title: Development of novel statistical potentials for protein fold recognition.
abstract: The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.
authors.affiliation: Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA.
authors.forename: N-V
authors.initials: NV
authors.lastname: Buchete
authors.forename: J E
authors.initials: JE
authors.lastname: Straub
authors.forename: D
authors.initials: D
authors.lastname: Thirumalai
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D008958:Models, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D011506:Proteins