Investigating a macromolecular complex: The toolkit of methods

Journal of Structural Biology 175 (2011) 106–112

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Journal of Structural Biology journal homepage: www.elsevier.com/locate/yjsbi

Review

Investigating a macromolecular complex: The toolkit of methods Anastassis Perrakis a,⇑, Andrea Musacchio b, Stephen Cusack c,d, Carlo Petosa e,⇑ a

Department of Biochemistry, NKI, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, I-20139 Milan, Italy c European Molecular Biology Laboratory, Grenoble Outstation, 6 rue Jules Horowitz, Grenoble Cedex 9, France d Unit of Virus Host-Cell Interactions, UMI3265, Université J. Fourier/EMBL/CNRS, 6 rue Jules Horowitz, Grenoble Cedex 9, France e Institut de Biologie Structurale JP Ebel, UMR5075 (Commissariat à L’Energie Atomique et aux Energies Alternatives/Centre National de la Recherche Scientifique/Université J. Fourier), 41 rue Jules Horowitz, 38027 Grenoble Cedex 1, France b

a r t i c l e

i n f o

Article history: Available online 18 May 2011 Keywords: Macromolecular complexes Protein–protein interactions Multidisciplinarity

a b s t r a c t Structural biologists studying macromolecular complexes spend considerable effort doing strictly ‘‘nonstructural’’ work: investigating the physiological relevance and biochemical properties of a complex, preparing homogeneous samples for structural analysis, and experimentally validating structure-based hypotheses regarding function or mechanism. Familiarity with the diverse perspectives and techniques available for studying complexes helps in the critical assessment of non-structural data, expedites the pre-structural characterization of a complex and facilitates the investigation of function. Here we survey the approaches and techniques used to study macromolecular complexes from various viewpoints, including genetics, cell and molecular biology, biochemistry/biophysics, structural biology, and systems biology/bioinformatics. The aim of this overview is to heighten awareness of the diversity of perspectives and experimental tools available for investigating complexes and of their usefulness for the structural biologist. Ó 2011 Elsevier Inc. All rights reserved.

1. Introduction Macromolecular complexes are an essential aspect of all cellular processes, including metabolism, cell signaling, gene expression, trafficking, cell cycle regulation and the formation of subcellular structures. Moreover, complexes are of great biomedical relevance: on the one hand, factors that perturb biomolecular interaction networks underlie a number of diseases (Zanzoni et al., 2009); on the other, the deliberate inhibition of protein–protein interactions (PPIs) is an increasingly common strategy in novel drug discovery initiatives (Arkin and Whitty, 2009). Thus, a detailed knowledge of

Abbreviations: AUC, analytical ultracentrifugation; ChIP, chromatin immunoprecipitation; co-IP, co-immunoprecipitation; cryoEM, cryoelectron microscopy; cryoET, cryoEM tomography; DSC, differential scanning calorimetry; EPR, electron paramagnetic resonance; FAIM, fluorescence anisotropy imaging microscopy; FCCS, fluorescence cross-correlation spectroscopy; FRAP, fluorescence recovery after photobleaching; FRET, Förster resonance energy transfer; ITC, isothermal titration calorimetry; MALLS, multi-angle laser light scattering; MS, mass spectrometry; PCA, protein complementation assay; PPI, Protein–protein interaction; PTM, posttranslational modification; RNAi, RNA interference; SAXS/SANS, small angle X-ray/neutron scattering; SPR, surface plasmon resonance; TAP, tandem affinity purification; Y2H, yeast two-hybrid. ⇑ Corresponding authors. Fax: +31 205 121 954 (A. Perrakis), +33 438 785494 (C. Petosa). E-mail addresses: [email protected] (A. Perrakis), andrea.musacchio@ ifom-ieo-campus.it (A. Musacchio), [email protected] (S. Cusack), [email protected] (C. Petosa). 1047-8477/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jsb.2011.05.014

