Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations

A Test and Refinement of Folding Free Energy Nearest Neighbor Parameters for RNA Including N6-Methyladenosine

RNA folding free energy change parameters are widely used to predict RNA secondary structure and to design RNA sequences. These parameters include terms for the folding free energies of helices and loops. Although the full set of parameters has only been traditionally available for the four common bases and backbone, it is well known that covalent modifications of nucleotides are widespread in natural RNAs. Covalent modifications are also widely used in engineered sequences. We recently derived a full set of nearest neighbor terms for RNA that includes N6-methyladenosine (m6A). In this work, we test the model using 98 optical melting experiments, matching duplexes with or without N6-methylation of A. Most experiments place RRACH, the consensus site of N6-methylation, in a variety of contexts, including helices, bulge loops, internal loops, dangling ends, and terminal mismatches. For matched sets of experiments that include either A or m6A in the same context, we find that the parameters for m6A are as accurate as those for A. Across all experiments, the root mean squared deviation between estimated and experimental free energy changes is 0.67 kcal/mol. We used the new experimental data to refine the set of nearest neighbor parameter terms for m6A. These parameters enable prediction of RNA secondary structures including m6A, which can be used to model how N6-methylation of A affects RNA structure.

Molecular Simulations Matching Denaturation Experiments for N6-Methyladenosine

Post-transcriptional modifications are crucial for RNA function and can affect its structure and dynamics. Force-field-based classical molecular dynamics simulations are a fundamental tool to characterize biomolecular dynamics, and their application to RNA is flourishing. Here, we show that the set of force-field parameters for N⁶-methyladenosine (m⁶A) developed for the commonly used AMBER force field does not reproduce duplex denaturation experiments and, specifically, cannot be used to describe both paired and unpaired states. Then, we use reweighting techniques to derive new parameters matching available experimental data. The resulting force field can be used to properly describe paired and unpaired m⁶A in both syn and anti conformation, which thus opens the way to the use of molecular simulations to investigate the effects of N6 methylations on RNA structural dynamics.

Accelerating the Ensemble Convergence of RNA Hairpin Simulations with a Replica Exchange Structure Reservoir

RNA is a key participant in many biological processes, but studies of RNA using computer simulations lag behind those of proteins, largely due to less-developed force fields and the slow dynamics of RNA. Generating converged RNA ensembles for force field development and other studies remains a challenge. In this study, we explore the ability of replica exchange molecular dynamics to obtain well-converged conformational ensembles for two RNA hairpin systems in an implicit solvent. Even for these small model systems, standard REMD remains computationally costly, but coupling to a pre-generated structure library using the reservoir REMD approach provides a dramatic acceleration of ensemble convergence for both model systems. Such precise ensembles could facilitate RNA force field development and validation and applications of simulation to more complex RNA systems. The advantages and remaining challenges of applying R-REMD to RNA are investigated in detail.

Secondary structure prediction for RNA sequences including N6-methyladenosine

There is increasing interest in the roles of covalently modified nucleotides in RNA. There has been, however, an inability to account for modifications in secondary structure prediction because of a lack of software and thermodynamic parameters. We report the solution for these issues for N⁶-methyladenosine (m⁶A), allowing secondary structure prediction for an alphabet of A, C, G, U, and m⁶A. The RNAstructure software now works with user-defined nucleotide alphabets of any size. We also report a set of nearest neighbor parameters for helices and loops containing m⁶A, using experiments. Interestingly, N⁶-methylation decreases folding stability for adenosines in the middle of a helix, has little effect on folding stability for adenosines at the ends of helices, and increases folding stability for unpaired adenosines stacked on a helix. We demonstrate predictions for an N⁶-methylation-activated protein recognition site from MALAT1 and human transcriptome-wide effects of N⁶-methylation on the probability of adenosine being buried in a helix.

RNA folding free energy nearest neighbor parameters were determined for sequences with the nucleotide m⁶A. The RNAstructure software package can accommodate modified nucleotides, enabling secondary structure prediction of sequences with m⁶A.

