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PLoS One. 2016 Feb 16;11(2):e0149166. doi: 10.1371/journal.pone.0149166. eCollection 2016.

Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon.

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Department of Applied Science and Technology, and Center for Computational Sciences, Politecnico di Torino, Torino, Italy.
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, United States of America.
Sorbonne Universités, UPMC, UMR 7238, Computational and Quantitative Biology, Paris, France.
CNRS, UMR 7238, Computational and Quantitative Biology, Paris, France.
Human Genetics Foundation, Molecular Biotechnology Center (MBC), Torino, Italy.


Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

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