Towards the prediction of complete protein--protein interaction networks.
We present a statistical method for the prediction of protein--protein interactions within an organism. This approach is based on the treatment of proteins as collections of conserved domains, where each domain is responsible for a specific interaction with another domain. By characterizing the frequency with which specific domain--domain interactions occur within known interactions, our model can assign a probability to an arbitrary interaction between any two proteins with defined domains. Domain interaction data is complemented with information on the topology of a network and is incorporated into the model by assigning greater probabilities to networks displaying more biologically realistic topologies. We use Markov chain Monte Carlo techniques for the prediction of posterior probabilities of interaction between a set of proteins; allowing its application to large data sets. In this work we attempt to predict interactions in a set of 40 human proteins, known to form a connected network, and discuss methods for future improvement.
Gomez
Shawn M
SM
Columbia Genome Center, Columbia University, 1150 St. Nicholas Avenue, Unit 109, New York, NY 10032, USA. smg42,ar345@columbia.edu
Rzhetsky
Andrey
A
D008390:Markov Chains; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011336:Probability; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
D011506:Proteins