Understanding relationships among proteins is crucial to understand the molecular machinery of the cell. Computational tools to predict domain-domain interactions provide a detailed molecular view of the protein interactions and complements expensive and laborious experimental techniques to identify such interactions. The evolutionary distances of interacting proteins often display a higher level of similarity than those of non-interacting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitute the basis of a method to predict protein interactions known as mirrortree. In a recent publication, we showed that binding neighborhoods of interacting proteins have, on average, higher co-evolutionary signal compared to the regions outside binding sites; although when the binding neighborhood was removed, the remaining domain sequence still contained some co-evolutionary signal. We have several projects focus on the investigation of the role of compensatory mutations in protein co-evolution and which are shading light on the process of co-evolution of interacting proteins. Kann, MG. Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefings in bioinformatics 11, 96-110 (2010). Kann, MG, Shoemaker, BA, Panchenko, AR & Przytycka, TM. Correlated evolution of interacting proteins: looking behind the mirrortree. Journal of molecular biology 385, 91-98 (2009). Kann, MG, Jothi, R, Cherukuri, PF & Przytycka, TM. Predicting protein domain interactions from coevolution of conserved regions. Proteins 67, 811-820 (2007).