Prediction of RNA 1H and 13C Chemical Shifts: A Structure Based Approach
Prediction of RNA 1H and 13C Chemical Shifts: A Structure Based Approach
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DOI：
10.1021/jp407254m
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发表时间：
2013-10-31
影响因子：
3.3
通讯作者：
Stelzer, Andrew C.
中科院分区：
文献类型：
--
作者：
Frank, Aaron T.;Bae, Sung-Hun;Stelzer, Andrew C.
核磁共振衍生的化学位移在蛋白质结构测定和预测中的应用受到了广泛关注，因此，已经开发了许多方法来预测三维（3D）坐标下的蛋白质化学位移。相比之下，很少有人关注预测RNA坐标的化学变化。利用随机森林机器学习方法，我们开发了RAMSEY，它能够预测H-1和质子化C-13从RNA坐标的化学位移。在这篇报告中，我们介绍了RAMSEY，评估了它的准确性，并证明了RAMSEY预测的化学位移对RNA 3D结构的敏感性。
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 H-1 and protonated C-13 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.