The data of the article "Force-constant-decayed anisotropic network model: An improved method for predicting RNA flexibility"

RNA is an important biological macromolecule, which plays an irreplaceable role in many life activities. Thus, how to effective extract structure-encoded dynamics is of great significance for understanding RNA functions. Anisotropic network model (ANM) is an efficient method to investigate macromolecular dynamical properties, which has been widely used in protein studies. However, the performance of the conventional ANM in describing RNA flexibility is not as good as that on proteins. In this study, we proposed a new approach, named force-constant-decayed anisotropic network model (fcd-ANM), to improve the performance in investigating the dynamical properties encoded in RNA structures. In fcd-ANM, nucleotide pairs in RNA structure were connected by springs and the force constant of springs was decayed exponentially based on the separation distance to describe the differences in the inter-nucleotide interaction strength. Fcd-ANM can serve as a more effective method for analysis of RNA dynamical properties. The data of this article published in Chinese Physics B are described below:The average PCC values calculated by the conventional ANM and fcd-ANM under different values; the PCC value between the predicted and experimental B-factors calculated by fcd-ANM and conventional ANM; the comparison of the calculation results between ANM and fcd-ANM for the “AN1” chain of the acinetobacter baumannii ribosome (PDB code:6V3A); the individual PCC values for all the RNA structures in the redundant database obtained by the conventional ANM and fcd-ANM; the RNA structures contained in the non-redundant database; the PCC values between the predicted and experimental B-factors for each RNA in the database calculated by the conventional ANM under different distance cutoff values; the PCC values between the predicted and experimental B-factors for each RNA in the database calculated by fcd-ANM under different σ values.