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

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.

Force-constant-decayed anisotropic network model: An improved method for predicting RNA flexibility

“…In our method, all pairwise nucleotides were connected by springs, and the force constants of springs were decayed exponentially based on the separation distance to describe different interaction strengths between nucleotides. The force-constant-decayed method has been successfully used in GNM in our previous studies, [40] and in the present study this method was further extended combing with ANM to better describe RNA dynamics. The performance of the proposed fcd-ANM in predicting RNA flexibility was evaluated by using a non-redundant RNA structure database, in which the B-factors of nucleotides in these RNA structures were calculated by fcd-ANM and compared with the experimental data.…”

“…In this study, the non-redundant RNA structure dataset constructed in our previous work [40] was used to test the performance of the proposed fcd-ANM model in predicting the flexibility of RNAs. The non-redundant RNA structures in the dataset were obtained from the representative set 3.128 of RNA 3D structures collected by Leontis and Zirbel.…”

“…[43] The RNA structures meeting the following screening criteria were selected: (a) structures determined by x-ray crystallography; (b) structural resolution better than 3.0 Å; (c) the number of nucleotides larger than 122; (d) nucleotide B-factors available. Finally, 51 RNA structures were screened and included in the non-redundant RNA structure dataset, and the PDB codes of the filtered RNA structures can be found in our previous 068704-3 work, [40] which were also listed in Table S1 in the supplementary materials.…”