Understanding the Sequence Preference of Recurrent RNA Building Blocks Using Quantum Chemistry: The Intrastrand RNA Dinucleotide Platform

Abstract

Folded RNA molecules are shaped by an astonishing variety of highly conserved noncanonical molecular interactions and backbone topologies. The dinucleotide platform is a widespread recurrent RNA modular building submotif formed by the side-by-side pairing of bases from two consecutive nucleotides within a single strand, with highly specific sequence preferences. This unique arrangement of bases is cemented by an intricate network of noncanonical hydrogen bonds and facilitated by a distinctive backbone topology. The present study investigates the gas-phase intrinsic stabilities of the three most common RNA dinucleotide platforms — 5′-GpU-3′, ApA, and UpC — via state-of-the-art quantum-chemical (QM) techniques. The mean stability of base-base interactions decreases with sequence in the order GpU > ApA > UpC. Bader’s atoms-in-molecules analysis reveals that the N2(G)…O4(U) hydrogen bond of the GpU platform is stronger than the corresponding hydrogen bonds in the other two platforms. The mixed-pucker sugar-phosphate backbone conformation found in most GpU platforms, in which the 5′-ribose sugar (G) is in the C2′-endo form and the 3′-sugar (U) in the C3′-endo form, is intrinsically more stable than the standard A-RNA backbone arrangement, partially as a result of a favorable O2′…O2P intra-platform interaction. Our results thus validate the hypothesis of Lu et al. (Lu Xiang-Jun, et al. Nucleic Acids Res. 2010, 38, 4868-4876), that the superior stability of GpU platforms is partially mediated by the strong O2′…O2P hydrogen bond. In contrast, ApA and especially UpC platform-compatible backbone conformations are rather diverse and do not display any characteristic structural features. The average stabilities of ApA and UpC derived backbone conformers are also lower than those of GpU platforms. Thus, the observed structural and evolutionary patterns of the dinucleotide platforms can be accounted for, to a large extent, by their intrinsic properties as described by modern QM calculations. In contrast, we show that the dinucleotide platform is not properly described in the course of atomistic explicit-solvent simulations. Our work also gives methodological insights into QM calculations of experimental RNA backbone geometries. Such calculations are inherently complicated by rather large data and refinement uncertainties in the available RNA experimental structures, which often preclude reliable energy computations.

Isosteric and Nonisosteric Base Pairs in RNA Motifs: Molecular Dynamics and Bioinformatics Study of the Sarcin–Ricin Internal Loop

“…8). As discussed elsewhere 55 this is a hitherto unresolved local force field problem. It does not prevent using the simulations to study base substitutions since the perturbation remains local on the present time scale and it is affecting all simulated structures to a similar extent.…”

“…3). The very complex system of 13 H-bonds and interstrand stackings make the G-bulge region very rigid 35,54,55 and means that the “bulged G” is actually very tightly integrated into the core of the motif.…”

Computational Approaches to Predicting the Impact of Novel Bases on RNA Structure and Stability

“…(64) Nonetheless, further work is needed on larger systems that incorporate structural features beyond the base pair in order to account for distortions (e.g., helical strain, propeller twist) imposed by the larger superstructure of the RNA duplex (57, 65) —distortions that likely contribute not only to magnitudes of interaction energies between bases, but also to base-pairing specificity and sequence effects. (66) …”

“…Studies of this type show that docking of nucleobases and nucleobase analogs into RNA binding sites is feasible, even using docking software not designed specifically for use with RNA. (57, 65, 66, 78–82) …”

“…Šponer and co-workers recently have made methodological advances toward this approach. (66) Alternatively, one could utilize the recently described fragment-based (86) QM approaches developed by Raghavachari and co-workers (and others) for treating large molecules such as DNA oligomers. (87) A third option would be to repurpose existing computational docking software to predict the binding proficiency of a nucleobase analog.…”