Systematic
prediction of human membrane receptor interactions
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 |
| ==> ISMB 2010 Highlighted Task : talk slide PDF |
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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) |
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Other global analyses of HMRI (besides
contents of above six supp-documents; HMRI: human membrane receptor
interactome): ·
Module analysis of the predicted receptor
interactome (from biclustering) ·
Graph properties analysis of the predicted
receptor interactome ·
Subgraphs
of HMRI in Cytoscape format
(could directly import into Cytoscape
for Visualization) ·
Multiple
kinds of hub protein lists of the predicted HMRI (cutoff 2.0) |
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Predictions Download (predicted interaction scores for all
human membrane receptors) ·
Gene
list of human membrane receptors and their associated family information ·
About
the download: README (each receptor has its own predicted
interaction score file) ==> Downloading List of All Prediction Files per Receptor ·
File
directory I to download all
prediction files (files larger than
2M) - README ·
File
directory II to download all
prediction files (files smaller than
2M) - README ·
Prediction
File
format readme ·
Here we provide another file including the predicted scores for all potential pairs (receptor to human proteins):
Predicted RF Scores for all potential receptor to human protein pairs ·
Predicted
human membrane receptor interactome with cutoff 2.0 (Cytoscape fileformat) Software Download (both source-code and runnable
versions provided): ·
A
software could be used to predict any sub-network of the human
protein-protein interactions (based on user provided human protein list) ·
A
Java based GUI interface version and a perl based command-line batch version
are provided. |
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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: 1 2Department of Structural Biology, 3Department of Otolaryngology, Eye and Ear Institute,
University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA 4Institute for Structural Biology (IBI-2), 5Department of Surgery, §
contact authors |