Piercing the fog of the RNA structure-ome

Stacking Interactions of Druglike Heterocycles with Nucleobases

Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.

A guide to RNA structure analysis and RNA-targeting methods

RNAs are increasingly recognized as promising therapeutic targets, susceptible to modulation by strategies that include targeting with small molecules, antisense oligonucleotides, deoxyribozymes (DNAzymes), or CRISPR/Cas13. However, while drug development for proteins follows well-established paths for rational design based on the accurate knowledge of their three-dimensional structure, RNA-targeting strategies are challenging since comprehensive RNA structures are yet scarce and challenging to acquire. Numerous methods have been developed to elucidate the secondary and three-dimensional structure of RNAs, including X-ray crystallography, cryo-electron microscopy, nuclear magnetic resonance, SHAPE, DMS, and bioinformatic methods, yet they have often revealed flexible transcripts and co-existing populations rather than single-defined structures. Thus, researchers aiming to target RNAs face a critical decision: whether to acquire the detailed structure of transcripts in advance or to adopt phenotypic screens or sequence-based approaches that are independent of the structure. Still, even in strategies that seem to rely only on the nucleotide sequence (like the design of antisense oligonucleotides), researchers may need information about the accessibility of the compounds to the folded RNA molecule. In this concise guide, we provide an overview for researchers interested in targeting RNAs: We start by revisiting current methodologies for defining secondary or three-dimensional RNA structure and then we explore RNA-targeting strategies that may or may not require an in-depth knowledge of RNA structure. We envision that complementary approaches may expedite the development of RNA-targeting molecules to combat disease.