title: A nonredundant structure dataset for benchmarking protein-RNA computational docking.
abstract: Protein-RNA interactions play an important role in many biological processes. The ability to predict the molecular structures of protein-RNA complexes from docking would be valuable for understanding the underlying chemical mechanisms. We have developed a novel nonredundant benchmark dataset for protein-RNA docking and scoring. The diverse dataset of 72 targets consists of 52 unbound-unbound test complexes, and 20 unbound-bound test complexes. Here, unbound-unbound complexes refer to cases in which both binding partners of the cocrystallized complex are either in apo form or in a conformation taken from a different protein-RNA complex, whereas unbound-bound complexes are cases in which only one of the two binding partners has another experimentally determined conformation. The dataset is classified into three categories according to the interface root mean square deviation and the percentage of native contacts in the unbound structures: 49 easy, 16 medium, and 7 difficult targets. The bound and unbound cases of the benchmark dataset are expected to benefit the development and improvement of docking and scoring algorithms for the docking community. All the easy-to-view structures are freely available to the public at http://zoulab.dalton.missouri.edu/RNAbenchmark/.
authors.affiliation: Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211, USA.
authors.forename: Sheng-You
authors.initials: SY
authors.lastname: Huang
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zou
mesh_terms: D030562:Databases, Protein; D062105:Molecular Docking Simulation; D009690:Nucleic Acid Conformation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012313:RNA / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins; D012313:RNA