddecadeof3Ctechnologies: detailedinsightsintonuclearorganization.ColdSpringHarbor LaboratoryPress;2016:1357-1382.

21. NaganoT,LublingY,StevensTJ,SchoenfelderS,YaffeE, DeanW,LaueED,TanayA,FraserP:Single-cellHi-Creveals cell-to-cellvariabilityinchromosomestructure.Nature2013, 502:59-64.

22. WangS,SuJH,BeliveauBJ,BintuB,MoffittJR,WuCT,ZhuangX: Spatialorganizationofchromatindomainsandcompartments insinglechromosomes.Science2016,353:598-602.

23. LeGrosMA,ClowneyEJ,MagklaraA,YenA, Markenscoff-PapadimitriouE,ColquittB,MyllysM,KellisM,LomvardasS, LarabellCA:SoftX-raytomographyrevealsgradualchromatin compactionandreorganizationduringneurogenesisinvivo. CellRep2016,17:2125-2136.

24. RusselD,LaskerK,WebbB,Velazquez-MurielJ,TjioeE, Schneidman-DuhovnyD,PetersonB,SaliA:Puttingthepieces together:integrativemodelingplatformsoftwareforstructure determinationofmacromolecularassemblies.PLoSBiol2012, 10:e1001244.

25. Leaver-FayA,TykaM,LewisSM,LangeOF,ThompsonJ, JacakR,KaufmanK,RenfrewPD,SmithCA,ShefflerWetal.: ROSETTA3:anobject-orientedsoftwaresuiteforthe simulationanddesignofmacromolecules.MethodsEnzymol 2011,487:545-574.

26. vanZundertGC,RodriguesJP,TrelletM,SchmitzC,KastritisPL, KaracaE,MelquiondAS,vanDijkM,deVriesSJ,BonvinAM:The HADDOCK2.2webserver:user-friendlyintegrativemodeling ofbiomolecularcomplexes.JMolBiol2016,428:720-725.

27. FarabellaI,VasishtanD,JosephAP,PanduranganAP,SahotaH, TopfM:TEMPy:apythonlibraryforassessmentof three-dimensionalelectronmicroscopydensityfits.JAppl Crystallogr2015,48:1314-1323.

28. VillaE,LaskerK:Findingtherightfit:chiselingstructuresoutof cryo-electronmicroscopymaps.CurrOpinStructBiol2014, 25:118-125.

29. WriggersW:ConventionsandworkflowsforusingSitus.Acta CrystallogrD:BiolCrystallogr2012,68:344-351.

30. CamposM,FranceticO,NilgesM:Modelingpilusstructures fromsparsedata.JStructBiol2011,173:436-444.

31. FleishmanSJ,HarringtonS,FriesnerRA,HonigB,Ben-TalN:An automaticmethodforpredictingtransmembraneprotein structuresusingcryo-EMandevolutionarydata.BiophysJ 2004,87:3448-3459.

32. EmsleyP,LohkampB,ScottWG,CowtanK:Featuresand developmentofcoot.ActaCrystallogrD:BiolCrystallogr2010, 66:486-501.

33. PettersenEF,GoddardTD,HuangCC,CouchGS,GreenblattDM, MengEC,FerrinTE:UCSFChimera–avisualizationsystemfor exploratoryresearchandanalysis.JComputChem2004, 25:1605-1612.

34. PanduranganAP,VasishtanD,AlberF,TopfM:gamma-TEMPy: simultaneousfittingofcomponentsin3D-EMmapsoftheir assemblyusingageneticalgorithm.Structure2015, 23:2365-2376.

35. JosephAP,MalhotraS,BurnleyT,WoodC,ClareDK,WinnM, TopfM:RefinementofatomicmodelsinhighresolutionEM reconstructionsusingFlex-EMandlocalassessment. Methods2016,100:42-49.

36. TopfM,LaskerK,WebbB,WolfsonH,ChiuW,SaliA:Protein structurefittingandrefinementguidedbycryo-EMdensity. Structure2008,16:295-307.

