Mechanistic aspects of the interaction of intercalating metal complexes with nucleic acids

Since the discovery of the intercalative binding mode, almost half a century ago, intense efforts have been devoted to design, synthesize and test new small molecules that can bind nucleic acids with improved recognition and affinity. Among them, metal bearing compounds play a principal role. Despite the plethora of different metal complexes which have been designed to react with DNA and which have been tested, the binding mechanisms have often not been analysed. This is unfortunate, considering the importance of understanding of the binding features in depth in order to optimise their biological effects. This review covers articles where an analysis of the kinetic aspects of the interaction between the target metal compound and nucleic acids has been carried out and details of the reaction mechanism are provided. Flat metal complexes (porphyrins), spherical complexes with protruding intercalating residues, azamacrocycle metallo-intercalators and intercalators with metal bearing pendant arms are the classes of molecules that have been taken into account. The limits of the SDS method, employed to measure the rates of drug dissociation from polynucleotides, are also discussed.     

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.     

A metric space to study differences between polynucleotides

We introduce a new metric in the space of fuzzy polynucleotides.     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.