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Probabilistic model of the human protein-protein interaction network

Nature Biotechnology volume 23, pages 951–959 (2005)Cite this article

Abstract

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans—a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

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Figure 1: Diverse genomic and proteomic data sources contribute to the predictive modeling of human protein-protein interactions.
Figure 2: Data integration in a semi-naïve Bayes model to predict human protein-protein interactions.
Figure 3: Characterization and performance analysis of the predicted interactome.
Figure 4: Global and focused views of the predicted human interactome.
Figure 5: Experimental confirmation of two predicted interactions implicates uncharacterized proteins into specific pathways.

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Acknowledgements

We thank R. Varambally for database assistance, D. Gibbs for hardware support, and the Institute of Bioinformatics for making the Human Protein Reference Database available. This work was funded by pilot funds from the Dean's Office, Department of Pathology, Cancer Center Support Grant P30 CA46592, and the Bioinformatics Program. D.R.R. and S.A.T. are fellows of the Medical Scientist Training Program, D.R.R. was funded by the Cancer Biology Training Program and A.M.C. is a Pew Scholar. A.P. is chief scientific advisor to the Institute of Bioinformatics. The terms of this arrangement are being managed by the Johns Hopkins University in accordance with its conflict of interest policies.

Author information

Author notes

  1. Daniel R Rhodes, Scott A Tomlins and Sooryanarayana Varambally: These authors contributed equally to this work.

Authors and Affiliations

  1. Bioinformatics Program, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Daniel R Rhodes & Arul M Chinnaiyan

  2. Department of Pathology, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Daniel R Rhodes, Scott A Tomlins, Sooryanarayana Varambally, Vasudeva Mahavisno, Terrence Barrette, Shanker Kalyana-Sundaram & Arul M Chinnaiyan

  3. Department of Biostatistics, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Debashis Ghosh

  4. Department of Urology, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Arul M Chinnaiyan

  5. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, 48109, Michigan, USA

    Arul M Chinnaiyan

  6. Mc-Kusick-Nathans Institute of Genetic Medicine and the Department of Biological Chemistry, Johns Hopkins University, Baltimore, 21205, Maryland, USA

    Akhilesh Pandey

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Supplementary information

Supplementary Figure 1

Duplicate representation of Figure 4a, but including gene symbols on zoom. (PDF 4326 kb)

Supplementary Figure 2

Protein-protein interactions among proteins encoded by transcripts that are over-expressed in pancreatic cancer. (PDF 951 kb)

Supplementary Figure 3

Interactions among proteins encoded by transcripts that are overexpressed in multiple myeloma. (PDF 3044 kb)

Supplementary Figure 4

Interactions among proteins encoded by transcripts that are overexpressed in clear cell renal cell carcinoma (RCC). (PDF 1346 kb)

Supplementary Table 1

Predicting human protein-protein interactions from model organism interactions. (PDF 13 kb)

Supplementary Table 2

Predicting human protein-protein interactions from gene co-expression. (PDF 14 kb)

Supplementary Table 3

Predicting human protein-protein interactions from gene co-expression. (PDF 13 kb)

Supplementary Table 4

Predicting human protein-protein interactions from shared biological function as defined by Gene Ontology Biological Process annotations. (PDF 10 kb)

Supplementary Table 5

Predicting human protein-protein interactions from domain pair enrichment as defined by Interpro protein domains and families and known interactions from the GSP. (PDF 10 kb)

Supplementary Table 6

Predicting human protein-protein interactions from both shared biological function and domain pair enrichment. (TXT 59 kb)

Supplementary Table 7

Model performance and validation. (PDF 453 kb)

Supplementary Table 8

Model performance and validation treating functional annotation and domain enrichment as conditionally independent. (PDF 285 kb)

Supplementary Table 9

Primer sequences for cloning. (TXT 1056 kb)

Supplementary Table 10

Model performance and validation. (PDF 351 kb)

Supplementary Table 11

Primer sequences for cloning. (PDF 214 kb)

Supplementary Methods

(PDF 45 kb)

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Rhodes, D., Tomlins, S., Varambally, S. et al. Probabilistic model of the human protein-protein interaction network. Nat Biotechnol 23, 951–959 (2005). https://doi.org/10.1038/nbt1103

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