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In silico prediction of physical protein interactions and characterization of interactome orphans

Nature Methods volume 12, pages 79–84 (2015)Cite this article

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Abstract

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining–based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

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Figure 1: FpClass workflow.
Figure 2: Evaluation of FpClass using experimental PPI data sets.
Figure 3: Experimental validation of FpClass predictions.
Figure 4: Properties of d0 (orphan) proteins and genes.

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  • 10 December 2014

    In the version of this article initially published online, an author (G.B.M.) was incorrectly listed twice. The error has been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

This research was supported by the grants from Genome Canada via the Ontario Genomics Institute, Ontario Research Fund (GL2-01-030, RE-03-020 to I.J.), the Canadian Institutes for Health Research (#99745, #93579 to I.J., A.J.), the Natural Sciences Research Council (#203475 to I.J.), US Army Department of Defense W81XWH-12-1-0501 (to I.J.), the Italian Association for Cancer Research, the Friuli Venezia-Giulia and CRO 5xmille Intramural Grant (to R.M.), the Friuli Venezia-Giulia Exchange Program (to C.P.), the Ontario Genomics Institute (#303547 to I.S.), the Canadian Institutes of Health Research (Catalyst-NHG99091, PPP-125785 to I.S.), the Canadian Cystic Fibrosis Foundation (#300348 to I.S.), the Canadian Cancer Society (2010-700406 to I.S.), Genentech and University Health Network (GL2-01-018 to I.S.), US National Institutes of Health (NIH) PO1/PPG grant 01CA0099031 (to G.B.M., I.J.) and NCI R21 CA126700 (to Z.D., G.B.M.). Computational resources were supported by grants from the Canada Foundation for Innovation (CFI #12301, #203373, #29272, #22540a, #30865) and IBM (to I.J.). I.J. is supported by the Canada Research Chair program. This research was also supported by the University of Toronto McLaughlin Centre and the Ontario Ministry of Health and Long-Term Care (OMOHLTC). The views expressed do not necessarily reflect those of the OMOHLTC. We thank M. Vidal, D. Hill, F. Roth and the Center for Cancer Systems Biology (Dana-Farber Cancer Institute) for prepublication release of protein interaction data, funded by NIH NHGRI grant R01 HG001715.

Author information

Authors and Affiliations

  1. Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada

    Max Kotlyar, Chiara Pastrello, Christian Cumbaa, Yun Niu, Fatemeh Vafaee, Fiona Broackes-Carter & Igor Jurisica

  2. Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy

    Chiara Pastrello, Flavia Pivetta, Alessandra Lo Sardo & Roberta Maestro

  3. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

    Han Li, Taline Naranian & Andrea Jurisicova

  4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Yun Niu

  5. Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

    Zhiyong Ding & Gordon B Mills

  6. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Fatemeh Vafaee

  7. Departments of Molecular Genetics and Biochemistry, Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Julia Petschnigg & Igor Stagljar

  8. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Igor Jurisica

  9. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Igor Jurisica

  10. TECHNA Institute for the Advancement of Technology for Health, Toronto, Ontario, Canada

    Igor Jurisica

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  1. Max Kotlyar
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Contributions

M.K. and I.J. conceived of the project. M.K. developed the algorithm and executed computational analyses and validation. Additional validation and assay-related analyses were executed by C.P., C.C., Y.N., F.V. and F.B.-C. R.M., I.S., A.J. and G.B.M. provided guidance for biological validation experiments that were executed by F.P., A.L.S., H.L., C.P., T.N. and Z.D. M.K. and I.J. wrote the initial manuscript, and all authors were involved in results presentation, discussion and preparation of the final manuscript.

Corresponding author

Correspondence to Igor Jurisica.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Estimating FDR of PPI predictions.

(a-b) We used the approach of D'Haeseleer and Church1 to estimate FDR. This approach calculates the FDR of a PPI dataset, D, by analyzing intersections among three PPI datasets, D, R, and D′, where R is a reference set of trusted PPIs and D′ is a set of PPIs from a method similar to that of D. It is assumed that the overlap of any two datasets contains largely true positive PPIs. The number of non-overlapping true positives, IV, is calculated from the numbers of shared PPIs: IV = (II × III) / I. Then, the number of false positives, V, and the FDR are calculated. The FDR tends to be low if D has a high overlap with either D′ or R. (c)To calculate the FDR of FpClass we initially set D to our top 35,000 proteome-wide predictions, excluding any PPIs used in training; (we subsequently calculated FDR for larger sets of FpClass predictions (panels d-g)). We defined R as a set of experimentally detected interactions and D′ as the union of high confidence predictions from previous studies by Rhodes et al., 2005, Scott et al., 2007, Elefsinioti et al., 2011, and Zhang et al., 2012. Using a similar approach, we calculated FDRs for high-confidence predictions from these previous studies. For example, to calculate the FDR for Rhodes et al., we defined D as high-confidence predictions from that study, and D′ as the union of top FpClass predictions and high-confidence predictions from the three remaining previous studies. To ensure that estimated FDRs were not due to biases of a particular reference set, we repeated FDR calculations using 6 reference sets. We calculated FDRs using each reference set, except when the intersection of datasets D, D', and R comprised less than 5 PPIs. In such cases the FDR is indicated as NA. (d-g) Using the approach of D'Haeseleer and Church, we estimated FDRs of predicted networks of various sizes from FpClass and four previous prediction methods. The approach of D'Haeseleer and Church requires a trusted reference set of PPIs. We tried four ways of defining this set: (d) using six reference sets (panel c) individually, and then calculating the median of the six resulting FDR estimates, (e) using the union of PPIs from methods that detect direct interactions (Y2H and LUMIER reference sets), (f) using the union of our six reference sets, and (g) using the union of Y2H reference sets.

