Interactome3D: adding structural details to protein networks

Journal name:
Nature Methods
Volume:
10,
Pages:
47–53
Year published:
DOI:
doi:10.1038/nmeth.2289
Received
Accepted
Published online

Abstract

Network-centered approaches are increasingly used to understand the fundamentals of biology. However, the molecular details contained in the interaction networks, often necessary to understand cellular processes, are very limited, and the experimental difficulties surrounding the determination of protein complex structures make computational modeling techniques paramount. Here we present Interactome3D, a resource for the structural annotation and modeling of protein-protein interactions. Through the integration of interaction data from the main pathway repositories, we provide structural details at atomic resolution for over 12,000 protein-protein interactions in eight model organisms. Unlike static databases, Interactome3D also allows biologists to upload newly discovered interactions and pathways in any species, select the best combination of structural templates and build three-dimensional models in a fully automated manner. Finally, we illustrate the value of Interactome3D through the structural annotation of the complement cascade pathway, rationalizing a potential common mechanism of action suggested for several disease-causing mutations.

At a glance

Figures

  1. The Interactome3D pipeline.
    Figure 1: The Interactome3D pipeline.

    The pipeline starts from either a user-provided list of interactions or a list of organisms for which it automatically compiles the most complete binary interactome. Experimental structures are collected and homology models built for both single interactors and binary complexes. Experimental structures are collected from the PDB, and homology models for single proteins are downloaded from Modbase. Models for interactions are built using Modeller starting from either global templates from the PDB or domain-domain templates from 3did.

  2. Structural coverage of proteins and interactions for eight organisms.
    Figure 2: Structural coverage of proteins and interactions for eight organisms.

    (a) Structural coverage of the single proteins in the binary interactomes. Coloring indicates the availability of complete experimental structures (green), complete models (yellow) or only partial models (orange). (b) Structural coverage of the binary interactions. Green, interactions for which an experimental structure is available; yellow and orange, interactions for which a homology model can be built from either a global template or a domain-domain template, respectively.

  3. Benchmarking of the homology models of interactions generated by Interactome3D.
    Figure 3: Benchmarking of the homology models of interactions generated by Interactome3D.

    (a,b) Classification of homology models generated by Interactome3D for interactions already having an experimental structure, shown in absolute numbers (a) and as percentages within a given organism (b). High, medium, acceptable and incorrect correspond to the quality criteria used by CAPRI for the evaluation of protein-protein docking predictions.

  4. Structural annotation of the complement cascade.
    Figure 4: Structural annotation of the complement cascade.

    Shown is the complement cascade pathway from KEGG (pathway ID hsa04610, inset) annotated with all the structural data collected by Interactome3D. For some of the proteins only partial models are available. We also mapped mutations related to three different diseases onto the collected structures and classified them as buried in the core of the proteins (circle), on the surface (star on the structure of a protein) or at an interface between two proteins (star on the line connecting two proteins). Mutations that could not be mapped onto the structure of the protein are represented with squares. Symbols of mutations are colored according to the disease to which they belong.

  5. Mapping disease mutations in the context of the structural interactome.
    Figure 5: Mapping disease mutations in the context of the structural interactome.

    Shown are complement C3 and CFH (right) and their interactions (left) either within the complement cascade (light blue) or taken from the human interactome (gray). Although VSIG4 is not in the KEGG pathway for the complement cascade, it is shown in blue because it is known to be a potent inhibitor of the complement pathway convertases42, 43. Onto this interaction neighborhood we have mapped mutations for ten diseases (see key, top left) and classified them as either buried in the core of the protein (circles) or exposed on the surface (stars). Stars on a line between two proteins refer to mutations found at an interface. The two circular insets show the mapping of nine mutations related to hemolitic uremic syndrome atypical onto two different experimental structures (PDB 2XQW and 2WII) of the interaction between CFH (orange) and C3 (blue). Mutated residues are shown in atomic surface representation and colored red. Although the mutations belong to two different proteins, most are located on the same interaction interfaces, and these may all act by affecting the binding between the two proteins. One mutated residue (Cys1158, in green) belongs to the interacting domain A2M_comp of C3 and is buried inside the domain instead of being exposed on the interaction interface with CFH.

