Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Interactome INSIDER: a structural interactome browser for genomic studies

Abstract

We present Interactome INSIDER, a tool to link genomic variant information with structural protein–protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The current size of structural interactomes.
Figure 2: ECLAIR prediction results.
Figure 3: Workflow for calculating mutation and variant enrichment using Interactome INSIDER (http://interactomeinsider.yulab.org).
Figure 4: Functional properties of predicted interfaces.
Figure 5: Interaction-partner-specific interface prediction.
Figure 6: The hypertrophic cardiomyopathy (HCM) pathway.

References

  1. 1

    Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Arabidopsis Interactome Mapping Consortium. Evidence for network evolution in an Arabidopsis interactome map. Science 333, 601–607 (2011).

  3. 3

    Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Vo, T.V. et al. A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human. Cell 164, 310–323 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Das, J. & Yu, H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).

    PubMed  PubMed Central  Google Scholar 

  6. 6

    Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Kim, P.M., Lu, L.J., Xia, Y. & Gerstein, M.B. Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314, 1938–1941 (2006).

    CAS  PubMed  Google Scholar 

  8. 8

    Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159–164 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Kühlbrandt, W. Cryo-EM enters a new era. eLife 3, e03678 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10

    Halperin, I., Ma, B., Wolfson, H. & Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47, 409–443 (2002).

    CAS  PubMed  Google Scholar 

  11. 11

    Šali, A. & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    PubMed  Google Scholar 

  12. 12

    Mosca, R., Céol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013).

    CAS  PubMed  Google Scholar 

  13. 13

    Hopf, T.A. et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3, 03430 (2014).

    Google Scholar 

  14. 14

    Hwang, H., Vreven, T. & Weng, Z. Binding interface prediction by combining protein-protein docking results. Proteins 82, 57–66 (2014).

    CAS  PubMed  Google Scholar 

  15. 15

    Zhang, Q.C. et al. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490, 556–560 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Garzón, J.I. et al. A computational interactome and functional annotation for the human proteome. eLife 5, 18715 (2016).

    Google Scholar 

  17. 17

    Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl. Acad. Sci. USA 108, E1293–E1301 (2011).

    CAS  PubMed  Google Scholar 

  18. 18

    Lockless, S.W. & Ranganathan, R. Evolutionarily conserved pathways of energetic connectivity in protein families. Science 286, 295–299 (1999).

    CAS  PubMed  Google Scholar 

  19. 19

    Bergstra, J.S., Bardenet, R., Bengio, Y. & Kégl, B. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems (eds. Shawe-Taylor, T et al.) 2546–2554 (NIPS, 2011).

  20. 20

    Kufareva, I., Budagyan, L., Raush, E., Totrov, M. & Abagyan, R. PIER: protein interface recognition for structural proteomics. Proteins 67, 400–417 (2007).

    CAS  PubMed  Google Scholar 

  21. 21

    Liang, S., Zhang, C., Liu, S. & Zhou, Y. Protein binding site prediction using an empirical scoring function. Nucleic Acids Res. 34, 3698–3707 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Porollo, A. & Meller, J. Prediction-based fingerprints of protein-protein interactions. Proteins 66, 630–645 (2007).

    CAS  PubMed  Google Scholar 

  23. 23

    de Vries, S.J. & Bonvin, A.M. CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS One 6, e17695 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Jordan, R.A., El-Manzalawy, Y., Dobbs, D. & Honavar, V. Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinformatics 13, 41 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Hwang, H., Vreven, T., Janin, J. & Weng, Z. Protein-protein docking benchmark version 4.0. Proteins 78, 3111–3114 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Maheshwari, S. & Brylinski, M. Predicting protein interface residues using easily accessible on-line resources. Brief. Bioinform. 16, 1025–1034 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Wei, X. et al. A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations. PLoS Genet. 10, e1004819 (2014).

    PubMed  PubMed Central  Google Scholar 

  28. 28

    Stenson, P.D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1–9 (2014).

    CAS  PubMed  Google Scholar 

  29. 29

    Landrum, M.J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).

