FAMP: A Software Framework for FRET-Based Integrative Modeling of RNA.

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data

One of polymeric biological molecules called ribonucleic acid (RNA) is involved in diverse biological roles in regulation of gene expression. Generally, secondary structures of RNA determine their functions in living organisms. In medical field, various structures of synthetic RNAs are much needed at present. Recently, an RNA design tool has been developed to identify optimal RNA sequences for the desired interacting RNAs based on Genetic Algorithm (GA). In an attempt to improve the design tool, we applied Artificial Bee Colony (ABC) algorithm, which can outperform several population-based methods including GA. To achieve this goal, we developed an ABC-based RNA design tool and employed this tool to design 6 different interacting RNA models. The design performance from ABC-based tool for all models were compared with that from the existing GA-based tool. It was found that the ABC-based tool and the GA-based tools equally perform in all 6 models. Both tools can achieve the fitness score above 0.996. Although the ABC-based tool could be used to design interacting RNA more reliably, it seems to require more design time, especially in the models with larger RNA length. Thus, with further improvement, this tool shall be a promising tool for designing synthetic RNA devices for biomedical applications.

Ribonucleic acid (RNA) is a polymeric nucleic acid that is crucial for cellular function, regulating gene expression and encoding/decoding protein/DNA molecules. Recent discoveries of diverse functionality in non-coding RNAs have led to unprecedented demand for RNA 3D structure determination. With current technology, general, accurate prediction of 3D structures for large RNAs from the sequence remains computationally intractable. One of the principal challenges arises from the conformational flexibility of RNA, especially in loop/junction regions, which results in a rugged energy landscape. Several strategies exist to overcome this challenge, including incorporation of efficient experimental information and coarse-grained (CG) modeling to improve computational sampling of the structural ensemble. A second challenge is the inclusion of naturally modified derivatives of canonical RNA nucleotides in structure analysis. Most RNA prediction strategies rely upon the canonical nucleotides (adenine (A), uracil (U), guanine (G), and cytosine (C)), ignoring the effects of modified nucleotides on the structure and system dynamics. In general, RNA molecules contain rigid and flexible structural elements, which can be probed using efficient selective 2'-hydroxyl analyzed by primer extension (SHAPE) experiments. SHAPE experiments selectively modify flexible RNA nucleotides and can be processed to produce a characteristic reactivity profile for an RNA molecule that contains structural information. Incorporation of efficient experimental information, such as SHAPE, in predicting RNA 3D structure is highly desirable for overcoming the current knowledge gap between RNA sequence and 3D structure. In the first project, we introduce a physics-based model, the 3D structure-SHAPE relationship (3DSSR) model, to predict the SHAPE reactivity from the structure and show how this model may be used to sieve SHAPE-compatible structures from a pool of low-energy decoys and refine our predictions. In the second project, we compare 3DSSR performance to that of a convolutional neural network (CNN) trained on the SHAPE data and RNA structures, showing that 3DSSR outperforms the CNN given the limited data available. In the third project, we further improve the 3DSSR model, gaining deeper insights into the SHAPE reaction and biases. In the fourth project, we explore the theory underpinning the iterative simulated CG RNA folding model (IsRNA). In establishing the underlying mechanics driving the success of the model, we were able to clarify and improve the parameterization method while expanding the model interpretation, which should broaden application of the method to other biopolymers, such as protein. We found that the parameterization method follows statistical mechanics principles but also has a Bayesian interpretation. Further, we found that the parameterization process can benefit from application of the principle of maximum entropy, which improves simulation and parameterization efficiency. In the fifth project, we investigate the impact of nucleotide modification on the structure and configurational ensemble of RNA molecules using free energy calculations. By applying modifications to a common RNA hairpin, we estimate the impact on the stability of the structural ensemble, identifying specific interactions that drive changes to the potential of mean force (PMF) and showing the context and modification-dependence of the variable alterations to the structure stability.

Modeling of RNA secondary structures using two-way quantum finite automata

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.