complexes is key to understanding cell physiology and to expediting advances in medicine. Acquiring this knowledge requires the combined research efforts from diverse biological disciplines. During the course of investigation, a given macromolecular complex will undergo distinct stages of inquiry or discovery, with each stage lying within the realm of one or more disciplines (Fig. 1). These stages may include: discovering that two or more gene products functionally interact; determining that they physically associate in vivo; deducing the interaction’s relevance for a particular cellular process; characterizing biochemical and biophysical properties of the complex; determining its three-dimensional structure; and situating the complex within the global network of intermolecular interactions in the cell. Structural biologists are concerned with several of these stages. For example, they may spend considerable effort biochemically characterizing a complex prior to solving its structure, and afterwards may perform diverse experiments to clarify its functional role or mechanism of action. Knowledge of the concepts and techniques that characterize non-structural disciplines is therefore valuable. These ideas motivated us to organize an international workshop aimed primarily at structural biologists working on macromolecular complexes. The meeting, held in Amsterdam in October 2009, was attended by over 120 scientists and was co-sponsored by three European-Commission funded initiatives: 3D Repertoire, TEACH-SG, and the main contributor to this special JSB issue, SPINE2-Complexes. Invited speakers presented research from diverse fields of expertise, including cell,

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Fig. 1. Stages of inquiry concerning a macromolecular complex. Each oval represents a particular discovery or line of inquiry. At any given stage, information about the complex is acquired using techniques from one or more biological disciplines, as shown. For simplicity, individual techniques are classified under a single discipline (see Table 1), and disciplines are depicted as having sharp boundaries. In reality, many techniques can be classified under more than one discipline, and the boundaries between disciplines are themselves blurred.

structural, systems and computational biology (the program is available at http://xtal.nki.nl/Oct2009/). Here we provide an overview of some key ideas that emerged from this meeting. We focus on approaches and experimental techniques that characterize the study of macromolecular complexes from five distinct perspectives: genetics, molecular and cell biology, biochemistry/biophysics, structural biology, and systems biology/bioinformatics. Our intention is not to provide an exhaustive list of techniques but to provide entry points into the relevant literature. 2. Investigating functional interactions: the power of genetics Initial evidence for the existence of a macromolecular complex often comes from experiments reporting a genetic interaction. Such interactions include synthetic lethality (i.e., the deletion or mutation of two separate genes is lethal, whereas the individual

alterations are not), synthetic rescue (the defect caused by altering one gene is suppressed by altering a second), dosage lethality (a strain viable when a single gene is altered becomes non-viable upon increasing the expression level of a second gene) and dosage rescue (the phenotype due to one mutation is suppressed or viability rescued by increasing a second gene’s expression) (Table 1). Most large-scale genetic interaction screens have been performed using S. cerevisae, where the availability of genome-wide deletion collections permits the comprehensive generation of double knock-out mutants (Dixon et al., 2009). Large-scale screens are also commonly performed using Caenorhabditis elegans, Drosophila melanogaster and Danio rerio, as well as cultured mammalian cells; these screens primarily rely on genome-wide RNAi libraries that mimic the effect of gene deletions by reducing the expression of individual genes (Mohr et al., 2010). Information on genetic interactions has also been enriched by transgenic mouse studies (Miller, 2011).

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A. Perrakis et al. / Journal of Structural Biology 175 (2011) 106–112 Table 1 Techniques for investigating macromolecular complexes. Technique

References

Genetics Synthetic lethality or enhancement Dosage lethality or growth defect Synthetic rescue (phenotypic suppression) Dosage rescue (multicopy suppression) Gene co-expression

(Nijman, 2011) (Dixon et al., 2009) (Appling, 1999; Dixon et al., 2009) (Appling, 1999; Dixon et al., 2009) (Farber and Lusis, 2008)

Cell and molecular biology Affinity capture: Co-immunoprecipitation (co-IP) Chromatin immunoprecipitation (ChIP, ChIP-seq, ChIP-chip) RNA immunoprecipitation (RIP) Tandem affinity purification (TAP) Pull-down Far Western Protein microarray Affinity selection: Phage display Reporter reconstitution: Yeast two-hybrid analysis (Y2H) Mammalian two-hybrid Mammalian protein–protein interaction trap (MAPPIT) SOS recruitment Ras recruitment G-protein based screening Split-ubiquitin Protein complementation assay (PCA) Fluorescence imaging: Confocal microscopy for co-localization of proteins Förster resonance energy transfer (FRET) Fluorescence (cross-)correlation spectroscopy (FCS, FCCS) Fluorescence lifetime imaging (FLIM) Fluorescence recovery after photobleaching (FRAP) HomoFRET by fluorescence anisotropy imaging microscopy (FAIM)

(Markham et al., 2007) (Collas, 2010) (Conrad, 2008) (Collins and Choudhary, 2008) (Brymora et al., 2004) (Machida and Mayer, 2009) (Wolf-Yadlin et al., 2009) (Pande et al., 2010) (Suter et al., 2008) (Lievens et al., 2009) (Eyckerman et al., 2001) (Aronheim et al., 1997) (Broder et al., 1998) (Ehrhard et al., 2000) (Stagljar et al., 1998) (Shyu and Hu, 2008) (Miyashita, 2004) (Sun et al., 2011) (Haustein and Schwille, 2007) (Levitt et al., 2009) (Bancaud et al., 2010) (Chan et al., 2011)

Biochemistry and biophysics Surface plasmon resonance (SPR) Isothermal titration calorimetry (ITC) Differential scanning calorimetry (DSC) Thermophoresis Fluorescence Polarization/Anisotropy Analytical ultracentrifugation (AUC) Native gel electrophoresis (native PAGE) DNA footprinting Co-sedimentation Size exclusion chromatography and Multi-angle laser light scattering (SEC/MALLS) Limited proteolysis Fluorescence imaging techniques

(Rich and Myszka, 2010) (Falconer and Collins, 2011) (Privalov, 2009) (Wienken et al., 2010) (Jameson and Ross, 2010) (Cole et al., 2008) (Wittig and Schagger, 2008) (Hampshire et al., 2007) (Hughes et al., 2008) (Mogridge, 2004) (Singh et al., 2001) See above

Structural biology Atomic force microscopy (AFM) Native mass spectrometry Ion mobility mass spectrometry (IM-MS) Super-resolution microscopy (STORM/(F)PALM, STED, (S)SIM) Förster resonance energy transfer (FRET) Cross-linking and MS Electron para-magnetic resonance (EPR) Small-angle scattering (SAXS/SANS) Macromolecular crystallography Nuclear magnetic resonance Cryo-EM and single-particle analysis High-resolution cryoEM CryoEM tomography (cryoET) X-ray laser diffraction X-ray tomography

(Allison et al., 2010) (Sharon and Robinson, 2007) (Uetrecht et al., 2010) (Huang et al., 2010) (Muschielok et al., 2008) (Rappsilber, 2011) (Schiemann and Prisner, 2007) (Mertens and Svergun, 2010) (Giege and Sauter, 2010) (Foster et al., 2007) (Orlova and Saibil, 2010) (Grigorieff and Harrison, 2011) (Ben-Harush et al., 2010) (Seibert et al., 2011) (Larabell and Nugent, 2010)

Variations of the above screens are also possible using chemical genetics, whereby phenotypes are perturbed by inhibiting gene products with small molecules (Kawasumi and Nghiem, 2007). In forward chemical genetics, a chemical library is used to screen for a desired phenotype, and the target of the drug that induced the phenotype is subsequently identified; in reverse chemical genetics, a specific protein is blocked with a known inhibitor and the phenotypic consequences of inhibition are examined. Another frequently

studied genetic interaction involves co-regulated gene expression, whereby the expression levels of two genes, when observed under different conditions, are more highly correlated than that of a random pair of genes. Interacting proteins are more likely to have their genes co-expressed compared with non-interacting proteins, and gene products which constitute a dedicated complex such as the ribosome or the proteasome have the most highly correlated co-expression (Webb and Westhead, 2009). DNA microarray

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technology has allowed millions of gene expression patterns to be recorded, with most of these available for query and analysis at public repositories such as Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and ArrayExpress (http:// www.ebi.ac.uk/arrayexpress/). Each of the above approaches has advantages and disadvantages. Interaction screens in yeast can be performed relatively simply and at low cost, but may not be suitable for studying complexes specific to higher eukaryotes. RNAi screening has the advantage that siRNA is easy to synthesize and to apply to a wide variety of cell types, but its usefulness is potentially limited by off-target effects. Transgenic mice allow for the physiological relevance of a given interaction to be assessed in the whole animal, but generating these mice is labor-intensive and costly. Chemical inhibitors work rapidly and often reversibly, and can perturb a single function of a multifunctional protein. However, in forward chemical genetic screens the specific inhibitor target may resist identification, while reverse screens are limited to available and characterized compounds. A more general caveat regarding genetic data is that a genetic interaction does not necessarily imply a physical association, as regulatory, shared-pathway or other indirect interactions may underlie the observed genetic relationship. Nevertheless, many protein–protein and protein–RNA interactions give rise to a genetic interaction, and a genetic screen may reveal physical interactions too unstable or transient to detect by other methods. Moreover, because genetic and biochemical data are complementary, showing a genetic interaction is compelling evidence for the physiological relevance of an interaction observed in vitro. 3. Using molecular and cell biology to study physical associations in vivo While a functional interaction may point to the existence of a physiologically relevant complex, demonstrating that the gene products associate in vivo is essential for the hypothesis to be credible. At this stage the notion of a physical interaction is central, although whether the gene products are in direct or indirect contact is of secondary importance. A large battery of techniques for detecting and demonstrating macromolecular interactions in vivo has been developed (Table 1). These can be broadly classified into four groups: (i) affinity capture methods, including co-immunoprecipitation (co-IP), pull-down assays and tandem affinity purification (TAP), each potentially coupled to mass spectrometry; (ii) reporter reconstitution strategies, including yeast two hybrid (Y2H) and protein complementation assays (PCA); (iii) affinity selection techniques, such as phage display; and (iv) fluorescence microscopy imaging, including co-localization studies, Förster resonance energy transfer (FRET), fluorescence lifetime imaging (FLIM), fluorescence cross-correlation spectroscopy (FCCS), fluorescence recovery after photobleaching (FRAP), and homoFRET by fluorescence anisotropy imaging microscopy (FAIM). (References are given in Table 1.) Each of these methods has specific advantages and drawbacks. For instance, weak or transient interactions may pass undetected in affinity capture/MS methods (generally useful for Kd < mid nM), whereas Y2H and phage display are more sensitive (Kd of lM are detectable). Phage display is highly suited for detecting interactions between a domain and a linear motif (peptide), but poorly suited for detecting those between globular domains. For both Y2H and phage display, false negatives may arise if proteins fail to undergo the appropriate post-translational modifications (PTMs) or to fold properly in the relevant sub-compartment of the heterologous host (yeast nucleus or bacterial periplasm). Also, the use of fusion tags (affinity, epitope, or fluorescent tags) in many of these techniques is problematic, as the presence or experimental processing of the tag may alter the biology (due to impaired folding

