Protein-Protein Interactions, Networks and Pathways - Chagall

Protein-Protein Interactions,Networks and PathwaysDoron BetelComputational Biology CenterMSKCCMay 6, 2010

Outline• Definitions• Experimental methods & Datasets• Computational Challenges• Databases• Networks• PPI reliability• Visualization• Network properties• Interaction predictions

David S. Goodsell, http://mgl.scripps.edu/people/goodsell/

Pathways• Do not exist in the cell!• A human abstraction to help organizeour understanding of biology.• A description of chronological orderingof proteins/DNA/small moleculeinteractions.• Unlike proteins and genes, pathwaysrepresent processes that may not beclearly defined.

http://www.biocarta.com/pathfiles/h_mTORPathway.asp

http://www.genome.jp/kegg/pathway/map/map00780.html

Protein Interactions• A geometric matching between two proteinssurfaces and ensuing covalent bonds. Similardefinition for protein-DNA, protein-small molecule interactions.• An obvious requirement for a PPI is the cooccurrenceof the two proteins in the sameplace at the same time.• Must be specific relative to other moleculespresent.• Interactions are governed by two opposingforces, binding affinities and thermal collisionsthat disrupt the binding.• Context is everything.

Experimental methods foranalysis of PPIShoemaker BA, Pancheko AR, PLoS CB, 2007, 3: e42

Yeast-two-hybrid• Widely used for detectingPPI.• Can detect weakinteractions (Kd~10 -7 )• Often detect spuriousinteractions.• Excludes membrane andsecreted proteins thatcannot be detected by anuclear-based system.• Some TF are self-activating.• Review - Fields S., FEBS J.,2005, 272,5391-7

IP-MS• Does not detect binaryprotein interactions butidentifies ‘complexes’.• More biological correct.In vivo conditionsconcentrations,cofactors,unmodifiedproteins.• Can use differentprotein tags and MSdetection systems.Kumar A., Snyder M., Nature 415, 123 - 4 (10 Jan 2002)

Graph TheoryVertex (node)CycleEdge-5DirectedEdge710WeightedEdge

Internet Mapping Project• ISPs• 100KNodeshttp://www.cs.bell-labs.com/who/ches/map/

Yeast Protein Linkage Map Project• proteins• 1548Nodeshttp://depts.washington.edu/sfields/yplm/data/index.html

Computational ChallengesI:Data description• The identity of the interacting proteins is notalways clear. Gene names, differentIdentifiers, alternative splicing, experimentalconstructs - a major resource expend.• Post translational modification. Not easy todescribe and can be very complex (e.g.glycosylation).• Conditions and context are often missing. Invitro experiments may lack the time/spatialcomponents of interactions.

Computational Challenges II:Data quality• Different experiments provide differentinformation: functional assays, site-directedmutagenesis, Y2H, IP-MS, IP-Western.• Different levels of details: Structuralcomplexes provide detailed atomicinformation. IP-MS provide proteincomplexes.• Incomplete coverage: not all cellularcomponents and events are covered equally.For example, membrane proteins are missedby Y2H. Impacts PPI predictions.

Computational ChallengesIII:Data availability• Most and best data is in the minds of expertscientists. Most scientists will not volunteer theirtime.– Indexers that read the literature and enter the data. Highquality records but slow and expensive.• Literature - large corpus of text published overmany years of research. Accessible but notcomputable.– There are semi-automated methods to extract informationfrom literature. Major area of text-mining research.• Datasets - obtaining data from othercomputational sources (PDB, other databases) orhigh-throughput interaction experiments.

www.pathguide.org

Types of resources• Interaction/pathway databases focus ondifferent types of data.• Species specific - Human, Yeast, Drosophila.• Interactions type specific - Protein:Protein,Protein:DNA, Protein:Ligand.• Protein specific - G-coupled receptors, TFbinding sites.• Varying qualities of data, licensing conditions,query options and updates.

Data standards• There are none currently. There areproposed standards under developmentwith growing user-base.• Most databases have their ownrepresentation of PPI and pathways.• Some use formal description languages(XML), database schemas others usesimple text files.

Data Exchange Formats• New XML standards are available toexchange data between PPI and pathwaydatabases.• PSI-MI - Proteomics Standards Initiative(psidev.sourceforge.net). Mostly for PPI,Mass spec and proteomics.– Supported by DIP, MINT, IntAct, HPRD, BIND,MIPS– IMEx - International Molecular ExchangeConsortium using PSI-MI.• BioPax - Biological Pathway Exchange(biopax.org). Broader coverage towardspathways and networks.– Supported by BioCyc, Reactome, NCIPathwayCommons, INOH,...

CellMap.org

Reactome.org

PATIKA.org

Mapping PPI to a Graph• A simple mapping– compound=node, interaction=edge• A more realistic mapping– Cell localization, cell cycle, cell type, taxonomy– Only represent physiologically relevantinteraction networks• Visualizing large datasets– Collapsing redundancies to a single node– Hierarchical organization (pathways,complexes).

