Research Article

Systematic prediction of human membrane receptor interactions

  1. Yanjun Qi1,†,
  2. Harpreet K. Dhiman2,
  3. Neil Bhola3,
  4. Ivan Budyak4,
  5. Siddhartha Kar5,
  6. David Man2,
  7. Arpana Dutta2,
  8. Kalyan Tirupula2,
  9. Brian I. Carr5,
  10. Jennifer Grandis3,
  11. Ziv Bar-Joseph1,
  12. Judith Klein-Seetharaman1,2,4,*

Article first published online: 1 OCT 2009

DOI: 10.1002/pmic.200900259

Issue

PROTEOMICS

PROTEOMICS

Volume 9, Issue 23, pages 5243–5255, No. 23 December 2009

Additional Information

How to Cite

Qi, Y., Dhiman, H. K., Bhola, N., Budyak, I., Kar, S., Man, D., Dutta, A., Tirupula, K., Carr, B. I., Grandis, J., Bar-Joseph, Z. and Klein-Seetharaman, J. (2009), Systematic prediction of human membrane receptor interactions. PROTEOMICS, 9: 5243–5255. doi: 10.1002/pmic.200900259

Author Information

  1. 1

    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

  2. 2

    Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

  3. 3

    Department of Otolaryngology, Eye and Ear Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

  4. 4

    Institute for Structural Biology (IBI-2), Research Center Jülich, Germany

  5. 5

    Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

  1. †

    NEC Labs America Inc, 4 Independence Way, Princeton, NJ 08540, USA

*Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15217, USA Fax: +1-412-648-8998

Publication History

  1. Issue published online: 16 DEC 2009
  2. Article first published online: 1 OCT 2009
  3. Manuscript Accepted: 11 AUG 2009
  4. Manuscript Revised: 31 JUL 2009
  5. Manuscript Received: 22 APR 2009

Funded by

  • National Science Foundation. Grant Numbers: ITR 0225656, CAREER 0448453, CAREER CC044917
  • National Institutes of Health. Grant Numbers: NLM108730, R01 CA098372, AI060422
  • Sofya Kovalvskaya Award

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Keywords:

  • Data integration;
  • Membrane proteins;
  • Protein–protein interaction network;
  • Receptor interactome;
  • Receptor crosstalk;
  • Signal transduction;
  • Systems biology

Abstract

Membrane receptor-activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high-throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system-wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.

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