Accession codes

Primary accessions

Referenced accessions

References

  1. Lee, M.J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780794 (2012).
  2. Shapira, S.D. et al. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139, 12551267 (2009).
  3. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 11731178 (2005).
  4. Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957968 (2005).
  5. Ewing, R.M. et al. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89 (2007).
  6. Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159164 (2012).
  7. David, A., Razali, R., Wass, M.N. & Sternberg, M.J. Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs. Hum. Mutat. 33, 359363 (2012).
  8. Dreze, M. et al. 'Edgetic' perturbation of a C. elegans BCL2 ortholog. Nat. Methods 6, 843849 (2009).
  9. Kim, P.M., Lu, L.J., Xia, Y. & Gerstein, M.B. Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314, 19381941 (2006).
  10. Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235242 (2000).
  11. Pieper, U. et al. ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. 39, D465D474 (2011).
  12. Zhang, Y. et al. Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 325, 15441549 (2009).
  13. Pache, R.A. & Aloy, P. Incorporating high-throughput proteomics experiments into structural biology pipelines: identification of the low-hanging fruits. Proteomics 8, 19591964 (2008).
  14. Stein, A., Mosca, R. & Aloy, P. Three-dimensional modeling of protein interactions and complexes is going 'omics. Curr. Opin. Struct. Biol. 21, 200208 (2011).
  15. Walhout, A.J. et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287, 116122 (2000).
  16. Aloy, P., Ceulemans, H., Stark, A. & Russell, R.B. The relationship between sequence and interaction divergence in proteins. J. Mol. Biol. 332, 989998 (2003).
  17. Aloy, P. et al. Structure-based assembly of protein complexes in yeast. Science 303, 20262029 (2004).
  18. Aloy, P. & Russell, R.B. Structural systems biology: modelling protein interactions. Nat. Rev. Mol. Cell Biol. 7, 188197 (2006).
  19. Kuzu, G., Keskin, O., Gursoy, A. & Nussinov, R. Constructing structural networks of signaling pathways on the proteome scale. Curr. Opin. Struct. Biol. 22, 367377 (2012).
  20. Kerrien, S. et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841D846 (2012).
  21. Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857D861 (2012).
  22. Turinsky, A.L., Razick, S., Turner, B., Donaldson, I.M. & Wodak, S.J. Interaction databases on the same page. Nat. Biotechnol. 29, 391393 (2011).
  23. Stein, A., Ceol, A. & Aloy, P. 3did: identification and classification of domain-based interactions of known three-dimensional structure. Nucleic Acids Res. 39, D718D723 (2011).
  24. Davis, F.P. & Sali, A. PIBASE: a comprehensive database of structurally defined protein interfaces. Bioinformatics 21, 19011907 (2005).
  25. Gong, S. et al. PSIbase: a database of Protein Structural Interactome map (PSIMAP). Bioinformatics 21, 25412543 (2005).
  26. Finn, R.D., Marshall, M. & Bateman, A. iPfam: visualization of protein-protein interactions in PDB at domain and amino acid resolutions. Bioinformatics 21, 410412 (2005).
  27. Itzhaki, Z., Akiva, E. & Margalit, H. Preferential use of protein domain pairs as interaction mediators: order and transitivity. Bioinformatics 26, 25642570 (2010).
  28. Sali, A. & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779815 (1993).
  29. Taylor, W.R. A deeply knotted protein structure and how it might fold. Nature 406, 916919 (2000).
  30. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 8390 (2009).
  31. Mosca, R., Pons, C., Fernandez-Recio, J. & Aloy, P. Pushing structural information into the yeast interactome by high-throughput protein docking experiments. PLoS Comput. Biol. 5, e1000490 (2009).
  32. Méndez, R., Leplae, R., De Maria, L. & Wodak, S.J. Assessment of blind predictions of protein-protein interactions: current status of docking methods. Proteins 52, 5167 (2003).
  33. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109D114 (2012).
  34. Matthews, L. et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37, D619D622 (2009).
  35. Bravo, J. & Aloy, P. Target selection for complex structural genomics. Curr. Opin. Struct. Biol. 16, 385392 (2006).
  36. Gordo, S. et al. Stability and structural recovery of the tetramerization domain of p53–R337H mutant induced by a designed templating ligand. Proc. Natl. Acad. Sci. USA 105, 1642616431 (2008).
