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Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

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The IMEx Consortium Curators, Khadake, J., Meldal, B., Panni, S., Thorneycroft, D., van Roey, K., Abbani, S., Salwinski, L., Pellegrini, M., Iannuccelli, M., Licata, L., Cesareni, G., Roechert, B., Bridge, A., Ammari, M. G., McCarthy, F., Broackes-Carter, F., Campbell, N. H., Melidoni, A. N., Rodriguez-Lopez, M., Lovering, R. C., Jagannathan, S., Chen, C., Lynn, D. J., Ricard-Blum, S., Mahadevan, U., Raghunath, A., del-Toro, N., Duesbury, M., Koch, M., Perfetto, L., Shrivastava, A., Ochoa, D., Wagih, O., Piñero, J., Kotlyar, M., Pastrello, C., Beltrao, P., Furlong, L. I., Jurisica, I., Hermjakob, H., Orchard, S., Porras, P.·2019·Nature Communications
DOI 10.1038/s41467-018-07709-6

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Abstract

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule’s interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

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Research works printed but not linked: 48

  1. Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159–164 (2012). · DOI · Paper page
  2. Mosca, R. et al. dSysMap: exploring the edgetic role of disease mutations. Nat. Methods 12, 167–168 (2015). · DOI
  3. Porta-Pardo, E., Garcia-Alonso, L., Hrabe, T., Dopazo, J. & Godzik, A. A pan-cancer catalogue of cancer driver protein interaction interfaces. PLoS Comput. Biol. 11, e1004518 (2015). · DOI
  4. Buljan, M., Blattmann, P., Aebersold, R. & Boutros, M. Systematic characterization of pan-cancer mutation clusters. Mol. Syst. Biol. 14, e7974–e7974 (2018). · DOI
  5. Chen, S. et al. An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat. Genet. 50, 1032–1040 (2018). · DOI
  6. Burén, S. et al. Regulation of OGT by URI in response to glucose confers c-MYC-dependent survival mechanisms. Cancer Cell 30, 290–307 (2016). · DOI
  7. Liu, X. et al. Bidirectional regulation of neutrophil migration by mitogen-activated protein kinases. Nat. Immunol. 13, 457–464 (2012). · DOI · Paper page
  8. Maio, N., Kim, K. S., Singh, A. & Rouault, T. A. A single adaptable cochaperone-scaffold complex delivers nascent iron-sulfur clusters to mammalian respiratory chain complexes I–III. Cell Metab. 25, 945–953.e6 (2017). · DOI
  9. Orchard, S. et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat. Methods 9, 345–350 (2012). · DOI · Paper page
  10. Sivade Dumousseau, M. et al. Encompassing new use cases—level 3.0 of the HUPO-PSI format for molecular interactions. BMC Bioinforma. 19, 134 (2018). · DOI
  11. UniProt Consortium, U. C. U. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017). · DOI · Paper page
  12. Young, J. Y. et al. Worldwide Protein Data Bank biocuration supporting open access to high-quality 3D structural biology data. Database 2018, bay002 (2018). · DOI
  13. Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–363 (2014). · DOI · Paper page
  14. Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857–D861 (2012). · DOI · Paper page
  15. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–212 (2015). · DOI · Paper page
  16. Kotlyar, M., Pastrello, C., Sheahan, N. & Jurisica, I. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 44, D536–41 (2016). · DOI · Paper page
  17. Ammari, M. G., Gresham, C. R., McCarthy, F. M. & Nanduri, B. HPIDB 2.0: a curated database for host-pathogen interactions. Database 2016, baw103 (2016). · DOI · Paper page
  18. Lynn, D. J. et al. Curating the innate immunity interactome. BMC Syst. Biol. 4, 117 (2010). · DOI
  19. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004). · DOI · Paper page
  20. Launay, G., Salza, R., Multedo, D., Thierry-Mieg, N. & Ricard-Blum, S. MatrixDB, the extracellular matrix interaction database: updated content, a new navigator and expanded functionalities. Nucleic Acids Res. 43, D321–327 (2015). · DOI
  21. Albrecht, A. G. et al. Mechanistic characterization of sulfur transfer from cysteine desulfurase SufS to the iron-sulfur scaffold SufU in Bacillus subtilis. FEBS Lett. 585, 465–470 (2011). · DOI
  22. Morrison, K. L. & Weiss, G. A. Combinatorial alanine-scanning. Curr. Opin. Chem. Biol. 5, 302–307 (2001). · DOI · Paper page
  23. Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014). · DOI · Paper page
  24. Nightingale, A. et al. The Proteins API: accessing key integrated protein and genome information. Nucleic Acids Res. 45, W539–W544 (2017). · DOI
  25. Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–811 (2015). · DOI · Paper page
  26. Piñero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2017). · DOI · Paper page
  27. Famiglietti, M. L. et al. Genetic variations and diseases in UniProtKB/Swiss-Prot: the ins and outs of expert manual curation. Hum. Mutat. 35, 927–935 (2014). · DOI
  28. Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662–669 (2015). · DOI · Paper page
  29. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017). · DOI · Paper page
  30. Lipscomb, C. E. Medical Subject Headings (MeSH). Bull. Med. Libr. Assoc. 88, 265–266 (2000).
  31. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013). · DOI · Paper page
  32. Verma, R., Schwaneberg, U. & Roccatano, D. Computer-aided protein directed evolution: a review of web servers, databases and other computational tools forprotein engineering. Comput. Struct. Biotechnol. J. 2, e201209008–e201209008 (2012). · DOI
  33. Wagih, O. et al. Comprehensive variant effect predictions of single nucleotide variants in model organisms. Preprint at bioRxiv https://doi.org/10.1101/313031 (2018). · doi:10.1101/313031
  34. Mosca, R., Céol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013). · DOI · Paper page
  35. Van Durme, J. et al. A graphical interface for the FoldX forcefield. Bioinforma. Oxf. Engl. 27, 1711–1712 (2011). · DOI · Paper page
  36. Grossmann, A. et al. Phospho‐tyrosine dependent protein–protein interaction network. Mol. Syst. Biol. 11, 794 (2015). · DOI
  37. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–520 (2015). · DOI · Paper page
  38. Schaefer, M. H., Serrano, L. & Andrade-Navarro, M. A. Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front. Genet. 6, 260 (2015). · DOI
  39. Rahmati, S., Abovsky, M., Pastrello, C. & Jurisica, I. pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis. Nucleic Acids Res. 45, D419–D426 (2017). · DOI
  40. Schuster-Böckler, B. & Bateman, A. Protein interactions in human genetic diseases. Genome Biol. 9, R9 (2008). · DOI
  41. Woodsmith, J. et al. Protein interaction perturbation profiling at amino-acid resolution. Nat. Methods 14, 1213–1221 (2017). · DOI
  42. Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018). · DOI · Paper page
  43. Sahni, N. et al. Edgotype: a fundamental link between genotype and phenotype. Curr. Opin. Genet. Dev. 23, 649–657 (2013). · DOI
  44. Babu, M. M. The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem. Soc. Trans. 44, 1185–1200 (2016). · DOI · Paper page
  45. Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996). · DOI · Paper page
  46. den Dunnen, J. T. et al. HGVS Recommendations for the Description of Sequence Variants: 2016 Update. Hum. Mutat. 37, 564–569 (2016). · DOI · Paper page
  47. Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016). · DOI · Paper page
  48. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164–e164 (2010). · DOI · Paper page

