Systematic prediction of human membrane receptor
Yanjun Qi[1]1, Harpreet K. Dhiman2, Neil Bhola3, Ivan Budyak4, Siddhartha Kar5, David Man2, Arpana Dutta2, Kalyan Tirupula2, Brian I. Carr5, Jennifer Grandis3,� Ziv Bar-Joseph1� and Judith Klein-Seetharaman1,2,4�
@ PROTEOMICS (2009)� (Published: 1 Oct 2009,9,5243-5255) & PDF version
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.
Supplementary Files (also in PROTEOMICS ):
� S1-Computational Methods.pdf
� S2-Computational Results.pdf
� S4-Analysis & Visualization.pdf
� S6-PredictedReceptorInteractomeRFcut1.xls�(S6 is the list of predicted human membrane receptor interactions in EXCEL spread sheet format. It includes both RF scores and p-values for each predicted pair)
Web Service HMRI :
� To enable other biomedical researchers to benefit from our human membrane receptor interactome, we have implemented our method as a web server �HMRI�
� (http://flan.blm.cs.cmu.edu/HMRI/)
� An interactive interface has been created that allows users the retrieval of interactions. Researchers interested in specific membrane receptors can enter individual or sets of proteins and retrieve their rank-ordered interactions, along with the evidence supporting these predictions.
(sorry. The "web service" site is down currently due the machine problem. You can access all of our predictions from the above file downloading link.)
� Affiliations:�
1School of Computer Science, Carnegie Mellon University,Pittsburgh, PA 15213, USA
2Department of Structural Biology, University of Pittsburgh School of Medicine,Pittsburgh, PA 15260, USA
3Department of Otolaryngology, Eye and Ear Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA� 15260, USA
4Institute for Structural Biology (IBI-2), Research Center J�lich 52425, Germany
5Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
� contact authors