Prediction of RNA 1H and 13C Chemical Shifts: A Structure Based Approach

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• 作者：Aaron T. Frank ; Sung-Hun Bae ; Andrew C. Stelzer
• 刊名：Journal of Physical Chemistry B
• 出版年：2013
• 出版时间：October 31, 2013
• 年：2013
• 卷：117
• 期：43
• 页码：13497-13506
• 全文大小：359K
• 年卷期：v.117,no.43(October 31, 2013)
• ISSN：1520-5207

文摘

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 ¹H and protonated ¹³C 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.