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非编码RNA的三维结构对于人们理解和干预其生物功能具有重要的意义,从计算的角度发展RNA结构预测方法可以加速结构获取过程,对三维结构进行评分是进行结构预测的关键步骤.近年来,基于机器学习的方法,如AlphaFold2,已在分子结构预测领域取得了革命性的进展.基于深度卷积神经网络,建立了一个对RNA三维结构进行评估的方法 .为了训练这一网络,建立了一个非冗余的含有422个RNA以及126600个decoys结构的数据集.训练得到的模型在RNA-Puzzles数据集上进行了测试,结果表明,在28个RNA中,网络从众多decoys中挑选出实验结构的正确率约为71.4%,这一结果比之前有所提高.另外,还对网络的工作机制进行了分析,发现神经网络对结构评分的倾向性和已知的物理化学知识相一致. 相似文献
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Chengwei Deng 《中国物理 B》2022,31(11):118702-118702
RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn. 相似文献
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