Rual JF, et al. Nature, (2005), 437, 1173-8

Giot L, et al., Science, (2003), 302, 1727-36

Yeast protein complexesKrogan NJ., et al., Nature, (2006), 440, 637-643

Visualization• Cytoscape (www.cytoscape.org)• GenMapp (www.genmapp.org/)(human, mouse, rat, yeast)• Osprey (biodata.mshri.on.ca/osprey)• Patika (www.patika.org)• Generic graph viewers (Pajek,GraphViz)• Static image mappers

Many ways to draw the samethingsLe Novère et al., Nature Biotech. 27, 735 - 741 (2009)

The Systems BiologyGraphical Notation(process diagram, entity relationship diagram and activity flow diagram)Le Novère et al., Nature Biotech. 27, 735 - 741 (2009)

Cytoscape• Open source platform for visualizinginteractions and pathways.• Integrates expression and annotation tools.Can import data directly from data sources (e.g.NCBI, BioMart, IntAct, PathwayCommons).• Supports many input standards (text, GML, BioPax…).• Lots of layouts and visual styles.• Plug-in architecture that allows for addition ofnew functionalities. New plug-ins arecontinuously added.• Collaboration between ISB, UCSD, MSKCC,U of Toronto, Pasteur, Agilent. Very activedevelopment team.

GenMAPP• Application to visualize pathway files.• Overlays expression and annotationdata• Can build your own pathways – drawingtools• Contains many Human, mouse, rat,yeast pathways.• Windows only

Osprey

VisAnt

Cerami, et al. PLoS One, (2010), 5,e8918Netbox

Cerami, et al. PLoS One, (2010), 5,e8918

Cerami, et al. PLoS One, (2010), 5,e8918

Reliability of PPI• PPI interactions are identified by variousexperimental techniques with varying degreesof accuracies.• High-throughput interactions are inherently‘noisy’. Large variations betweenexperiments, sparse overlap with knowninteractions.• Many abundant ‘sticky’ proteins (e.gRibosome, chaperones).• Experimental techniques are continuouslyimproving. New datasets are more reliable.• References - vonMering et al., 2002; Sprinzak et al., 2003;Bader et al. 2002; Siato et al., 2003; Jansen et al.; 2003, Denget al.; 2003; Bader et al., 2004; Gandhi et al.,2006 and more…

How to assess reliability?• Repeated observations by multiple experimentsby different groups by different techniques.Limitation: interactions are not likely to bepublished twice, especially by the sametechnique.• Similar annotation - Similar GO Biologicalfunction (with some limitation), similar cellularcompartment.• Correlated gene expression profile.• Genetic interactions - Suppressions, syntheticlethality.• Homologous interaction - homologous interactingin other species.• Network characteristics and neighboringinteractions.

Yeast PPI landscape (2002)von Mering C., et al., Nature, 2002, 417,399-403

Human PPI (2006)Gandhi TKB. et al., Nature Genetics, 2006, 38,285-293

Network motifs• Network motifs - Patterns of connectivity thatappear in high frequencies in certain types ofnetworks.• Network motifs constitute the building blocksof networks. Can optimizefunctionalities=evolutionary selection.• For example: Feed-Forward motifs arecommon in transcriptional networks, enablingthe cells to regulate transcriptional response.

Milo R., et al., Science, 298:824-827 (2002).Network motifs

Milo R., et al., Science, 298:824-827 (2002).

Zhang LV., et al., J Biol. 2005;4(2):6Yeast PPI motifs

Network Connectivity• Networks can be characterized byspecific attributes.• Shortest paths, distribution of nodeconnectivity, clustering coefficients (C I= 2n I /k(k-1)), network motifs.• The evolution and/or design of networksis represented by these attributes.

Barabasi AL, et al., Nat Rev Genet. 2004 Feb;5(2):101-13Network Models

Predicting pathways andinteractions• Many of the feature that are used to assessreliability can be used for predictions.• Gene Fusion• Genome co-localization• Phylogenetic profiles• Correlated mutations• Gene expression• Network properties• Domain-Domain correlation

Gene Fusion

Predicting pathways andinteractions• STRING – string.embl.de– Predicted functional associations – geneneighborhood, phylogenetic profile, genefusion, coexpression experiments, textmining.• VisAnt - VisAnt.bu.edu/• GeneMania – genemania.org– Gene function prediction server.– Integrates an extensive amount of geneinformation, interaction and associations.– Flash-based visualization and integratedCytoscape web

Domain-Domain Interactions• Many PPI predictions are based on identifyinginteracting domain pairs or domain-motifs. (Ref:Deng et al.,2002, Ng et al.,2003, Riley et al., 2005, Sprinzak et al.,2001,Li et al., 2004, Neduva et al., 2005, Wang et al.,2004, Betel et al.,2007)• Based on identifying over representeddomain pairs in interacting proteins orconserved motifs.• To date, only a small number of conservedrecognition motifs have been identified(Scansite, ELM).

Domains and protein interactionsPawson T., Nash P., (2003), Science, 300,445-452

SH3 domain

Structure-based PPI predictionQEDYXRYVPXVPQEDYXRLXXLYXPXXFBetel D. et al., (2007) PLoS Computational Biology 3(9): e182

Learning the motifsRhoGAPPPI sources:YVMTVFRhoGAPRhoGAPRhoGAPBINDDIPIntActMINTYVPTVFQEDYCRLCPLYVPTVYYIPSVFRhoGAPRhoGAPYVPTVFRhoGAPWVPTVFQEDGDTLRGL

Gibbs Sampling• Gibbs sampling is a Markov Chain MonteCarlo (MCMC) algorithm that samples froman unknown joint distribution using the knownconditional probabilities.• Commonly used (in bioinformatics) for motifdiscoverysuch as, transcription factorsbinding sites.• Provides a mechanism for incorporating priorknowledge about the length and compositionof the motif.

Converting a motif to a profile

Applications of domain bindingprofiles• Predicting new interactions – predictinteracting proteins based on thedomain composition of given proteins.• Evaluate existing interactions – identifyinteractions within affinity pull-downcomplexes.

Verified PredictionsBetel D. et al., (2007) PLoS Computational Biology 3(9): e182