The structure of ribonucleic acid (RNA) polymers is strongly dependent on the presence of, in particular Mg2+ cations to stabilize structural features. Only in high-resolution X-ray crystallography structures can ions be identified reliably. Here, we perform molecular dynamics simulations of 24 RNA structures with varying ion concentrations. Twelve of the structures were helical and the others complex folded. The aim of the study is to predict ion positions but also to evaluate the impact of different types of ions (Na+ or Mg2+) and the ionic strength on structural stability and variations of RNA. As a general conclusion Mg2+ is found to conserve the experimental structure better than Na+ and, where experimental ion positions are available, they can be reproduced with reasonable accuracy. If a large surplus of ions is present the added electrostatic screening makes prediction of binding-sites less reproducible. Distinct differences in ion-binding between helical and complex folded structures are found. The strength of binding (ΔG‡ for breaking RNA atom-ion interactions) is found to differ between roughly 10 and 26 kJ/mol for the different RNA atoms. Differences in stability between helical and complex folded structures and of the influence of metal ions on either are discussed.

We present the results of large-scale molecular simulations, run over several tens of nanoseconds, of 25-mer sequences of single-stranded ribonucleic acid (RNA) in bulk water and at the surface of three hydrated positively charged MgAl layered double hydroxide (LDH) minerals. The three LDHs differ in surface charge density, through varying the number of isomorphic Al substitutions. Over the course of the simulations, RNA adsorbs tightly to the LDH surface through electrostatic interactions between the charged RNA phosphate groups and the alumina charge sites present in the LDH sheet. The RNA strands arrange parallel to the surface with the base groups aligning normal to the surface and exposed to the bulk aqueous region. This templating effect makes LDH a candidate for amplifying the population of a known RNA sequence from a small number of RNAs. The structure and interactions of RNA at a positively charged, hydroxylated LDH surface were compared with those of RNA at a positively charged calcium montmorillonite surface, allowing us to establish the comparative effect of complexation and water structure at hydroxide and silicate surfaces. The systems were studied by computing radial distribution functions, atom density plots, and radii of gyration, as well as visualization. An observation pertinent to the role of these minerals in prebiotic chemistry is that, for a given charge density on the mineral surface, different genetic sequences of RNA adopt different configurations.

The development of an effective biological (bio) agent detection capability based upon terahertz (THz) frequency absorption spectra will require insight into how the constituent cellular components contribute to the overall THz signature. In this work, the specific contribution of ribonucleic acid (RNA) to THz spectra is analyzed in detail. Previously, it has only been possible to simulate partial fragments of the RNA (or DNA) structures due to the excessive computational demands. For the first time, the molecular structure of the entire transfer RNA (tRNA) molecule of E. coli was simulated and the associated THz signature was derived theoretically. The tRNA that binds amino acid tyrosine (tRNAtyr) was studied. Here, the molecular structure was optimized using the potential energy minimization and molecular dynamical (MD) simulations. Solvation effects (water molecules) were also included explicitly in the MD simulations. To verify that realistic molecular signatures were simulated, a parallel experimental study of tRNAs of E. coli was also conducted. Two very similar molecules, valine and tyrosine tRNA were investigated experimentally. Samples were prepared in the form of water solutions with the concentrations in the range 0.01-1 mg/ml. A strong correlation of the measured THz signatures associated with valine tRNA and tyrosine tRNA was observed. These findings are consistent with the structural similarity of the two tRNAs. The calculated THz signature of the tyrosine tRNA of E. coli reproduces many features of our measured spectra, and, therefore, provides valuable new insights into bio-agent detection.

CataCleave probes are catalytically cleavable fluorescence probes having a chimeric deoxyribonucleic acid (DNA)-ribonucleic acid (RNA)-DNA structure that can be used for real-time detection of single nucleotide polymorphisms (SNPs), insertions, and deletions. Fluorescent donor emission is normally quenched by Förster resonance energy transfer (FRET). Upon binding to the target, if the RNA/DNA hybrid is correctly base-paired, ribonuclease (RNase) H will cleave the RNA moiety and the probe fragments will dissociate. FRET is lost and the donor fluorescence signal is recovered. A single-base mismatch within the hybrid region causes probe cleavage to be significantly reduced. We designed CataCleave probes to detect SNPs located in the insulin-like growth factor 2 (IGF-2) gene and at position 702 within the NOD2/CARD15 gene. Probes were also designed to detect a six-basepair deletion in the amelogenin gene and a partially methylated target DNA. Discrimination between wild-type and SNP is demonstrated for both genes in homogeneous reactions under isothermal and temperature cycling conditions. These probes were also able to identify a multibase deletion and methylated DNA. Cleavage rates were proportional to target concentration. Probe length and position of fluorescent labels may also be modified for use in multiplexing high-throughput SNP assays. This represents a novel method for the detection of SNPs.

