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

作　　者：Wen-Jing Wang Ji-Guo Su 王文静;苏计国(Key Laboratory for Microstructural Material Physics of Hebei Province,School of Science,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]Key Laboratory for Microstructural Material Physics of Hebei Province,School of Science,Yanshan University,Qinhuangdao 066004,China

出　　处：《Chinese Physics B》2021年第5期176-183,共8页中国物理B（英文版）

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 

分 类 号：Q811.4[生物学—生物工程]