Calculation of Energy for RNA/RNA and DNA/RNA Duplex Formation by Molecular Dynamics Simulation

Abstract

The development of approaches for predictive calculation of hybridization properties of various nucleic acid (NA) derivatives is the basis for the rational design of the NA-based constructs. Modern advances in computer modeling methods provide the feasibility of these calculations. We have analyzed the possibility of calculating the energy of DNA/RNA and RNA/RNA duplex formation using representative sets of complexes (65 and 75 complexes, respectively). We used the classical molecular dynamics (MD) method, the MMPBSA or MMGBSA approaches to calculate the enthalpy (ΔH°) component, and the quasi-harmonic approximation (Q-Harm) or the normal mode analysis (NMA) methods to calculate the entropy (ΔS°) contribution to the Gibbs energy ($$\Delta G_{{37}}^{^\circ }$$ ) of the NA complex formation. We have found that the MMGBSA method in the analysis of the MD trajectory of only the NA duplex and the empirical linear approximation allow calculation of the enthalpy of formation of the DNA, RNA, and hybrid duplexes of various lengths and GC content with an accuracy of 8.6%. Within each type of complex, the combination of rather efficient MMGBSA and Q-Harm approaches being applied to the trajectory of only the bimolecular complex makes it possible to calculate the $$\Delta G_{{37}}^{^\circ }$$ of the duplex formation with an error value of 10%. The high accuracy of predictive calculation for different types of natural complexes (DNA/RNA, DNA/RNA, and RNA/RNA) indicates the possibility of extending the considered approach to analogs and derivatives of nucleic acids, which gives a fundamental opportunity in the future to perform rational design of new types of NA-targeted sequence-specific compounds.

Deciphering nucleotide modification-induced structure and stability changes

Nucleotide modification in RNA controls a bevy of biological processes, including RNA degradation, gene expression, and gene editing. In turn, misregulation of modified nucleotides is associated with a host of chronic diseases and disorders. However, the molecular mechanisms driving these processes remain poorly understood. To partially address this knowledge gap, we used alchemical and temperature replica exchange molecular dynamics (TREMD) simulations on an RNA duplex and an analogous hairpin to probe the structural effects of modified and/or mutant nucleotides. The simulations successfully predict the modification/mutation-induced relative free energy change for complementary duplex formation, and structural analyses highlight mechanisms driving stability changes. Furthermore, TREMD simulations for a hairpin-forming RNA with and without modification provide reliable estimations of the energy landscape. Illuminating the impact of methylated and/or mutated nucleotides on the structure-function relationship and the folding energy landscape, the simulations provide insights into modification-induced alterations to the folding mechanics of the hairpin. The results here may be biologically significant as hairpins are widespread structure motifs that play critical roles in gene expression and regulation. Specifically, the tetraloop of the probed hairpin is phylogenetically abundant, and the stem mirrors a miRNA seed region whose modification has been implicated in epilepsy pathogenesis.

Free-Energy Calculation of Ribonucleic Inosines and Its Application to Nearest-Neighbor Parameters

Can current simulations quantitatively predict the stability of ribonucleic acids (RNAs)? In this research, we apply a free-energy perturbation simulation of RNAs containing inosine, a modified ribonucleic base, to the derivation of RNA nearest-neighbor parameters. A parameter set derived solely from 30 simulations was used to predict the free-energy difference of the RNA duplex with a mean unbiased error of 0.70 kcal/mol, which is a level of accuracy comparable to that obtained with parameters derived from 25 experiments. We further show that the error can be lowered to 0.60 kcal/mol by combining the simulation-derived free-energy differences with experimentally measured differences. This protocol can be used as a versatile method for deriving nearest-neighbor parameters of RNAs with various modified bases.

Molecular Dynamics Study of the Hybridization between RNA and Modified Oligonucleotides

MicroRNAs (miRNAs) are attractive drug candidates for many diseases as they can modulate the expression of gene networks. Recently, we discovered that DNAs targeting microRNA-22-3p (miR-22-3p) hold the potential for treating obesity and related metabolic disorders (type 2 diabetes mellitus, hyperlipidemia, and nonalcoholic fatty liver disease (NAFLD)) by turning fat-storing white adipocytes into fat-burning adipocytes. In this work, we explored the effects of chemical modifications, including phosphorothioate (PS), locked nucleic acid (LNA), and peptide nucleic acid (PNA), on the structure and energy of DNA analogs by using molecular dynamics (MD) simulations. To achieve a reliable prediction of the hybridization free energy, the AMOEBA polarizable force field and the free energy perturbation technique were employed. The calculated hybridization free energies are generally compatible with previous experiments. For LNA and PNA, the enhanced duplex stability can be explained by the preorganization mechanism, i.e., the single strands adopt stable helical structures similar to those in the duplex. For PS, the S and R isomers (Sp and Rp) have preferences for C2'-endo and C3'-endo sugar puckering conformations, respectively, and therefore Sp is less stable than Rp in DNA/RNA hybrids. In addition, the solvation penalty of Rp accounts for its destabilization effect. PS-LNA is similar to LNA as the sugar puckering is dominated by the locked sugar ring. This work demonstrated that MD simulations with polarizable force fields are useful for the understanding and design of modified nucleic acids.

