title: Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures.
abstract: Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 10⁶ quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy.
authors.affiliation: Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892-5624, USA.
authors.forename: Guillermo A
authors.initials: GA
authors.lastname: Bermejo
authors.forename: G Marius
authors.initials: GM
authors.lastname: Clore
authors.forename: Charles D
authors.initials: CD
authors.lastname: Schwieters
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins