The astonishing diversity in folding patterns of RNA threedimensional (3D) structures is crafted by myriads of noncovalent contacts, of which base pairing and stacking are the most prominent. A systematic and comprehensive classification and annotation of these interactions is necessary for a molecular-level understanding of their roles. However, unlike in the case of base pairing, where a widely accepted nomenclature and classification scheme exists in the public domain, currently available classification schemes for base−base stacking need major enhancements to comprehensively capture the necessary features underlying the rich stacking diversity in RNA. Here, we extend the previous stacking classification based on nucleobase interacting faces by introducing a structurally intuitive geometry-cum topology-based scheme. Specifically, a stack is first classified in terms of the geometry described by the relative orientation of the glycosidic bonds, which generates eight basic stacking geometric families for heterodimeric stacks and six of those for homodimeric stacks. Further annotation in terms of the identity of the bases and the region of involvement of purines (five-membered, six-membered, or both rings) leads to the enumeration of 384 distinct RNA base stacks. Based on our classification scheme, we present an algorithm for automated identification of stacks in RNA crystal structures and analyze the stacking context in selected RNA structures. Overall, the work described here is expected to greatly facilitate the structure-based RNA research.

Nucleobase π−π stacking is one of the crucial organizing interactions within three-dimensional (3D) RNA architectures. Characterizing the structural variability of these contacts in RNA crystal structures will help delineate their subtleties and their role in determining function. This analysis of different stacking geometries found in RNA X-ray crystal structures is the largest such survey to date; coupled with quantum-mechanical calculations on typical representatives of each possible stacking arrangement, we determined the distribution of stacking interaction energies. A total of 1,735,481 stacking contacts, spanning 359 of the 384 theoretically possible distinct stacking geometries, were identified. Our analysis reveals preferential occurrences of specific consecutive stacking arrangements in certain regions of RNA architectures. Quantum chemical calculations suggest that 88 of the 359 contacts possess intrinsically stable stacking geometries, whereas the remaining stacks require the RNA backbone or surrounding macromolecular environment to force their formation and maintain their stability. Our systematic analysis of π−π stacks in RNA highlights trends in the occurrence and localization of these noncovalent interactions and may help better understand the structural intricacies of functional RNA-based molecular architectures.

Understanding the frequency and structural context of discrete noncovalent interactions between nucleotides is of pivotal significance in establishing the rules that govern RNA structure and dynamics. Although T-shaped contacts (i.e., perpendicular stacking contacts) between aromatic amino acids and nucleobases at the nucleic acid–protein interface have recently garnered attention, the analogous contacts within the nucleic acid structures have not been discussed. In this work, we have developed an automated method for identifying and unambiguously classifying T-shaped interactions between nucleobases. Using this method, we identified a total of 3262 instances of T-shaped (perpendicular stacking) contacts between two nucleobases in an array of RNA structures from an up-to-date dataset of high-resolution crystal structures deposited in the Protein Databank. These analyses add to the understanding of the physicochemical forces that are responsible for structure and dynamics of RNA.

Post-transcriptionally modified bases play vital roles in many biochemical processes involving RNA. Analysis of the noncovalent interactions associated with these bases in RNA is crucial for providing a more complete understanding of the RNA structure and function; however, the characterization of these interactions remains understudied. To address this limitation, we present a comprehensive analysis of base stacks involving all crystallographic occurrences of the most biologically relevant modified bases in a large dataset of high-resolution RNA crystal structures. This is accompanied by a geometrical classification of the stacking contacts using our established tools. Coupled with quantum chemical calculations and an analysis of the specific structural context of these stacks, this provides a map of the stacking conformations available to modified bases in RNA. Overall, our analysis is expected to facilitate structural research on altered RNA bases.

Understanding the frequency and structural context of discrete noncovalent interactions between nucleotides is of pivotal significance in establishing the rules that govern RNA structure and dynamics. Although T-shaped contacts (i.e., perpendicular stacking contacts) between aromatic amino acids and nucleobases at the nucleic acid–protein interface have recently garnered attention, the analogous contacts within the nucleic acid structures have not been discussed. In this work, we have developed an automated method for identifying and unambiguously classifying T-shaped interactions between nucleobases. Using this method, we identified a total of 3261 instances of T-shaped (perpendicular stacking) contacts between two nucleobases in an array of RNA structures from an up-to-date dataset of <= 3.5 Å resolution crystal structures deposited in the Protein Data Bank.