    CAS  PubMed  Google Scholar 

  30. 30

    Forbes, S.A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

    CAS  PubMed  Google Scholar 

  31. 31

    Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

    CAS  PubMed  Google Scholar 

  32. 32

    1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  33. 33

    UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

  34. 34

    Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Meyer, M.J. et al. mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Hum. Mutat. 37, 447–456 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Hopf, T.A. et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 35, 128–135 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    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, 359–363 (2012).

    CAS  PubMed  Google Scholar 

  39. 39

    Wang, R.N. et al. Bone Morphogenetic Protein (BMP) signaling in development and human diseases. Genes Dis. 1, 87–105 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Roth, S. et al. SMAD genes in juvenile polyposis. Genes Chromosom. Cancer 26, 54–61 (1999).

    CAS  PubMed  Google Scholar 

  41. 41

    Ngeow, J. et al. Exome sequencing reveals germline SMAD9 mutation that reduces phosphatase and tensin homolog expression and is associated with hamartomatous polyposis and gastrointestinal ganglioneuromas. Gastroenterology 149, 886–889 e5 (2015).

    CAS  PubMed  Google Scholar 

  42. 42

    Maron, B.J. Hypertrophic cardiomyopathy: a systematic review. J. Am. Med. Assoc. 287, 1308–1320 (2002).

    Google Scholar 

  43. 43

    Donkervoort, S. et al. Cardiomyopathy in patients with ACTA1-myopathy. Neuromuscul. Disord. 25, S287 (2015).

    Google Scholar 

  44. 44

    Sparrow, J.C. et al. Muscle disease caused by mutations in the skeletal muscle alpha-actin gene (ACTA1). Neuromuscul. Disord. 13, 519–531 (2003).

    PubMed  Google Scholar 

  45. 45

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Forbes, S.A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).

    CAS  PubMed  Google Scholar 

  47. 47

    Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Tas¸ an, M. et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods 12, 154–159 (2015).

    Google Scholar 

  50. 50

    Kamburov, A. et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc. Natl. Acad. Sci. USA 112, E5486–E5495 (2015).

    CAS  PubMed  Google Scholar 

  51. 51

    Kucukkal, T.G., Petukh, M., Li, L. & Alexov, E. Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins. Curr. Opin. Struct. Biol. 32, 18–24 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Li, M., Petukh, M., Alexov, E. & Panchenko, A.R. Predicting the impact of missense mutations on protein-protein binding affinity. J. Chem. Theory Comput. 10, 1770–1780 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Lounnas, V. et al. Current progress in structure-based rational drug design marks a new mindset in drug discovery. Comput. Struct. Biotechnol. J. 5, e201302011 (2013).

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Peng, K., Obradovic, Z. & Vucetic, S. Exploring bias in the Protein Data Bank using contrast classifiers. Pac. Symp. Biocomput. 2004, 435–446 (2004).

    Google Scholar 

  55. 55

    Dunker, A.K. et al. The unfoldomics decade: an update on intrinsically disordered proteins. BMC Genomics 9, S1 (2008).

    PubMed  PubMed Central  Google Scholar 

  56. 56

    Orchard, S. et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat. Methods 9, 345–350 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Kerrien, S. et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841–D846 (2012).

    CAS  PubMed  Google Scholar 

  59. 59

    Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857–D861 (2012).

    CAS  PubMed  Google Scholar 

  60. 60

    Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43, D470–D478 (2015).

    CAS  PubMed  Google Scholar 

  61. 61

    Turner, B. et al. iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford) 2010, baq023 (2010).

    Google Scholar 

  62. 62

    Keshava Prasad, T.S. et al. Human Protein Reference Database--2009 update. Nucleic Acids Res. 37, D767–D772 (2009).

    CAS  PubMed  Google Scholar 

  63. 63

    Mewes, H.W. et al. MIPS: curated databases and comprehensive secondary data resources in 2010. Nucleic Acids Res. 39, D220–D224 (2011).

    CAS  PubMed  Google Scholar 

  64. 64

    Alfarano, C. et al. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 33, D418–D424 (2005).

    CAS  PubMed  Google Scholar 

  65. 65

    Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes--2009. Nucleic Acids Res. 38, D497–D501 (2010).

    CAS  PubMed  Google Scholar 

  66. 66

    Güldener, U. et al. MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res. 34, D436–D441 (2006).

