title: Prediction of RNA 1H and 13C chemical shifts: a structure based approach.
abstract: The use of NMR-derived chemical shifts in protein structure determination and prediction has received much attention, and, as such, many methods have been developed to predict protein chemical shifts from three-dimensional (3D) coordinates. In contrast, little attention has been paid to predicting chemical shifts from RNA coordinates. Using the random forest machine learning approach, we developed RAMSEY, which is capable of predicting both (1)H and protonated (13)C chemical shifts from RNA coordinates. In this report, we introduce RAMSEY, assess its accuracy, and demonstrate the sensitivity of RAMSEY-predicted chemical shifts to RNA 3D structure.
authors.affiliation: Nymirum , 3510 West Liberty Road, Ann Arbor, Michigan 48103, United States.
authors.forename: Aaron T
authors.initials: AT
authors.lastname: Frank
authors.forename: Sung-Hun
authors.initials: SH
authors.lastname: Bae
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Stelzer
mesh_terms: D002247:Carbon Isotopes; D009682:Magnetic Resonance Spectroscopy / Q000592:standards; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D011522:Protons; D012313:RNA / Q000737:chemistry*; D012015:Reference Standards
chemical_list: D002247:Carbon Isotopes; D011522:Protons; D012313:RNA