title: Knowledge-based interaction potentials for proteins.
abstract: We discuss the derivation of atomic-level potentials of mean force from the known protein structures and their applicability for structural evaluation applications. In the derivation process, rigorous density estimation methodology is used to estimate the probability density functions (PDFs) for the distributions of interatomic distances in the protein structures. Potentials of mean force are then derived from these density functions using simple Boltzmann's relation. We also test the potentials against pairs of current and superseded protein structures in the Protein Data Bank. Using PDF potentials to evaluate each structure pair, we are able to identify, with high accuracy, which of the two structures is of higher resolution or better quality. This result shows that the PDF potentials are sensitive to details in protein structures as the current and superseded atomic coordinates generally do not differ by more than 1 A in root-mean-square deviation, and that the PDF potentials could potentially be used for X-ray structure refinement and protein structure prediction.
authors.affiliation: Department of Chemical Engineering, University of Wisconsin-Madison, USA.
authors.forename: A
authors.initials: A
authors.lastname: Rojnuckarin
authors.forename: S
authors.initials: S
authors.lastname: Subramaniam
mesh_terms: D016208:Databases, Factual; D005583:Fourier Analysis; D008958:Models, Molecular; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins