Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

doi:10.1088/1674-1056/abe1a5

中国物理B ›› 2021, Vol. 30 ›› Issue (5): 58701-058701.doi: 10.1088/1674-1056/abe1a5

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Wen-Jing Wang(王文静) and Ji-Guo Su(苏计国)^†

Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, China

收稿日期:2020-12-07 修回日期:2021-01-10 接受日期:2021-02-01 出版日期:2021-05-14 发布日期:2021-05-14
通讯作者: Ji-Guo Su E-mail:jiguosu@ysu.edu.cn

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Wen-Jing Wang(王文静) and Ji-Guo Su(苏计国)^†

Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, China

Received:2020-12-07 Revised:2021-01-10 Accepted:2021-02-01 Online:2021-05-14 Published:2021-05-14
Contact: Ji-Guo Su E-mail:jiguosu@ysu.edu.cn

1. 2021-058701-SI.pdf(213KB)

摘要/Abstract

摘要： Gaussian network model (GNM) is an efficient method to investigate the structural dynamics of biomolecules. However, the application of GNM on RNAs is not as good as that on proteins, and there is still room to improve the model. In this study, two novel approaches, named the weighted GNM (wGNM) and the force-constant-decayed GNM (fcdGNM), were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs. In wGNM, the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides. In fcdGNM, all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs. The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules. The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures. Compared with the conventional GNM, the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85% and 6.76% for wGNM and fcdGNM, respectively. Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.

关键词: Gaussian network model (GNM), B-factor, RNA molecules

Abstract: Gaussian network model (GNM) is an efficient method to investigate the structural dynamics of biomolecules. However, the application of GNM on RNAs is not as good as that on proteins, and there is still room to improve the model. In this study, two novel approaches, named the weighted GNM (wGNM) and the force-constant-decayed GNM (fcdGNM), were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs. In wGNM, the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides. In fcdGNM, all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs. The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules. The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures. Compared with the conventional GNM, the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85% and 6.76% for wGNM and fcdGNM, respectively. Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.

Key words: Gaussian network model (GNM), B-factor, RNA molecules

中图分类号: (Analytical theories)

87.15.ad

87.14.gn (RNA) 87.15.hp (Conformational changes)

Wen-Jing Wang(王文静) and Ji-Guo Su(苏计国). Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA[J]. 中国物理B, 2021, 30(5): 58701-058701.

Wen-Jing Wang(王文静) and Ji-Guo Su(苏计国). Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA[J]. Chin. Phys. B, 2021, 30(5): 58701-058701.

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Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

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