Computational model for predicting experimental RNA and DNA nearest-neighbor free energy rankings

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.

Models for prediction of single base primer extension efficiency from position and type of single mismatch in primer-template duplex

Amplification and specificity of polymerase chain reaction (PCR) are affected by the position and type of primer-template mismatches (MMs) as well as various conditions of reaction. In this study, multiple linear regression (MLR) models and artificial neural network (ANN) models were developed for the prediction of the effects of primer-template mismatches on the primer extension efficiency in primer-template duplex. In MLR models, the independent variable Pi representing the position effect of i-th mismatch from 3' end of primers was normalized to values between 0 and 1 according to the size of ΔΔGi, the difference of Gibbs free energy changes between the mismatch and its corresponding perfect-match, and other independent variables Pj representing the position effect of the j-th perfect-match from 3' end of primer were coded 1. A dependent variable of MLR model was relative extension efficiencies of primers. In ANN models, an input layer has neurons equal to the number of independent variables of corresponding MLR models and a hidden layer and an output layer have four and one neurons, respectively. Our MLR models and ANN models outperform the previous polynomial regression model for the prediction of the single base extension (SBE) efficiencies of single-MM primers. Especially, ANN model 6 which has 32 neurons representing the position effect of mismatch, the type of mismatch and the annealing temperature on primer-template duplex in the input layer can predict the SBE efficiencies of single-MM primers with a high accuracy, since its correlation coefficients R in training set, testing set and all data are 0.9870, 0.9782 and 0.9857, respectively. These results will have a good prospect applicable to the design of primer and testing the primer specificity in genome database.

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.

Rational design of aptazyme riboswitches for efficient control of gene expression in mammalian cells

Efforts to control mammalian gene expression with ligand-responsive riboswitches have been hindered by lack of a general method for generating efficient switches in mammalian systems. Here we describe a rational-design approach that enables rapid development of efficient cis-acting aptazyme riboswitches. We identified communication-module characteristics associated with aptazyme functionality through analysis of a 32-aptazyme test panel. We then developed a scoring system that predicts an aptazymes’s activity by integrating three characteristics of communication-module bases: hydrogen bonding, base stacking, and distance to the enzymatic core. We validated the power and generality of this approach by designing aptazymes responsive to three distinct ligands, each with markedly wider dynamic ranges than any previously reported. These aptayzmes efficiently regulated adeno-associated virus (AAV)-vectored transgene expression in cultured mammalian cells and mice, highlighting one application of these broadly usable regulatory switches. Our approach enables efficient, protein-independent control of gene expression by a range of small molecules.

DOI:http://dx.doi.org/10.7554/eLife.18858.001

π-Cooperativity effect on the base stacking interactions in DNA: is there a novel stabilization factor coupled with base pairing H-bonds?

The results from absolutely localized molecular orbital (ALMO)-energy decomposition analysis (EDA) and ALMO-charge transfer analysis (CTA) at M06-2X/cc-pVTZ level reveal that double-proton transfer (DPT) reactions through base pairing H-bonds have nonignorable effects on the stacking energies of dinucleotide steps, which introduces us to a novel stabilization (or destabilization) factor in the DNA duplex. Thus, intra- and inter-strand base stacking interactions are coalesced with each other mediated by H-bridged quasirings between base pairs. Changes in stacking energies of dinucleotide steps depending on the positions of H atoms are due to variations in local aromaticities of individual nucleobases, manifesting π-cooperativity effects. CT analyses show that dispersion forces in dinucleotide steps can lead to radical changes in the redox properties of nucleobases, in particular those of adenine and guanine stacked dimers in a strand. Besides Watson-Crick rules, novel base pairing rules were propounded by considering CT results. According to these, additional base pairing through π-stacks of nucleobases in dinucleotide steps does not cause any intrinsic oxidative damage to the associated nucleobases throughout DPT.

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

A computational model for predicting RNA nearest neighbor free energy rankings has been expanded to include the nonstandard nucleotide inosine. The model uses average fiber diffraction data and molecular dynamic simulations to generate input geometries for Quantum mechanic calculations. This resulted in calculated intrastrand stacking, interstrand stacking, and hydrogen bonding energies that were combined to give total binding energies. Total binding energies for RNA dimer duplexes containing inosine were ranked and compared to experimentally determined free energy ranks for RNA duplexes containing inosine. Statistical analysis showed significant agreement between the computationally determined ranks and the experimentally determined ranks.

An experimentally-informed coarse-grained 3-Site-Per-Nucleotide model of DNA: structure, thermodynamics, and dynamics of hybridization

A new 3-Site-Per-Nucleotide coarse-grained model for DNA is presented. The model includes anisotropic potentials between bases involved in base stacking and base pair interactions that enable the description of relevant structural properties, including the major and minor grooves. In an improvement over available coarse-grained models, the correct persistence length is recovered for both ssDNA and dsDNA, allowing for simulation of non-canonical structures such as hairpins. DNA melting temperatures, measured for duplexes and hairpins by integrating over free energy surfaces generated using metadynamics simulations, are shown to be in quantitative agreement with experiment for a variety of sequences and conditions. Hybridization rate constants, calculated using forward-flux sampling, are also shown to be in good agreement with experiment. The coarse-grained model presented here is suitable for use in biological and engineering applications, including nucleosome positioning and DNA-templated engineering.