1D’haeseleer, P. & Church, G. M. Estimating and improving protein interaction error rates. Proc IEEE Comput Syst Bioinform Conf 216–223 (2004). 2Rhodes, D. R. et al. Probabilistic model of the human protein-protein interaction network. Nat Biotechnol 23, 951–959 (2005). 3Scott, M. S. & Barton, G. J. Probabilistic prediction and ranking of human protein-protein interactions. BMC Bioinformatics 8, 239 (2007). 4Elefsinioti, A. et al. Large-scale de novo prediction of physical protein-protein association. Mol Cell Proteomics 10, M111.010629 (2011). 5Zhang, Q. C. et al. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490, 556–560 (2012).

Supplementary Figure 2 Experimental validation of PPI predictions.

(a) Predicted interactions tested by Co-IP assays.

(b-c) Predicted interactions tested by GST pull-down assays.

(d) Predicted interaction partners of p53 include some of its known partners and d0 proteins. The x-axis indicates the number of top predicted partners, ranked from 1 to 2377. The y-axis indicates the number of known partners and d0 proteins, among the top predicted partners.

Supplementary Figure 3 Top Gene Ontology (GO) categories among d0 genes.

(a-c) GO analysis includes genes without GO annotations. (d-f) GO analysis excludes genes without GO annotations. P-values were calculated by hypergeometric tests and adjusted for multiple testing using FDR.

Supplementary Figure 4 Percentages of d0 proteins in drug-target classes and structural properties of d0 proteins.

(a) Main drug target classes and (b) receptor drug target classes, as defined by Imming et al.6. Dashed lines indicate the percentage of d0 proteins in the proteome. P-values were calculated by hypergeometric tests and adjusted for multiple testing using FDR. (c) SCOP structural classes. P-values were calculated by hypergeometric tests and adjusted for multiple testing using FDR. (d) Protein lengths from UniProt8 and (e) protein disorder, predicted with DISOPRED9. P-values for protein length and disorder were calculated by two-sided Mann-Whitney U tests.

6Imming, P., Sinning, C. & Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov 5, 821–834 (2006). 7Andreeva, A. et al. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 36, D419–25 (2008). 8The UniProt Consortium. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res 38, D142–8 (2010). 9Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F. & Jones, D. T. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337, 635–645 (2004).

Supplementary Figure 5 Median and maximum expression of d0- and dk-encoding genes.

P-values were calculated by two-sided Mann-Whitney U tests. (a-d) Median expression of d0 and dk genes in healthy human tissues. Gene expression data was taken from (a) Su et al., 200410, (b) Roth et al., 200611, (c) Wang et al., 200812, and (d) Krupp et al., 201213. (e-h) Maximum expression of d0 and dk genes in the same datasets.

10Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101, 6062–6067 (2004). 11Roth, R. B. et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 7, 67–80 (2006). 12Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008). 13Krupp, M. et al. RNA-Seq Atlas--a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 28, 1184–1185 (2012).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1–10 and Supplementary Note (PDF 2171 kb)

Supplementary Software

FpClass code (ZIP 239668 kb)

Supplementary Data 1

Positive cases in our largest training set. (TXT 279 kb)

Supplementary Data 2

Predicted probabilities for protein pairs from Braun et al., 2009 (TXT 8 kb)

Supplementary Data 3

Cross-validation data: protein pairs and predicted probabilities. (TXT 48197 kb)

Supplementary Data 4

Experimentally tested predictions. (TXT 12 kb)

Supplementary Data 5

Fp60 network: predicted interactions with estimated FDR of 60%. (TXT 8784 kb)

Supplementary Data 6

D0 proteins: human proteins without experimentally detected interactions in I2D 1.95. (TXT 49 kb)

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Kotlyar, M., Pastrello, C., Pivetta, F. et al. In silico prediction of physical protein interactions and characterization of interactome orphans. Nat Methods 12, 79–84 (2015). https://doi.org/10.1038/nmeth.3178

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