Section: New Results

Proteins structures and interactions

The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose in [9] an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. New estimators of the coevolution between proteins have been developed and combined them in an ensemble learning procedure.

This method has been applied to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. Our method allows prediction of PPI with an unprecedented precision of $95.5$% for the first 200 sorted pairs of proteins compared to $28.5$% on the same dataset with the previous best method.

A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.