Exact Energy Landscapes of Proteins Using a Coarse-Grained Model

The understanding of the relation between the amino acid sequence and the spatial structure of the protein is an open task since many decades. Some tools for the prediction of protein structures from known ones were developed, but they let unanswered fundamental questions about properties of folded proteins and the folding process itself. Recently, coarse-grained models were developed, which are able to predict protein structures with an acceptable level of accuracy using probabilistic algorithms. In this paper, we introduce a new coarse-grained model, which neglects details on the amino acid level and uses structure elements of successive amino acids as building blocks instead. Within this approximation, we use a deterministic branch and bound algorithm, which is able to find the exact ground state and the complete low-energy landscape. The agreement of the calculated ground state with the native protein structure is shown. A possible application of the model to explain experiments with membrane proteins using dynamic force microscopy is sketched.