In vivo, large-scale preparation of uniformly (15)N- and site-specifically (13)C-labeled homogeneous, recombinant RNA for NMR studies.

DNA four‐way junctions (4WJ) are central intermediates in processes such as replication and recombination. This project studies how different conformations of the junction relate to function, and how proteins interact with the junction to enhance or repress function. Previously, we have examined the 4WJ structure in the presence of different ions, and used Förster Resonance Energy Transfer (FRET) to map the binding site of the protein HU onto the junction. These FRET measurements were used to build four different models for the binding of HU protein to the Holliday Junction, an interaction with a stoichiometry of 2 to 1. Molecular dynamics simulations ensured that the models were energetically stable. The three stable models included HU bound to the major groove, minor groove, or face‐to‐face. The fourth model, which contained an open junction, though stable, did not agree with the FRET data acquired for the system. Further FRET measurements are needed to narrow the possible orientations for HU binding the junction. To address the function of the protein‐bound junction, a new, migrating 4WJ is being created and characterized. This 4WJ will be analyzed by FRET in the presence of ions and proteins to compare to the characteristics of the stationary junction. The free energy of both the mobile and immobile junctions will be analyzed by molecular dynamics to predict stability of each construct alone and when complexed with HU.This research is supported by an the NSF (MCB‐0316625; MCB‐0843656 to I.M.), the NIH (GM‐076490 to D.L.B.), and by NIGMS Training grant T32GM08271.

Interaction of a two-transmembrane-helix peptide with lipid bilayers and dodecyl sulfate micelles

Förster resonance energy transfer (FRET) is analyzed in terms of distance- and orientation-dependent interactions between the transition dipole moments of the involved donor and acceptor molecules. However, the ideal dipole approximation (IDA) is known to fail at short donor-acceptor distances. In this work, we model FRET in a Cy5- and Alexa Fluor 488-labeled double-stranded RNA by means of combined molecular dynamics (MD) simulations and quantum-chemical calculations involving the IDA as well as the more sophisticated monomer transition density (MTD) approach. To this end, the relaxed ground-state geometries of the dyes were fitted to the MD-based structures. Although substantial deviations between IDA and MTD results can be observed for individual snapshots, the statistical impact of the failure on the FRET rates is negligible in the chosen examples. Our results clearly demonstrate that the IDA-based Förster model can still be applied to systems with small donor-acceptor distances, provided that the dyes are not trapped in arrangements with a high IDA failure and that the distribution of the relative transition dipole orientations is fairly isotropic.

Riboswitches are structured ribonucleic acid (RNA)segments that act as specific sensors for small molecules in bacterial metabolism. Due to the flexible nature of these highly charged macromolecules, molecular dynamics simulations are instrumental to investigating the mechanistic details of their regulatory function. In the present study, the guanidine-I riboswitch serves as an example of how atomistic simulations can shed light on the effect of ions on the structure and dynamics of RNA and on ligand binding. Relying on two orthologous crystal structures from different bacterial species, it is demonstrated how the ion setup crucially determines whether the simulation yields meaningful insights into the conformational stability of the RNA, functionally relevant residues and RNA-ligand interactions. The ion setup in this context includes diffuse ions in solution and bound ions associated directly with the RNA, in particular a triad of 2 Mg2+ ions and a K+ ion in close proximity to the guanidinium binding site. A detailed investigation of the binding pocket reveals that the K+ from the ion triad plays a decisive role in stabilizing the ligand binding by stabilizing important localized interactions, which in turn contribute to the overall shape of the folded state of the RNA.