Prediction of RNA 1H and 13C Chemical Shifts: A Structure Based Approach
作者： Aaron T. Frank , Sung-Hun Bae , Andrew C. Stelzer
DOI: 10.1021/JP407254M
关键词: Random forest 、 Structure based 、 Reference standards 、 Chemical shift 、 Protein structure 、 RNA 、 Sensitivity (control systems) 、 Computational chemistry 、 Chemistry 、 Biological system 、 Physical and Theoretical Chemistry 、 Materials Chemistry 、 Surfaces, Coatings and Films
摘要: 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 from three-dimensional (3D) coordinates. In contrast, little attention paid predicting RNA Using the random forest machine learning approach, we RAMSEY, which is capable both (1)H protonated (13)C this report, introduce assess its accuracy, demonstrate sensitivity RAMSEY-predicted 3D structure.
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