Inference of bacterial protein-protein interactions from multi-species sequence data

Experimental approaches to transient protein interactions are laborious, and our understanding of fundamental questions like the identification of interaction surfaces or the specificity of molecular recognition between interacting proteins is far from being complete. We propose a computational approach based on techniques from statistical physics, which exploits correlations in the natural sequence variability in families of homologous proteins across hundreds of species. Using bacterial two-component signal transduction as a test case, we show that our method is able (i) to identify inter-protein residue contacts and to facilitate the prediction of protein complex strutures, and (ii) to reconstruct a molecular recognition code which elucidates specificity in signal transduction in bacteria.