    PubMed  Google Scholar 

  67. 67

    Brown, K.R. & Jurisica, I. Online predicted human interaction database. Bioinformatics 21, 2076–2082 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Pagel, P. et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 21, 832–834 (2005).

    CAS  PubMed  Google Scholar 

  69. 69

    Hermjakob, H. et al. The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004).

    CAS  PubMed  Google Scholar 

  70. 70

    Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Velankar, S. et al. SIFTS: Structure Integration with Function, Taxonomy and Sequences resource. Nucleic Acids Res. 41, D483–D489 (2013).

    CAS  PubMed  Google Scholar 

  72. 72

    Lee, B. & Richards, F.M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55, 379–400 (1971).

    CAS  PubMed  Google Scholar 

  73. 73

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  74. 74

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Google Scholar 

  75. 75

    Witten, I.H., Frank, E., Hall, M.A. & Pal, C.J. Data Mining: Practical Machine Learning Tools and Techniques (Elsevier Science, 2016).

  76. 76

    Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290–D301 (2012).

    CAS  PubMed  Google Scholar 

  77. 77

    Sørensen, T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol. Skr. 5, 1–34 (1948).

    Google Scholar 

  78. 78

    Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    CAS  PubMed  Google Scholar 

  79. 79

    Tyner, C. et al. The UCSC Genome Browser database: 2017 update. Nucleic Acids Res. 45 D1, D626–D634 (2017).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank G. Hooker, D. Bindel, and K. Weinberger for helpful discussions and J. VanEe for technical support. This work was supported by National Institute of General Medical Sciences grants (R01 GM097358, R01 GM104424, R01 GM124559); National Cancer Institute grant (R01 CA167824); Eunice Kennedy Shriver National Institute of Child Health and Human Development grant (R01 HD082568); National Human Genome Research Institute grant (UM1 HG009393); National Science Foundation grant (DBI-1661380); and Simons Foundation Autism Research Initiative grant (367561) to H.Y.

Author information

Affiliations

Authors

Contributions

M.J.M., J.F.B., S.L., and H.Y. conceived the study. H.Y. oversaw all aspects of the study. M.J.M., J.F.B., S.L., and A.R. performed computational analyses. M.J.M. and J.F.B. designed ECLAIR. J.F.B. designed the web interface. R.F., J.L., and X.W. performed laboratory experiments. M.J.M. wrote the manuscript with input from J.F.B., S.L., and H.Y. All authors edited and approved of the final manuscript.

Corresponding author

Correspondence to Haiyuan Yu.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Features for predicting protein interaction interfaces.

(a) A schematic showing the five feature categories from which feature sets are optimized to train ECLAIR. (b) The portions of high-quality binary interactomes for which each feature type is available. (c) Feature aggregation strategies employed for combining multiple points of evidence into single co-evolution- and structure-based features. For co-evolution, we select the top co-evolved residue, the mean of features for the top 10 co-evolved residues, or the mean over all co-evolved residues in the partner protein. For proteins with multiple structures, we take the mean, minimum, or maximum SASA over all available structures.

Supplementary Figure 2 Testing set-training set feature balance.

Balance between testing/training and prediction sets of sequence- and structure-based feature depths. (a) Sources (PDB or ModBase) and number of structures used to calculate solvent-accessible surface area. (b) Number of homologous sequences used to calculate evolutionary features. (c) Sources of docked models for calculating docking-based features.

Supplementary Figure 3 A comparison of two methods for handling missing data in classification.

A comparison of (1) imputation and (2) an ensemble of fully-trained classifiers for handling missing data. During training, imputation must fill in gaps in feature coverage, whereas an ensemble trains independent classifiers on each feature-availability scenario. Since structural feature coverage is highly correlated with the existence of known interface residues in training, imputation will fail to predict interface residues outside of regions with structural feature coverage (red). An ensemble will predict interface residues based only on the features available and will not be biased by the missing structural feature.

Supplementary Figure 4 Training and optimizing the ECLAIR classifier.

(a) Training the ECLAIR classifier. (b) Four methods for optimizing machine learning algorithm hyperparameters, showing the order of trials and granularity of hyperparameter sampling spaces for optimizing two hyperparameters. (c) Cross-validation strategy using TPE to optimize hyperparameters and window sizes for both feature selection and ensemble classifier training. (d) Cross-validation results using TPE trials to select top performing feature or set of features (in red) in each feature category. (e) Comparison of four hyperparameter optimization methods’ performance (top panel) and hyperparameter and residue window sampling patterns (bottom panels) on one of the eight sub-classifiers of the ECLAIR ensemble.

