title: Density-based score for selecting near-native atomic models of unknown structures.
abstract: We present a low-resolution density-based scoring scheme for selecting high-quality models from a large pool of lesser quality models. We use pre-configured decoy data sets that contain large numbers of models with different degrees of correctness to evaluate the performance of the strategy. We find that the scoring scheme consistently identifies one of the highest quality models for a wide variety of target structures, resolution ranges, and noise models. Tests with experimental data yield similar results.
authors.affiliation: The Burnham Institute for Medical Research, Bioinformatics and Systems Biology Program, La Jolla, CA 92037, USA.
authors.forename: Eran
authors.initials: E
authors.lastname: Shacham
authors.forename: Brian
authors.initials: B
authors.lastname: Sheehan
authors.forename: Niels
authors.initials: N
authors.lastname: Volkmann
mesh_terms: D018360:Crystallography, X-Ray; D009682:Magnetic Resonance Spectroscopy; D008432:Mathematical Computing; D008854:Microscopy, Electron; D008958:Models, Molecular; D011487:Protein Conformation; D012107:Research Design