  37. Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009).
  38. Kiel, C. et al. Structural and functional protein network analyses predict novel signaling functions for rhodopsin. Mol. Syst. Biol. 7, 551 (2011).
  39. Russell, R.B. & Aloy, P. Targeting and tinkering with interaction networks. Nat. Chem. Biol. 4, 666673 (2008).
  40. Lopes, C.T. et al. Cytoscape Web: an interactive web-based network browser. Bioinformatics 26, 23472348 (2010).
  41. Russel, D. et al. Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLoS Biol. 10, e1001244 (2012).
  42. Vogt, L. et al. VSIG4, a B7 family-related protein, is a negative regulator of T cell activation. J. Clin. Invest. 116, 28172826 (2006).
  43. Wiesmann, C. et al. Structure of C3b in complex with CRIg gives insights into regulation of complement activation. Nature 444, 217220 (2006).
  44. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449D451 (2004).
  45. Goll, J. et al. MPIDB: the microbial protein interaction database. Bioinformatics 24, 17431744 (2008).
  46. Chautard, E., Fatoux-Ardore, M., Ballut, L., Thierry-Mieg, N. & Ricard-Blum, S. MatrixDB, the extracellular matrix interaction database. Nucleic Acids Res. 39, D235D240 (2011).
  47. Lynn, D.J. et al. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol. Syst. Biol. 4, 218 (2008).
  48. Stark, C. et al. The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 39, D698D704 (2011).
  49. Isserlin, R., El-Badrawi, R.A. & Bader, G.D. The Biomolecular Interaction Network Database in PSI-MI 2.5. Database (Oxford) 2011, baq037 (2011).
  50. Keshava Prasad, T.S. et al. Human Protein Reference Database–2009 update. Nucleic Acids Res. 37, D767D772 (2009).
  51. Côté, R.G. et al. The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases. BMC Bioinformatics 8, 401 (2007).
  52. UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 40, D71D75 (2012).
  53. Orchard, S. et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat. Methods 9, 345350 (2012).
  54. Orchard, S. et al. The minimum information required for reporting a molecular interaction experiment (MIMIx). Nat. Biotechnol. 25, 894898 (2007).
  55. Ceol, A. et al. MINT, the molecular interaction database: 2009 update. Nucleic Acids Res. 38, D532D539 (2010).
  56. Hu, P. et al. Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 7, e96 (2009).
  57. Velankar, S. et al. E-MSD: an integrated data resource for bioinformatics. Nucleic Acids Res. 33, D262D265 (2005).
  58. Eswar, N. et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 31, 33753380 (2003).
  59. Aloy, P. & Russell, R.B. InterPreTS: protein interaction prediction through tertiary structure. Bioinformatics 19, 161162 (2003).
  60. Shen, M.Y. & Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 15, 25072524 (2006).
  61. Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290D301 (2012).
  62. Eddy, S.R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).
  63. Stein, A. & Aloy, P. Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures. PLoS Comput. Biol. 6, e1000789 (2010).
  64. Kerrien, S. et al. Broadening the horizon–level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biol. 5, 44 (2007).
  65. Jones, S., Marin, A. & Thornton, J.M. Protein domain interfaces: characterization and comparison with oligomeric protein interfaces. Protein Eng. 13, 7782 (2000).
  66. Miller, S., Janin, J., Lesk, A.M. & Chothia, C. Interior and surface of monomeric proteins. J. Mol. Biol. 196, 641656 (1987).

Download references

Author information

Affiliations

  1. Joint IRB-BSC Program in Computational Biology, Institute for Research in Biomedicine, Barcelona, Spain.

    • Roberto Mosca,
    • Arnaud Céol &
    • Patrick Aloy
  2. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.

    • Patrick Aloy

Contributions

R.M. conceived and designed the work, wrote the manuscript, developed the pipeline, analyzed the data and implemented the Interactome3D web resource. A.C. compiled the integrated interaction database used by Interactome3D and implemented the Interactome3D web resource. P.A. conceived the work and wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (647 KB)

    Supplementary Figs. 1–3, Supplementary Tables 1, 2 and 4

Excel files

  1. Supplementary Table 3 (659 KB)

    Structures used for the structural annotation of the Complement Cascade pathway

Additional data