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or improper sub-cellular localization) or hinder the interaction with the partner species. False positives can also creep into these experiments. An interaction detected between two proteins may in fact be mediated by a third constituent, or involve a highly abundant cellular protein that binds to many proteins non-specifically. In affinity capture methods, the loss of sub-cellular integrity under the assay conditions might reveal spurious interactions between proteins ordinarily localized to different cellular compartments. False positives in Y2H screens, estimated to range from 25% to 45% (Huang et al., 2007), may arise because of the forced over-expression of heterologous proteins, because certain gene products (e.g., transcription factors) can auto-activate transcription of the reporter genes, and because expressed fusion proteins are forced to the nucleus, which may not be their physiologically relevant site of localization (particularly true for membrane proteins) (Suter et al., 2008). Variant Y2H screens, in which modular reporters other than transcription factors are reconstituted, overcome some of these problems. These include the SOS and Ras recruitment systems, the G-protein-based screening assay, the split-ubiquitin system, the mammalian protein–protein interaction trap (MAPPIT) and the mammalian twohybrid system (Table 1). 4. Studying direct physical interactions in vitro: biochemical and biophysical approaches The successful structure determination of a complex often depends on the prior detailed characterization of its biochemical and biophysical behavior. Such studies are usually performed on highly purified species in vitro, and the working premise is that macromolecules not only physically associate but also are in direct physical contact. Typical issues addressed at this stage include: (i) Stoichiometry: in what relative proportions are subunits present? Is the complex stoichiometrically homogeneous? Do stable sub-complexes exist? (ii) Nature of the interaction. Do macromolecules interact by a domain–domain or a domain–motif type of interaction? In the former case, two or more globular domains associate, typically through an extensive interface and with relatively high affinity (Kd < 100 nM; e.g., Ras–RasGAP complex); in the latter, a linear motif in a disordered region becomes structured upon binding, and the interaction is usually rather weak (Kd > 1 lM; e.g., an SH2-phosphopeptide complex) (Bader et al., 2008). The prevalence of intrinsically disordered regions has become increasingly apparent in recent years, with over 150 distinct types of linear motifs reported (available for query at http://elm.eu.org). (iii) Stability. What are the binding affinities between components? Is assembly cooperative or non-cooperative? What is the lifetime of the complex? Is it transient or stable? Examples of transient complexes include most kinases and their substrates, while stable (also called dedicated) complexes include the ribosome, proteasome, and RNA polymerases (Nooren and Thornton, 2003). (iv) Kinetics: Is there a preferred order of assembly? What are the rate constants for complex formation? (v) Covalent modifications: Do certain subunits possess PTMs? Do these influence complex formation? (vi) Specificity: For complexes involving peptide, DNA or RNA recognition, is complex formation dependent on nucleotide or peptide sequence? Which sequence yields the highest affinity? Which bases or residues contribute most to binding affinity? These properties may be determined either ‘‘top-down’’, by investigating an intact complex purified from a biological extract, or ‘‘bottom-up’’, by recombinantly (co-)expressing constituents and studying them in binary, ternary, and higher-order combinations. Such studies may call upon a wide range of techniques, including pull-down assays, native gel electrophoresis, chromatography, proteolytic protection assays, footprinting, cross-linking, mass spectrometry, multi-angle laser light scattering (MALLS), analytical

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ultracentrifugation (AUC), fluorescence polarization, thermophoresis, isothermal titration calorimetry (ITC), differential scanning calorimetry (DSC), and surface plasmon resonance (SPR). (see Table 1 for specific references). Biophysical parameters may also be determined by fluorescence imaging techniques such as FRET and FCCS. False negatives are possible at this stage and often cast doubt on data obtained through molecular or cell biology techniques. Biochemical analysis may fail to recapitulate associations detected in vivo, as a key element (e.g., an additional subunit, PTMs, allosteric activation, cooperativity) may be missing from the in vitro experiment. False positives are also possible, as one may unknowingly investigate a complex that is biophysically bona fide but physiologically irrelevant. A good example comes from one of the authors’ (A.P.) laboratories: in the course of purifying a recombinant human protein, a bacterial heterodimer was inadvertently purified and crystallized. Although this complex was stable and stoichiometrically homogeneous, it comprised two proteins ordinarily localized to distinct (peri- and cytoplasmic) compartments, and so was not biologically relevant. 5. Obtaining 3D information by structural methods Knowledge about the structure of a complex depends on the resolving power of the method employed. At the lower end of the scale, several techniques provide partial spatial information that may yield important functional insights in their own right, and which when combined with atomic structures of individual subunits can guide the construction of a pseudo-atomic model of the entire complex. For example, native mass spectrometry (MS), ion-mobility MS and super-resolution microscopy techniques can yield the overall topology and stoichiometry of a multi-subunit complex; techniques such as FRET, cross-linking combined with mass spectrometry, and site-directed spin-labeling combined with electron paramagnetic resonance (EPR) spectroscopy provide distance data between pairs of residues; and techniques such as small angle scattering (SAXS/SANS), atomic force microscopy (AFM), cryoelectron microscopy (cryoEM), and EM tomography provide global shape information. At the higher end of the resolution scale are the traditional methods for atomic structure determination: NMR and macromolecular crystallography, as well as the emerging technique of high-resolution cryo-EM, which has yet to reach its full potential. Exciting developments have also recently been reported for X-ray laser diffraction and X-ray tomography. (see Table 1 for references). Each of these techniques has its strengths and weaknesses. Xray crystallography can give the highest resolution regarding the structure of a complex and is not limited (in principle) by the molecular weight of the sample; however, many complexes are stubbornly resistant to crystallization efforts. Also, crystal contacts may be mistaken for intersubunit interfaces, necessitating additional techniques to establish the correct quaternary structure. Structure determination by NMR is difficult or impossible for high molecular-weight complexes (>50 kDa); however, NMR can provide information regarding conformational dynamics as well as binding affinities, and can pinpoint regions mediating complex formation without the need for a full structure determination. SAXS provides low-resolution information which in itself may be of limited use; however, when combined with other structural data, a SAXS experiment can powerfully discriminate between alternative hypotheses regarding the structure of a complex. Also, like NMR, SAXS can handle the presence of large intrinsically disordered domains, which remains an obstacle for crystallographic studies. CryoEM is limited to the study of high molecular-weight complexes, but has the advantage of allowing direct visualization of individual particles; indeed, new developments allow particles to be clustered into structural classes that represent different snap-

