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A Root Mean Square Deviation Estimation Algorithm (REA) and Its Use for Improved RNA Structure Prediction

biorxiv(2024)

Institute for Fundamental Biomedical Science

Cited 0|Views16
Abstract
The 3D structure of RNA is crucial for biotechnological applications and to comprehend its biological function. Recent developments using AlphaFold-inspired deep neural networks improved the prediction of 3D structure from RNA sequence, but evaluation of the accuracy of these predictions is still necessary. We present the RMSD Estimation Algorithm (REA), a feed-forward neural network to predict the root-mean-square deviation (RMSD) of a 3D RNA structure from its experimentally determined counterpart using its Molprobity [[1][1]] stereochemical validation features. It is trained on structures predicted by the DeepFoldRNA [[2][2]] and trRosettaRNA [[3][3]] transformer-based deep neural networks on a set of 182 models of RNA structures with pseudoknots. We compare REA with ARES [[4][4]], a deep learning algorithm that predicts the RMSD by extracting geometric patterns with equivariant convolution, assessing the prediction accuracy on RNAs with and without pseudoknots. REA outperformed ARES on both test sets with smaller absolute difference between the true and the predicted RMSD. Using a combination of REA and a Support Vector Regression (SVR) trained on the same data as REA, we can select RNA structures predicted with DeepFoldRNA, trRosettaRNA and Rhofold [[5][5]] to achieve a significantly higher prediction accuracy than any of the prediction methods used alone. This was shown on a validation set with 261 novel RNA chains extracted from the Nonredundant 3D Structure Dataset [[5][5]] and a test set with 55 novel RNA chains from RNA-Puzzles [[5][5]]. Our selection based prediction method can easily incorporate additional prediction algorithms.### Competing Interest StatementThe authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5
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RNA Structure,Secondary Structure Prediction,tRNA
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要点】:本文提出了一种新的RMSD估计算法REA,通过预测3D RNA结构与实验确定的结构之间的RMSD,以改进RNA结构的预测准确性。

方法】:REA算法使用了一个前馈神经网络,训练数据来自于DeepFoldRNA和trRosettaRNA两个基于变压器的深度神经网络预测的RNA结构模型。

实验】:实验使用了包含伪结的182个RNA结构模型进行训练,并在包含261个新型RNA链的非冗余3D结构数据集和55个RNA链的RNA-Puzzles数据集上验证了方法的有效性,结果显示REA算法在预测RMSD方面优于ARES算法。