We propose a new framework that can exploit topological and biological information to predict protein-protein interactions. The algorithm relies on the underlying hypothesis that two proteins interact in proportion to the structural similarity that one has with the most similar of the interactors of the other. From a topological perspective, the structural similarity between two proteins is proportional to the number of common neighbors. Instead, from a biological perspective, the structural similarity between two proteins is proportional to the similarity of their primary sequences.