37. Zeev-Ben-MordehaiT,VasishtanD,HernandezDuranA, VollmerB,WhiteP,PrasadPanduranganA,SiebertCA,TopfM, GrunewaldK:Twodistincttrimericconformationsofnatively membrane-anchoredfull-lengthherpessimplexvirus 1glycoproteinB.ProcNatlAcadSciUSA2016,113:4176-4181. 38.

 LukoyanovaCaradoc-DaviesN,KondosTT,SpicerSC,BA,FarabellaKleifeldI,O,LawTraoreRH,ReboulDA,EkkelCF,SM etal.:Conformationalchangesduringporeformationbythe perforin-relatedproteinpleurotolysin.PLoSBiol2015,13: e1002049.

Themechanismofactionofapore-forming proteinfromtheMACPF superfamilyisshownforthefirsttimebasedondatafromX-ray crystal-lography, cryo electron microscopy, fluorescence spectroscopy and cross-linking experiments combined with molecular modelling and flexiblefittingwithTEMPyandMODELLER/Flex-EM.

39.

 PlaschkaTegunovD,C,PetrotchenkoLariviereL,WenzeckEV,BorchersL,SeizlCH,M,BaumeisterHemannM,W, HerzogFetal.:ArchitectureoftheRNApolymeraseII-Mediator coreinitiationcomplex.Nature2015,518:376-380.

UsingcryoEMmapsatintermediateresolutionsanddistancerestraints fromXL-MSdata,theauthorsdeterminethestructureofRNApolymerase II—mediatorcoreinitiationcomplex.

40. KasprzakJM,Dobrych_łopM,BujnickiJ.PyRy3D:http:// genesilico.pl/pyry3d.

41. UklejaM,CuellarJ,SiwaszekA,KasprzakJM, Czarnocki-CieciuraM,BujnickiJM,DziembowskiA,ValpuestaJM:The architectureoftheSchizosaccharomycespombeCCR4-NOT complex.NatCommun2016,7:10433.

42. DegiacomiMT,DalPeraroM:Macromolecularsymmetric assemblypredictionusingswarmintelligencedynamic modeling.Structure2013,21:1097-1106.

43. DegiacomiMT,IacovacheI,PernotL,ChamiM,KudryashevM, StahlbergH,vanderGootFG,DalPeraroM:Molecularassembly oftheaerolysinporerevealsaswirlingmembrane-insertion mechanism.NatChemBiol2013,9:623-629.

44. GraewertMA,SvergunDI:Impactandprogressinsmalland wideangleX-rayscattering(SAXSandWAXS).CurrOpinStruct Biol2013,23:748-754.

45. PetoukhovMV,FrankeD,ShkumatovAV,TriaG,KikhneyAG, GajdaM,GorbaC,MertensHD,KonarevPV,SvergunDI:New developmentsintheATSASprogrampackageforsmall-angle scatteringdataanalysis.JApplCrystallogr2012,45:342-350. 46. Schulze-GahmenU,EcheverriaI,StjepanovicG,BaiY,LuH,

Schneidman-DuhovnyD,DoudnaJA,ZhouQ,SaliA,HurleyJH: InsightsintoHIV-1proviraltranscriptionfromintegrative structureanddynamicsoftheTat:AFF4:P-TEFb:TARcomplex. Elife2016,5.

47. LosslP,KolbelK,TanzlerD,NannemannD,IhlingCH,KellerMV, SchneiderM,ZauckeF,MeilerJ,SinzA:Analysisofnidogen-1/ laminingamma1interactionbycross-linking,mass

spectrometry,andcomputationalmodelingrevealsmultiple bindingmodes.PLoSONE2014,9:e112886.

48.

 DemersBeckerS,JP,BakerHabensteinD,LangeB,A,LoquetSgourakisA,KumarNG:High-resolutionVasaS,GillerK, structureoftheShigellatype-IIIsecretionneedleby solid-stateNMRandcryo-electronmicroscopy.NatCommun2014, 5:4976.

Usingcryo-EMandssNMRrestraints,theauthorsdetermine a high-resolutionstructureoftheShigellatype-IIIsecretionneedlewithRosetta structurecalculations.

49. vanZundertGCP,BonvinAMJJ:Fastandsensitiverigid-body fittingintocryo-EMdensitymapswithPowerFit.AIMSBiophys 2015,2:73-87.

