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

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

RNA is an important biological macromolecule, which plays an irreplaceable role in many life activities. RNA functions are largely determined by its tertiary structure and the intrinsic dynamics encoded in the structure. 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. The performance of fcd-ANM in predicting RNA flexibility was evaluated using a non-redundant structure database composed of 51 RNAs. The results indicate that fcd-ANM significantly outperforms the conventional ANM in reproducing the experimental B-factors of nucleotides in RNA structures, and the Pearson correlation coefficient between the predicted and experimental nucleotide B-factors was distinctly improved by 21.05% compared to the conventional ANM. Fcd-ANM can serve as a more effective method for analysis of RNA dynamical properties.     

持久性有机污染物4,4’-DDE和CB-153的反向虚拟筛选

持久性有机污染物(POPs)会导致人类和动植物各种疾病的发生, 已经成为一种新的全球性环境问题.研究发现, POPs分子能够特异性地结合到生物体内一些蛋白质受体上, 发挥其致病机制. 本文运用基于分子对接的反向虚拟筛选方法,从蛋白质结构数据库中筛选出配体分子可能的受体蛋白质, 研究了持久性有机污染物4,4'-DDE和CB-153潜在的受体蛋白质. 结合实验信息, 讨论分析了排在前5%的蛋白质受体. 值得注意的是,已知的一些蛋白质受体都排在了虚拟筛选结果的前列. 该研究不仅有助于进一步了解这些有毒污染物分子的致病机制, 还将为设计针对该类污染物分子的生物传感器提供有用的信息.     

Identification of key residues in protein functional movements by using molecular dynamics simulations combined with a perturbation-response scanning method

The realization of protein functional movement is usually accompanied by specific conformational changes, and there exist some key residues that mediate and control the functional motions of proteins in the allosteric process. In the present work, the perturbation-response scanning method developed by our group was combined with the molecular dynamics (MD) simulation to identify the key residues controlling the functional movement of proteins. In our method, a physical quantity that is directly related to protein specific function was introduced, and then based on the MD simulation trajectories, the perturbation-response scanning method was used to identify the key residues for functional motions, in which the residues that highly correlated with the fluctuation of the function-related quantity were identified as the key residues controlling the specific functional motions of the protein. Two protein systems, i.e., the heat shock protein 70 and glutamine binding protein, were selected as case studies to validate the effectiveness of our method. Our calculated results are in good agreement with the experimental results. The location of the key residues in the two proteins are similar, indicating the similar mechanisms behind the performance of their biological functions.