shots in the lifetime of a complex (Fischer et al., 2010). Likewise, cryoEM tomography holds great promise for observing individual conformations, both in vitro and in the natural cellular environment; however, current technology limits the analysis to large complexes, and the resulting structural resolution remains rather low. To overcome these and other limitations of individual techniques, hybrid approaches that combine multiple techniques are increasingly being used to deduce the structures of multi-subunit assemblies (Cowieson et al., 2008). 6. Mapping the interactome through systems biology and bioinformatics Systems biology aims to provide a framework for understanding biology as an integrated system. Achieving this goal requires the comprehensive determination of all interactions among genes and proteins (Charbonnier et al., 2008; Chuang et al., 2010). Large-scale genetic interaction screens have already been discussed above (see Section 2). Large-scale studies to map protein– protein interactions have been carried out in yeast, Escherichia coli, drosophila, C. elegans and humans. The majority of these screens have been performed using Y2H, affinity capture/mass spectrometry (e.g. TAP-MS), and protein chips. Protein–DNA networks have also been mapped using protein microarrays. The errors associated with a large-scale screen may be significant (see Section 3 for Y2H error rates), and so it is important to validate individual results using small-scale experiments or to integrate data from different high-throughput studies. Combined with bioinformatics, systems biology provides powerful insights into the way a particular complex fits into the global network of interacting partners (the ‘interactome’). This is usually depicted as a graph in which individual macromolecules are shown as nodes linked by edges to their interacting partners. Such a representation lays bare the global structure of the network, allowing for a clearer understanding of cellular processes and of how these may go awry during disease (Vidal et al., 2011). Analyzing the topology of local motifs within the network can reveal important clues into the role played by individual macromolecules or complexes of interest. For example, proteins associated with disease are frequently ‘hub’ proteins, those having many more interaction partners than most other proteins in the network. However, an important drawback of the graph notation is the fact that detailed information is necessarily lost when proteins are represented as simple dots. This includes information regarding binding affinity, stoichiometry, PTM-dependence of complex formation, and structural details of the intermolecular interfaces. Moreover, most current representations are static snapshots, whereas complex formation in the cell is a highly dynamic process. Significant progress has been made in overcoming some of these limitations [see for example: (Kiel et al., 2008; Santonico et al., 2005)]. A wealth of interaction data is available for retrieval and analysis from many public repositories (Table 2). These include primary databases of experimentally determined interaction data (curated from the literature or deposited by the investigator), databases that include interactions based on in silico predictions, and meta-databases, which integrate information from multiple primary databases. Individual repositories vary greatly in scope of content and depth of annotation. However, redundancy between databases makes it difficult to recommend a single resource for any particular query. To address this problem, several databases have formed the International Molecular Exchange (IMEx) consortium, in which partners exchange molecular interaction records so as to create an overarching repository broader in scope and deeper in information content than otherwise possible. One should nevertheless be aware that all interaction databases contain both false positive and false negative interaction data. These are due to primary errors