50. GrinterR,JostsI,MosbahiK,RoszakAW,CogdellRJ,BonvinAM, MilnerJJ,KellySM,ByronO,SmithBOetal.:Structureofthe bacterialplant-ferredoxinreceptorFusA.NatCommun2016, 7:13308.

51. PolitisA,SchmidtC,TjioeE,SandercockAM,LaskerK, GordiyenkoY,RusselD,SaliA,RobinsonCV:Topological modelsofheteromericproteinassembliesfrommass spectrometry:applicationtotheyeasteIF3:eIF5complex. ChemBiol2015,22:117-128.

52.

 RobinsonDavisR,BurlingamePJ,TrnkaMJ,AL,PellarinSaliA,KornbergR,GreenbergRD:MolecularCH,BushnellDA, architectureoftheyeastMediatorcomplex.Elife2015,4. Anexcellentexampleofintegrativemodelling,whereavarietyof experi-mentalinformation(includingcryoelectronmicroscopyandcrosslinking data)were usedtocharacterizethestructureof21-subunitMediator complexYeastMediatorcomplexusingIMP.

53.

 FerberSimonB,M,BouvierKosinskiG,J,BatistaOriA,RashidPR,MullerUJ,CW,Moreno-MorcilloBeckMetal.:M, Automatedstructuremodelingoflargeproteinassemblies usingcrosslinksasdistancerestraints.NatMethods2016, 13:515-520.

TheauthorsdevelopedXL-MOD,anautomatedmethodformodelling largeassembliesbyweightingandoptimizingspatialrestraintsfrom XL-MSdata,andallowingflexibilityofsubunits.

54. RosaA,EveraersR:Structureanddynamicsofinterphase chromosomes.PLoSComputBiol2008,4:e1000153. 55. DixonJR,SelvarajS,YueF,KimA,LiY,ShenY,HuM,LiuJS,

RenB:Topologicaldomainsinmammaliangenomesidentified byanalysisofchromatininteractions.Nature2012, 485:376-380.

56. RaoSSP,HuntleyMH,DurandNC,StamenovaEK,BochkovID, RobinsonJT,SanbornAL,MacholI,OmerAD,LanderESetal.:A 3Dmapofthehumangenomeatkilobaseresolutionreveals principlesofchromatinlooping.Cell2014,159:1665-1680. 57. BarbieriM,ChotaliaM,FraserJ,LavitasLM,DostieJ,PomboA,

NicodemiM:Complexityofchromatinfoldingiscapturedby thestringsandbindersswitchmodel.ProcNatlAcadSciUSA 2012,109:16173-16178.

58. BrackleyCA,JohnsonJ,KellyS,CookPR,MarenduzzoD: Simulatedbindingoftranscriptionfactorstoactiveand inactiveregionsfoldshumanchromosomesintoloops, rosettesandtopologicaldomains.NucleicAcidsRes2016, 44:3503-3512.

59. FudenbergG,ImakaevM,LuC,GoloborodkoA,AbdennurN, MirnyLA:Formationofchromosomaldomainsbyloop extrusion.CellReports2016,15:2038-2049.

60. SanbornAL,RaoSSP,HuangS-C,DurandNC,HuntleyMH, JewettAI,BochkovID,ChinnappanD,CutkoskyA,LiJetal.: Chromatinextrusionexplainskeyfeaturesofloopanddomain formationinwild-typeandengineeredgenomes.ProcNatl AcadSciUSA2015,112:201518552.

61. DuanZ,AndronescuM,SchutzK,McIlwainS,KimYJ,LeeC, ShendureJ,FieldsS,BlauCA,NobleWS:Athree-dimensional modeloftheyeastgenome.Nature2010,465:363-367. 62. HuM,DengK,QinZ,DixonJ,SelvarajS,FangJ,RenB,LiuJS:

Bayesianinferenceofspatialorganizationsofchromosomes. PLoSComputBiol2013,9:e1002893.

63. LesneA,RiposoJ,RogerP,CournacA,MozziconacciJ:3D genomereconstructionfromchromosomalcontacts.Nat Methods2014,11:1141-1143.

