Determining the three-dimensional structure of a protein is an important step in understanding biological function. Despite advances in experimental methods (crystallography and NMR) and protein structure prediction techniques, the gap between the number of known protein sequences and determined structures continues to grow. Approaches to protein structure prediction vary from those that apply physical principles to those that consider known amino acid sequences and previously determined protein structures. In this paper we consider a two-step approach to structure prediction: (1) predict contacts between amino acids using sequence data; (2) predict protein structure using the predicted contact maps. Our focus is on the second step of this approach. In particular, we apply a case-based reasoning framework to determine the alignment of secondary structures based on previous experiences stored in a case base, along with detailed knowledge of the chemical and physical properties of proteins. Case-based reasoning is founded on the premise that similar problems have similar solutions. Our hypothesis is that we can use previously determined structures and their contact maps to predict the structure for novel proteins from their contact maps. The paper presents an overview of contact maps along with the general principles behind our methodology of case-based reasoning. We discuss details of the implementation of our system and present empirical results using contact maps retrieved from the Protein Data Bank.

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Information Systems Frontiers
Department of Cognitive Science

Glasgow, J. (Janice), Kuo, T. (Tony), & Davies, J. (2006). Protein structure from contact maps: A case-based reasoning Approach. Information Systems Frontiers, 8(1), 29–36. doi:10.1007/s10796-005-6101-9