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A. Perrakis et al. / Journal of Structural Biology 175 (2011) 106–112 Table 2 Some protein interaction databases. Database Primary Databases AfCS

Web site

Alliance for Cellular Signalling

http://www.signaling-gateway.org

BIND

Biomolecular Interaction Network Database

http://www.bind.ca

BioGRIDa

Biological General Repository for Interaction Datasets

http://www.thebiogrid.org

DIPa

Database of Interacting Proteins

http://dip.doe-mbi.ucla.edu

HPRD

Human Protein Reference Database IntAct molecular interaction database Extracellular Matrix Interactions Database

IntActa MatrixDBa MINTa

http://www.hprd.org

MPACTa

Molecular Interactions Database Representation of Interaction Data at the Munich Information Center for Protein Sequences (MIPS)

MppDB

Mouse protein–protein interaction DataBase

Meta-databases APID BISC CORUM HAPPI

Saccharomyces Protein–protein Interaction Database

http://gemdock.life.nctu.edu.tw/dapid http://flydpi.nhri.org.tw/protein/fly http://mips.helmholtz-muenchen.de/proj/ppi http://cmb.bnu.edu.cn/SPIDer/index.html

Agile Protein Interaction DataAnalyzer

http://bioinfow.dep.usal.es/apid

Binary subcomplexes in proteins database Comprehensive resource of mammalian protein complexes

http://bisc.cse.ucsc.edu

Human Annotated and Predicted Protein Interaction database

http://mips.gsf.de/genre/proj/corum http://bio.informatics.iupui.edu/HAPPI

iRefIndex

Interaction Reference Index

http://irefindex.uio.no

iRefWeb MPIDBa

Microbial Protein Interaction Database

http://wodaklab.org/iRefWeb/ http://www.jcvi.org/mpidb

OPHID

Online Predicted Human Interaction Database

http://ophid.utoronto.ca

STRING

Search Tool for the Retrieval of Interacting Genes/Proteins

http://string-db.org

Protein–DNA interaction databases EDGEdb C. elegans differential gene expression database ENSEMBL hPDI human protein DNA interactome database UniPROBE Universal PBM Resource for Oligonucleotide-Binding Evaluation a

http://mips.gsf.de/genre/proj/mpact http://bio.scu.edu.cn/mppi

Databases that include in silico predictions DAPID Domain-Annotated Protein Interaction Database Fly-DPI Database of protein interactomes for D. melanogaster MPPI MIPS Mammalian Protein–Protein Interaction database SPIDer

http://www.ebi.ac.uk/intact http://matrixdb.ibcp.fr http://mint.bio.uniroma2.it/mint

http://edgedb.umassmed.edu http://www.ensembl.org http://bioinfo.wilmer.jhu.edu/PDI/ http://uniprobe.org

Members of the IMEx consortium.

in the underlying experimental data as well as secondary errors introduced upon abstracting information to the data models and transferring this to the database. Differences in curation method also lead to discrepancies between databases; a recent analysis of nine public databases revealed only 42% pairwise agreement on interactions curated from the same publication (Turinsky et al., 2010). Nonetheless, used cautiously, interaction databases can be a valuable tool for verifying and generating hypotheses. 7. Concluding remarks Two trends are likely to continue among structural biologists: an increasing focus on macromolecular complexes and an increasingly multidisciplinary approach towards studying them. We hope this overview will serve as a useful resource for researchers interested in exploiting the full spectrum of techniques available for achieving these goals. Acknowledgments We are grateful to 3DRepertoire, Spine2Complexes and TeachSG for sponsoring the workshop mentioned in this report. We thank all meeting participants and especially the speakers: J. Basquin, G. Cesareni, J. Ellenberg, T. Gibson, C. Kiel, A. Ladurner, O. Medalia, D. Moras, C. Müller, L. Pearl, B. Séraphin, H. Stark, U. Stelzl, D. Stuart, and P. Tompa. We are particularly grateful to B. Séraphin for articulating a useful definition of a macromolecular complex, and to Titia Sixma for helpful comments on the manu-

script. We apologize to colleagues for omitting many important references due to space constraints.

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