Nonredundant 3D Structure Datasets for RNA Knowledge Extraction and Benchmarking

摘要: The continual improvement of methods for RNA 3D structure modeling and prediction requires accurate statistically meaningful data concerning structure, both extraction knowledge benchmarking predictions. source sufficiently structural these purposes is atomic-resolution X-ray structures nucleotides, oligonucleotides, biologically functional molecules. All our basic bond lengths, angles, stereochemistry in as well their interaction preferences, including all types base-pairing, base-stacking, base-backbone interactions, ultimately extracted from structures. One key requirement reference databases intended the nonredundancy that are included analysis, to avoid bias deduced frequency parameters. Here, we address this issue detail how produce, on a largely automated ongoing basis, nonredundant lists at different resolution thresholds use knowledge-driven applications. file collections available download http://rna.bgsu.edu/nrlist. primary provide only include structures, organized by thresholds, but completeness, also separate solved NMR or cryo-EM.