Supplementary Figure 5 ECLAIR predictions.

(a) Number of residues predicted in each prediction confidence category. (b) Cumulative distribution of interactions with ≥ n residues classified as interface for each of the highest interface potential categories.

Supplementary Figure 6 Performance of ECLAIR sub-classifiers on testing set.

(a) Receiver operating characteristic (ROC) curves for each sub-classifier. (b) Precision-recall curves for each sub-classifier. (c) Distribution of raw prediction scores for each sub-classifier. For all panels, sub-classifiers plotted in blue used only sequence-based features; sub-classifiers in red used additional structure-based features. (d) Raw prediction scores compared to actual probabilities of residues in each bin to be at the interface.

Supplementary Figure 7 ROC and precision-recall curves comparing ECLAIR with other popular interface residue prediction methods.

Here, only known surface residues were used in benchmarking all methods. All methods have a slightly lower AUROC (since it is more difficult to distinguish interface from non-interface among only surface residues), however ECLAIR still performs as well or better than all tested methods.

Supplementary Figure 8 Genomic properties of predicted interface residues in interactions lacking structural features.

(a) Enrichment of disease mutations in predicted and known interfaces. (b) Enrichment of recurrent cancer mutations in predicted and known interfaces. (c) Enrichment of rare and common population variants in predicted and known interfaces. (d) Predicted deleteriousness of population variants in known and predicted interfaces (using PolyPhen-2). (e) Predicted effects of population variants in known and predicted interfaces (using EVmutation). (In a-b, significance determined by two-sided Z-test. In d-e, significance determined by a two-sided U-test. n.s. denotes not significant)

Supplementary Figure 9 Genomic properties of predicted interface domains.

(a) Enrichment of disease mutations in predicted and known interfaces. (b) Enrichment of recurrent cancer mutations in predicted and known interfaces. (c) Enrichment of rare and common population variants in predicted and known interfaces. (d) Predicted deleteriousness of population variants in known and predicted interfaces (using PolyPhen-2). (e) Predicted effects of population variants in known and predicted interfaces (using EVmutation). (In a-b, significance determined by two-sided Z-test. In d-e, significance determined by a two-sided U-test)

Supplementary Figure 10 Other properties of predicted interface domains.

(a-c) Precision recall curves for interfaces predicted with ECLAIR: (a) interface residues in all benchmarked interactions, (b) interface residues in interactions lacking structural features, and (c) interface domains in interactions lacking structural features. (d) Fraction of interface residues localized to domains for known interface residues in co-crystalized co-bound proteins, predicted interface residues in interactions with structural features, and predicted interface residues in interactions without structural features. (e) Enrichment of human disease mutations in domains determined by known interface residues in co-crystalized co-bound proteins, predicted interface residues in interactions with structural features, and predicted interface residues in interactions without structural features. (Significance determined by two-sided Z-test)

Supplementary Figure 11 Uncropped gel image from Figure 5a.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 and Supplementary note 1–7 (PDF 3047 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Table 1

Comparison of ECLAIR using docking benchmark 4.0 (XLSX 12 kb)

Supplementary Table 2

PSI-MI binary evidence codes (XLSX 14 kb)

Supplementary Table 3

Training and Testing Sets (XLSX 141 kb)

Supplementary Table 4

Feature Selection (XLSX 16 kb)

Supplementary Table 5

Full sub-classifier training (XLSX 10 kb)

Supplementary Table 6

Comparison of ECLAIR performance with and without co-evolution (XLSX 11 kb)

Supplementary Table 7

ECLAIR prediction category performance using docking benchmark 4.0 (XLSX 9 kb)

Supplementary Table 8

Initially-trained ECLAIR vs. fully-trained ECLAIR performance (XLSX 11 kb)

Supplementary Software

ÉCLAIR software (ZIP 127 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meyer, M., Beltrán, J., Liang, S. et al. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 15, 107–114 (2018). https://doi.org/10.1038/nmeth.4540

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing