Bioinformatics Vol. 17 no. 90001 2001
Pages S296-S305
© 2001 Oxford University Press
Protein-protein interaction map inference using interacting domain profile pairs
Hybrigenics S.A., 180 avenue Daumesnil, 75012 Paris, France
Received on February 6, 2001
; revised on April 2, 2001
; accepted on April 2, 2001
A number of predictive methods have been designed to predict protein interaction from sequence or expression data. On the experimental front, however, high-throughput proteomics technologies are starting to yield large volumes of protein-protein interaction data. High-quality experimental protein interaction maps constitute the natural dataset upon which to build interaction predictions. Thus the motivation to develop the first interaction-based protein interaction map prediction algorithm.
A technique to predict protein-protein interaction maps across organisms is introduced, the ‘interaction-domain pair profile’ method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. We apply this approach to the prediction of an interaction map of Escherichia coli from the recently published interaction map of the human gastric pathogen Helicobacter pylori. Results are compared with predictions of a second inference method based only on full-length protein sequence similarity — the "naive" method. The domain-based method is shown to i) eliminate a significant amount of false-positives of the naive method that are the consequences of multi-domain proteins; ii) increase the sensitivity compared to the naive method by identifying new potential interactions.
Availability: Contact the authors.
Contact: vschachter{at}hybrigenics.fr
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  A. Schlicker, C. Huthmacher, F. Ramirez, T. Lengauer, and M. Albrecht Functional evaluation of domain domain interactions and human protein interaction networks Bioinformatics, April 1, 2007; 23(7): 859 - 865. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Chen, W. Hsu, M. L. Lee, and S.-K. Ng Increasing confidence of protein interactomes using network topological metrics Bioinformatics, August 15, 2006; 22(16): 1998 - 2004. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Li, J. Li, and L. Wong Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale Bioinformatics, April 15, 2006; 22(8): 989 - 996. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Kim, M. Koyuturk, U. Topkara, A. Grama, and S. Subramaniam Inferring functional information from domain co-evolution Bioinformatics, January 1, 2006; 22(1): 40 - 49. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X.-W. Chen and M. Liu Prediction of protein-protein interactions using random decision forest framework Bioinformatics, December 15, 2005; 21(24): 4394 - 4400. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. R. Brown and I. Jurisica Online Predicted Human Interaction Database Bioinformatics, May 1, 2005; 21(9): 2076 - 2082. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Droit, G. G Poirier, and J. M Hunter Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function J. Mol. Endocrinol., April 1, 2005; 34(2): 263 - 280. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D.-S. Han, H.-S. Kim, W.-H. Jang, S.-D. Lee, and J.-K. Suh PreSPI: a domain combination based prediction system for protein-protein interaction Nucleic Acids Res., December 1, 2004; 32(21): 6312 - 6320. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Deng, S. Mehta, F. Sun, and T. Chen Inferring Domain-Domain Interactions From Protein-Protein Interactions Genome Res., October 1, 2002; 12(10): 1540 - 1548. [Abstract] [Full Text] [PDF]
|
|