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Metadata

Authors
The IMEx Consortium Curators, Khadake, J., Meldal, B., Panni, S., Thorneycroft, D., van Roey, K., Abbani, S., Salwinski, L., Pellegrini, M., Iannuccelli, M., Licata, L., Cesareni, G., Roechert, B., Bridge, A., Ammari, M. G., McCarthy, F., Broackes-Carter, F., Campbell, N. H., Melidoni, A. N., Rodriguez-Lopez, M., Lovering, R. C., Jagannathan, S., Chen, C., Lynn, D. J., Ricard-Blum, S., Mahadevan, U., Raghunath, A., del-Toro, N., Duesbury, M., Koch, M., Perfetto, L., Shrivastava, A., Ochoa, D., Wagih, O., Piñero, J., Kotlyar, M., Pastrello, C., Beltrao, P., Furlong, L. I., Jurisica, I., Hermjakob, H., Orchard, S., & Porras, P.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-01-02
Volume
10
Issue
1
Pages
10-10
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-018-07709-6
OpenAlex
W2949814610
Cited by
247
References
10
FWCI
11.46
Type
journal article
Language
en
Update notices
correction (2019-03-04)
Open access
gold
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The IMEx Consortium Curators, Khadake, J., Meldal, B., Panni, S., Thorneycroft, D., van Roey, K., Abbani, S., Salwinski, L., Pellegrini, M., Iannuccelli, M., Licata, L., Cesareni, G., Roechert, B., Bridge, A., Ammari, M. G., McCarthy, F., Broackes-Carter, F., Campbell, N. H., Melidoni, A. N., . . . Porras, P. (2019). Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set. Nature Communications. https://doi.org/10.1038/s41467-018-07709-6

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