RNA molecules play important roles in biological processes, their functions are intimately related to structural dynamics. Elastic network model (ENM) has achieved great success in predicting the large-amplitude collective behavior of proteins. However, for loosely-packed RNA structures, ENM models can not reproduce their dynamics as accurate as the densely-packed ones. In this work, the multiscale Gaussian network model (mGNM) is extended to predict dynamic properties of RNAs. All tests are performed on a non-redundant RNA structure database we constructed. In results, for B-factor reproduction, encouragingly mGNM achieves a significant improvement with the average value of Pearson correlation coefficient (PCC) between theoretical and experimental B-factors being 0.732, much higher than 0.494 and 0.321 obtained by conventional GNM and parameter-free GNM (pfGNM) models, respectively. Furtherly, mGNM attains a larger improvement in B-factor prediction for loosely-packed parts. Additionally, based on the analysis of functional movements, mGNM can properly make domain decompositions for tRNA^Asp and xrRNA. This work can strengthen the understanding of the intrinsic dynamics of RNAs, and mGNM is expected to have a bright prospect in dynamic analyses for loosely folded biomolecules, especially RNAs.

RNA分子在生物过程中起重要作用，其功能与结构动力学密切相关。弹性网络模型（ENM）在预测蛋白质的大幅度集体行为方面取得了巨大成功。但是，对于松散堆积的RNA结构，ENM模型无法像密集堆积的模型那样精确地再现其动力学。在这项工作中，多尺度高斯网络模型（mGNM）被扩展以预测RNA的动态特性。所有测试均在我们构建的非冗余RNA结构数据库上进行。结果，对于B因子的复制，令人鼓舞的是mGNM取得了显着改善，理论和实验B因子之间的皮尔逊相关系数（PCC）的平均值为0.732，远高于0.494和0。分别由常规GNM模型和无参数GNM（pfGNM）模型获得的321。此外，对于松散包装的零件，mGNM在B因子预测方面获得了更大的改进。此外，基于功能性运动的分析，mGNM可以适当地对tRNA进行结构域分解^Asp和xrRNA。这项工作可以加深对RNA内在动力学的理解，并且mGNM有望在松散折叠的生物分子（尤其是RNA）的动力学分析中具有广阔的前景。