Prediction of protein interaction based on similarity of phylogenetic trees.

Computational methods for predicting protein interaction partners are becoming increasingly popular. Many of them are mature enough to be widely used by molecular biologists who can look for proteins related to the protein of interest in order to infer information about its context in the cell. In this chapter we describe the use of the mirrortree set of programs and related software for predicting protein interactions. They are all based on the idea that interacting or functionally related proteins tend to show similar phylogenetic trees due to coevolution. The basic mirrortree program can be used to calculate the similarity between the phylogenetic trees implicit in the multiple sequence alignments of two protein families. The ECID database contains protein interactions and relationships from different computational and experimental sources for the model organism Escherichia coli, including the ones generated with mirrortree. Finally, the TSEMA server uses the concept of tree similarity between interacting families to look for the best mapping between two families of interacting proteins: which member in one family interacts with which member in the other.