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.