Estimating Energy Parameters for RNA Secondary Structure Predictions Using Both Experimental and Computational Data

Computational RNA secondary structure prediction depends on a large number of nearest-neighbor free-energy parameters, including 10 parameters for Watson-Crick stacked base pairs that were estimated from experimental measurements of the free energies of 90 RNA duplexes. These experimental data are provided by time-consuming and cost-intensive experiments. In contrast, various modified nucleotides in RNAs, which would affect not only their structures but also functions, have been found, and rapid determination of energy parameters for a such modified nucleotides is needed. To reduce the high cost of determining energy parameters, we propose a novel method to estimate energy parameters from both experimental and computational data, where the computational data are provided by a recently developed molecular dynamics simulation protocol. We evaluate our method for Watson-Crick stacked base pairs, and show that parameters estimated from 10 experimental data items and 10 computational data items can predict RNA secondary structures with accuracy comparable to that using conventional parameters. The results indicate that the combination of experimental free-energy measurements and molecular dynamics simulations is capable of estimating the thermodynamic properties of RNA secondary structures at lower cost.

Chemically Accurate Relative Folding Stability of RNA Hairpins from Molecular Simulations

To benchmark RNA force fields, we compared the folding stabilities of three 12-nucleotide hairpin stem loops estimated by simulation to stabilities determined by experiment. We used umbrella sampling and a reaction coordinate of end-to-end (5' to 3' hydroxyl oxygen) distance to estimate the free energy change of the transition from the native conformation to a fully extended conformation with no hydrogen bonds between non-neighboring bases. Each simulation was performed four times using the AMBER FF99+bsc0+χ_OL3 force field, and each window, spaced at 1 Å intervals, was sampled for 1 μs, for a total of 552 μs of simulation. We compared differences in the simulated free energy changes to analogous differences in free energies from optical melting experiments using thermodynamic cycles where the free energy change between stretched and random coil sequences is assumed to be sequence-independent. The differences between experimental and simulated ΔΔ G° are, on average, 0.98 ± 0.66 kcal/mol, which is chemically accurate and suggests that analogous simulations could be used predictively. We also report a novel method to identify where replica free energies diverge along a reaction coordinate, thus indicating where additional sampling would most improve convergence. We conclude by discussing methods to more economically perform these simulations.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Exploring RNA structure and dynamics through enhanced sampling simulations

RNA function is intimately related to its structural dynamics. Molecular dynamics simulations are useful for exploring biomolecular flexibility but are severely limited by the accessible timescale. Enhanced sampling methods allow this timescale to be effectively extended in order to probe biologically relevant conformational changes and chemical reactions. Here, we review the role of enhanced sampling techniques in the study of RNA systems. We discuss the challenges and promises associated with the application of these methods to force-field validation, exploration of conformational landscapes and ion/ligand-RNA interactions, as well as catalytic pathways. Important technical aspects of these methods, such as the choice of the biased collective variables and the analysis of multi-replica simulations, are examined in detail. Finally, a perspective on the role of these methods in the characterization of RNA dynamics is provided.

In silico approaches to RNA aptamer design

RNA aptamers are ribonucleic acids that bind to specific target molecules. An RNA aptamer for a disease-related protein has great potential for development into a new drug. However, huge time and cost investments are required to develop an RNA aptamer into a pharmaceutical. Recently, SELEX combined with high-throughput sequencers (i.e., HT-SELEX) has been widely used to select candidate RNA aptamers that bind to a target protein with high affinity and specificity. After candidate selection, further optimizations such as shortening and modifying candidate sequences are performed. In these steps, in silico approaches are expected to reduce the time and cost associated with aptamer drug development. In this article, we review existing in silico approaches to RNA aptamer development, including a method for ranking the candidates of RNA aptamers from HT-SELEX data, clustering a huge number of aptamer sequences, and finding motifs amidst a set of significant RNA aptamers. It is expected that further studies in addition to these methods will be utilized for in silico RNA aptamer design, permitting a minimal number of experiments to be performed through the utilization of sophisticated computational methods.

Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.