A Computational Model for Predicting Experimental RNA Nearest-Neighbor Free Energy Rankings: Inosine•Uridine Pairs

Predicting nearest neighbor free energies of modified RNA with LIE: results for pseudouridine and N1-methylpseudouridine within RNA duplexes

Pseudouridine (Ψ) and N1-methylpseudouridine (m1Ψ) are among the key modifications in the field of mRNA therapeutics and vaccine research. The accuracy of the design and development of therapeutic RNAs containing such modifications depends on the accuracy of the secondary structure prediction, which in turn depends on the nearest neighbor (NN) thermodynamic parameters for the standard and modified residues. Here, we propose a simple approach based on molecular dynamics simulations and linear interaction energy (LIE) approximation that is able to predict the NN free energy parameters for U-A, Ψ-A and m1Ψ-A pairs in reasonable agreement with the recent experimental reports. We report the NN thermodynamic parameters for different U, Ψ and m1Ψ base pairs, which might be helpful for a deeper understanding of the effect of these modifications in RNA. The predicted NN free energy parameters in this study are able to closely reproduce the folding free energies of duplexes containing internal Ψ for which the thermodynamic data were available. Additionally, we report the predicted folding free energies for the duplexes containing internal m1Ψ.

Inosine and its methyl derivatives: Occurrence, biogenesis, and function in RNA

Inosine is one of the most common post-transcriptional modifications. Since its discovery, it has been noted for its ability to contribute to non-Watson-Crick interactions within RNA. Rapidly accumulating evidence points to the widespread generation of inosine through hydrolytic deamination of adenosine to inosine by different classes of adenosine deaminases. Three naturally occurring methyl derivatives of inosine, i.e., 1-methylinosine, 2'-O-methylinosine and 1,2'-O-dimethylinosine are currently reported in RNA modification databases. These modifications are expected to lead to changes in the structure, folding, dynamics, stability and functions of RNA. The importance of the modifications is indicated by the strong conservation of the modifying enzymes across organisms. The structure, binding and catalytic mechanism of the adenosine deaminases have been well-studied, but the underlying mechanism of the catalytic reaction is not very clear yet. Here we extensively review the existing data on the occurrence, biogenesis and functions of inosine and its methyl derivatives in RNA. We also included the structural and thermodynamic aspects of these modifications in our review to provide a detailed and integrated discussion on the consequences of A-to-I editing in RNA and the contribution of different structural and thermodynamic studies in understanding its role in RNA. We also highlight the importance of further studies for a better understanding of the mechanisms of the different classes of deamination reactions. Further investigation of the structural and thermodynamic consequences and functions of these modifications in RNA should provide more useful information about their role in different diseases.

Predictions and analyses of RNA nearest neighbor parameters for modified nucleotides

The most popular RNA secondary structure prediction programs utilize free energy (ΔG°₃₇) minimization and rely upon thermodynamic parameters from the nearest neighbor (NN) model. Experimental parameters are derived from a series of optical melting experiments; however, acquiring enough melt data to derive accurate NN parameters with modified base pairs is expensive and time consuming. Given the multitude of known natural modifications and the continuing use and development of unnatural nucleotides, experimentally characterizing all modified NNs is impractical. This dilemma necessitates a computational model that can predict NN thermodynamics where experimental data is scarce or absent. Here, we present a combined molecular dynamics/quantum mechanics protocol that accurately predicts experimental NN ΔG°₃₇ parameters for modified nucleotides with neighboring Watson–Crick base pairs. NN predictions for Watson-Crick and modified base pairs yielded an overall RMSD of 0.32 kcal/mol when compared with experimentally derived parameters. NN predictions involving modified bases without experimental parameters (N⁶-methyladenosine, 2-aminopurineriboside, and 5-methylcytidine) demonstrated promising agreement with available experimental melt data. This procedure not only yields accurate NN ΔG°₃₇ predictions but also quantifies stacking and hydrogen bonding differences between modified NNs and their canonical counterparts, allowing investigators to identify energetic differences and providing insight into sources of (de)stabilization from nucleotide modifications.

Stability of RNA duplexes containing inosine·cytosine pairs

Inosine is found naturally in the anticodon loop of tRNA, is a product of adenosine deaminases that act on RNA, and can be used in oligonucleotide probes or to investigate the role of the exocyclic amino group of guanosine. Although the thermodynamics of I·U pairs in RNA have been systematically studied [Wright, D. J., Rice, J. L., Yanker, D. M., and Znosko, B. M. (2007) Biochemistry 46, 4625-4634], the thermodynamics of I·C pairs in RNA have not. Here, we have performed optical melting experiments on a series of RNA duplexes containing I·C pairs and compared their thermodynamics to the same duplexes containing A·C and G-C pairs. Nearest neighbor parameters for single I·C pairs adjacent to Watson-Crick pairs were derived. The derived nearest neighbor parameters are compared to those previously predicted blindly through a reweighting of energy-function collection with conformational ensemble sampling in Rosetta [Chou, F.-C., Kladwang, W., Kappel, K., and Das, R. (2016) Proc. Natl. Acad. Sci. U.S.A. 113, 8430-8435]. Scientists can use these nearest neighbor parameters to calculate the stability of ADAR products and to calculate the stability of an RNA duplex in which G-to-I substitution was used to determine the role of the exocyclic amino group of G.

RNA modifications in structure prediction - Status quo and future challenges

Chemical modifications of RNA nucleotides change their identity and characteristics and thus alter genetic and structural information encoded in the genomic DNA. tRNA and rRNA are probably the most heavily modified genes, and often depend on derivatization or isomerization of their nucleobases in order to correctly fold into their functional structures. Recent RNomics studies, however, report transcriptome wide RNA modification and suggest a more general regulation of structuredness of RNAs by this so called epitranscriptome. Modification seems to require specific substrate structures, which in turn are stabilized or destabilized and thus promote or inhibit refolding events of regulatory RNA structures. In this review, we revisit RNA modifications and the related structures from a computational point of view. We discuss known substrate structures, their properties such as sub-motifs as well as consequences of modifications on base pairing patterns and possible refolding events. Given that efficient RNA structure prediction methods for canonical base pairs have been established several decades ago, we review to what extend these methods allow the inclusion of modified nucleotides to model and study epitranscriptomic effects on RNA structures.

Role of Inosine⁻Uracil Base Pairs in the Canonical RNA Duplexes

Adenosine to inosine (A–I) editing is the most common modification of double-stranded RNA (dsRNA). This change is mediated by adenosine deaminases acting on RNA (ADARs) enzymes with a preference of U>A>C>G for 5′ neighbor and G>C=A>U or G>C>U=A for 3′ neighbor. A–I editing occurs most frequently in the non-coding regions containing repetitive elements such as ALUs. It leads to disruption of RNA duplex structure, which prevents induction of innate immune response. We employed standard and biased molecular dynamics (MD) simulations to analyze the behavior of RNA duplexes with single and tandem inosine–uracil (I–U) base pairs in different sequence context. Our analysis showed that the I–U pairs induce changes in base pair and base pair step parameters and have different dynamics when compared with standard canonical base pairs. In particular, the first I–U pair from tandem I–U/I–U systems exhibited increased dynamics depending on its neighboring 5′ base. We discovered that UII sequence, which is frequently edited, has lower flexibility compared with other sequences (AII, GII, CII), hence it only modestly disrupts dsRNA. This might indicate that the UAA motifs in ALUs do not have to be sufficiently effective in preventing immune signaling.