Computational approaches to predicting the impact of novel bases on RNA structure and stability

The use of computational modeling techniques to gain insight into nucleobase interactions has been a challenging endeavor to date. Accurate treatment requires the tackling of many challenges but also holds the promise of great rewards. The development of effective computational approaches to predict the binding affinities of nucleobases and analogues can, for example, streamline the process of developing novel nucleobase modifications, which should facilitate the development of new RNAi-based therapeutics. This brief review focuses on available computational approaches to predicting base pairing affinity in RNA-based contexts such as nucleobase-nucleobase interactions in duplexes and nucleobase-protein interactions. The challenges associated with such modeling along with potential future directions for the field are highlighted.

Thermodynamic contribution and nearest-neighbor parameters of pseudouridine-adenosine base pairs in oligoribonucleotides

Pseudouridine (Ψ) is the most common noncanonical nucleotide present in naturally occurring RNA and serves a variety of roles in the cell, typically appearing where structural stability is crucial to function. Ψ residues are isomerized from native uridine residues by a class of highly conserved enzymes known as pseudouridine synthases. In order to quantify the thermodynamic impact of pseudouridylation on U-A base pairs, 24 oligoribonucleotides, 16 internal and eight terminal Ψ-A oligoribonucleotides, were thermodynamically characterized via optical melting experiments. The thermodynamic parameters derived from two-state fits were used to generate linearly independent parameters for use in secondary structure prediction algorithms using the nearest-neighbor model. On average, internally pseudouridylated duplexes were 1.7 kcal/mol more stable than their U-A counterparts, and terminally pseudouridylated duplexes were 1.0 kcal/mol more stable than their U-A equivalents. Due to the fact that Ψ-A pairs maintain the same Watson-Crick hydrogen bonding capabilities as the parent U-A pair in A-form RNA, the difference in stability due to pseudouridylation was attributed to two possible sources: the novel hydrogen bonding capabilities of the newly relocated imino group as well as the novel stacking interactions afforded by the electronic configuration of the Ψ residue. The newly derived nearest-neighbor parameters for Ψ-A base pairs may be used in conjunction with other nearest-neighbor parameters for accurately predicting the most likely secondary structure of A-form RNA containing Ψ-A base pairs.

Mechanism of the cis-[Pt(1R,2R-DACH)(H2O)2]2+ intrastrand binding to the double-stranded (pGpG)·(CpC) dinucleotide in aqueous solution: a computational DFT study

A mechanism of the intrastrand 1,2-cross-link formation between the double-stranded pGpG·CpC dinucleotide (ds(pGpG)) and fully aquated oxaliplatin cis-[Pt(DACH)(H2O)2](2+) (DACH = cyclohexane-1R,2R-diamine) is presented. All structures of the reaction pathways including the transition states (TSs) were fully optimized in water solvent using DFT methodology with dispersion corrections. Both 5' → 3' and 3' → 5' binding directions were considered. In the first step there is a slight kinetic preference for 5'-guanine (5'G) monoadduct formation with an activation Gibbs free energy of 18.7 kcal/mol since the N7 center of the 5'G base is fully exposed to the solvent. On the other hand, the N7 atom of 3'-guanine (3'G) is sterically shielded by 5'G. The lowest energy path for formation of the 3'G monoadduct with an activation barrier of 19.3 kcal/mol is connected with a disruption of the 'DNA-like' structure of ds(pGpG). Monoadduct formation is the rate-determining process. The second step, chelate formation, is kinetically preferred in the 3' → 5' direction. The whole process of the platination is exergonic by up to -18.8 kcal/mol. Structural changes of ds(pGpG), charge transfer effects, and the influence of platination on the G·C base pair interaction strengths are also discussed in detail.

The Amber ff99 Force Field Predicts Relative Free Energy Changes for RNA Helix Formation

The ability of the Amber ff99 force field to predict relative free energies of RNA helix formation was investigated. The test systems were three hexaloop RNA hairpins with identical loops and varying stems. The potential of mean force of stretching the hairpins from the native state to an extended conformation was calculated with umbrella sampling. Because the hairpins have identical loop sequence, the differences in free energy changes are only from the stem composition. The Amber ff99 force field was able to correctly predict the order of stabilities of the hairpins, although the magnitude of the free energy change is larger than that determined by optical melting experiments. The two measurements cannot be compared directly because the unfolded state in the optical melting experiments is a random coil, while the end state in the umbrella sampling simulations was an elongated chain. The calculations can be compared to reference data by using a thermodynamic cycle. By applying the thermodynamic cycle to the transitions between the hairpins using simulations and nearest neighbor data, agreement was found to be within the sampling error of simulations, thus demonstrating that ff99 force field is able to accurately predict relative free energies of RNA helix formation.

Effects of non-nearest neighbors on the thermodynamic stability of RNA GNRA hairpin tetraloops

Currently, several models for predicting the secondary structure of RNA exist, one of which is free energy minimization using the Nearest Neighbor Model. This model predicts the lowest-free energy secondary structure from a primary sequence by summing the free energy contributions of the Watson-Crick nearest neighbor base pair combinations and any noncanonical secondary structure motif. The Nearest Neighbor Model also assumes that the free energy of the secondary structure motif is dependent solely on the identities of the nucleotides within the motif and the motif's nearest neighbors. To test the current assumption of the Nearest Neighbor Model that the non-nearest neighbors do not affect the stability of the motif, we optically melted different stem-loop oligonucleotides to experimentally determine their thermodynamic parameters. In each of these oligonucleotides, the hairpin loop sequence and the adjacent base pairs were held constant, while the first or second non-nearest neighbors were varied. The experimental results show that the thermodynamic contributions of the hairpin loop were dependent upon the identity of the first non-nearest neighbor, while the second non-nearest neighbor had a less obvious effect. These results were then used to create an updated model for predicting the thermodynamic contributions of a hairpin loop to the overall stability of the stem-loop structure.