Globally the energy landscape of a folding protein resembles a partially rough funnel. The local roughness of the funnel reflects transient trapping of the protein configurations in local free energy minima. A consequence of minimizing energetic frustration is that the topology of the native fold also plays a major role in the folding mechanism. Some folding motifs are easier to design than others suggesting the possibility that evolution not only selected sequences with sufficiently small energetic frustration but also selected more easily designable native structures. Since protein energy landscapes are in most cases minimally frustrated, structure based models (SMBs) have successfully determined the geometrical features associated with folding and functional transitions. Structural information, however, is limited with respect to different functional configurations. This is a major limitation for SBMs. Alternatively statistical methods to study amino acid co-evolution provide information on residue–residue interactions useful for the study of structure and function. I will show how the combination of these two methods gives rise to a novel way to investigate the mechanisms associated with folding and function.