RNAGCN: RNA tertiary structure assessment with a graph convolutional network
Chengwei Deng(邓成伟)1, Yunxin Tang(唐蕴芯)1, Jian Zhang(张建)1,2,†, Wenfei Li(李文飞)1,2, Jun Wang(王骏)1,2, and Wei Wang(王炜)1,2,‡
1 Collaborative Innovation Center of Advanced Microstructures, School of Physics, Nanjing University, Nanjing 210008, China; 2 Institute for Brain Sciences, Nanjing University, Nanjing 210008, China
Abstract 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.
Fund: This study was funded by the National Natural Science Foundation of China (Grant Nos. 11774158 to JZ, 11934008 to WW, and 11974173 to WFL). The authors acknowledge High Performance Computing Center of Advanced Microstructures, Nanjing University for the computational support.
Corresponding Authors:
Jian Zhang, Wei Wang
E-mail: jzhang@nju.edu.cn;wangwei@nju.edu.cn
Cite this article:
Chengwei Deng(邓成伟), Yunxin Tang(唐蕴芯), Jian Zhang(张建), Wenfei Li(李文飞), Jun Wang(王骏), and Wei Wang(王炜) RNAGCN: RNA tertiary structure assessment with a graph convolutional network 2022 Chin. Phys. B 31 118702
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