64. Bau` D,Marti-RenomMA:Genomestructuredeterminationvia 3C-baseddataintegrationbytheIntegrativeModeling Platform.Methods2012,58:300-306.

65. TrussartM,YusE,MartinezS,BauD,TaharaYO,PengoT, WidjajaM,KretschmerS,SwogerJ,DjordjevicSetal.:Defined chromosomestructureinthegenome-reducedbacterium Mycoplasmapneumoniae.NatCommun2017,8:14665. 66. SerraF,DiStefanoM,SpillYG,CuarteroY,GoodstadtM,Bau` D,

Marti-RenomMA:Restraint-basedthree-dimensional modelingofgenomesandgenomicdomains.FEBSLett2015, 589:2987-2995.

67. PaulsenJ,SekeljaM,OldenburgAR,BarateauA,BriandN, DelbarreE,ShahA,SørensenAL,VigourouxC,BuendiaBetal.: Chrom3D:three-dimensionalgenomemodelingfromHi-Cand nuclearlamin-genomecontacts.GenomeBiol2017,18:21. 68. KalhorR,TjongH,JayathilakaN,AlberF,ChenL:Genome

architecturesrevealedbytetheredchromosome

conformationcaptureandpopulation-basedmodeling.Nat Biotechnol2012,30:90-98.

69. TjongH,LiW,KalhorR,DaiC,HaoS,GongK,ZhouY,LiH, ZhouXJ,LeGrosMAetal.:Population-based3Dgenome

structureanalysisrevealsdrivingforcesinspatialgenome organization.ProcNatlAcadSciUSA2016,113:E1663-E1672. 70. ZhuY,GongK,DenholtzM,ChandraV,KampsMP,AlberF,

MurreC:Comprehensivecharacterizationofneutrophil genometopology.GenesDev2017,31:141-153. 71. ZhangB,WolynesPG:Topology,structures,andenergy

landscapesofhumanchromosomes.ProcNatlAcadSciUSA 2015,112:6062-6067.

72. DiPierroM,ZhangB,AidenEL,WolynesPG,OnuchicJN: Transferablemodelforchromosomearchitecture.ProcNatl AcadSciUSA2016,113:12168-12173.

73. GiorgettiL,GalupaR,NoraEP,PiolotT,LamF,DekkerJ,TianaG, HeardE:Predictivepolymermodelingrevealscoupled fluctuationsinchromosomeconformationandtranscription. Cell2014,157:950-963.

74. CarstensS,NilgesM,HabeckM:Inferentialstructure determinationofchromosomesfromsingle-cellHi-Cdata. PLoSComputBiol2016,12:e1005292.

75. StevensTJ,LandoD,BasuS,AtkinsonLP,CaoY,LeeSF,LeebM, WohlfahrtKJ,BoucherW,O’Shaughnessy-KirwanAetal.:3D structuresofindividualmammaliangenomesstudiedby single-cellHi-C.Nature2017.

76. DaiC,LiW,TjongH,HaoS,ZhouY,LiQ,ChenL,ZhuB,AlberF, JasmineZhouX:Mining3Dgenomestructurepopulations identifiesmajorfactorsgoverningthestabilityofregulatory communities.NatCommun2016,7:11549.

77. SaliA,BermanHM,SchwedeT,TrewhellaJ,KleywegtG, BurleySK,MarkleyJ,NakamuraH,AdamsP,BonvinAMetal.: OutcomeofthefirstwwPDBhybrid/integrativemethodstask forceworkshop.Structure2015,23:1156-1167.

78. MichaelAK,FribourghJL,ChelliahY,SandateCR,HuraGL, Schneidman-DuhovnyD,TripathiSM,TakahashiJS,PartchCL: Formationofarepressivecomplexinthemammalian circadianclockismediatedbythesecondarypocketofCRY1. ProcNatlAcadSciUSA2017,114:1560-1565.

79. DiebolderCA,HalffEF,KosterAJ,HuizingaEG,KoningRI: CryoelectrontomographyoftheNAIP5/NLRC4

inflammasome:implicationsforNLRactivation.Structure 2015,23:2349-2357.