The jerky and knotty dynamics of RNA

Effects of mRNA conformational switching on translational noise in gene circuits

Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.

Structure, folding and flexibility of co-transcriptional RNA origami

RNA origami is a method for designing RNA nanostructures that can self-assemble through co-transcriptional folding with applications in nanomedicine and synthetic biology. However, to advance the method further, an improved understanding of RNA structural properties and folding principles is required. Here we use cryogenic electron microscopy to study RNA origami sheets and bundles at sub-nanometre resolution revealing structural parameters of kissing-loop and crossover motifs, which are used to improve designs. In RNA bundle designs, we discover a kinetic folding trap that forms during folding and is only released after 10 h. Exploration of the conformational landscape of several RNA designs reveal the flexibility of helices and structural motifs. Finally, sheets and bundles are combined to construct a multidomain satellite shape, which is characterized by individual-particle cryo-electron tomography to reveal the domain flexibility. Together, the study provides a structural basis for future improvements to the design cycle of genetically encoded RNA nanodevices.

Design Approaches to Expand the Toolkit for Building Cotranscriptionally Encoded RNA Strand Displacement Circuits

Cotranscriptionally encoded RNA strand displacement (ctRSD) circuits are an emerging tool for programmable molecular computation, with potential applications spanning in vitro diagnostics to continuous computation inside living cells. In ctRSD circuits, RNA strand displacement components are continuously produced together via transcription. These RNA components can be rationally programmed through base pairing interactions to execute logic and signaling cascades. However, the small number of ctRSD components characterized to date limits circuit size and capabilities. Here, we characterize over 200 ctRSD gate sequences, exploring different input, output, and toehold sequences and changes to other design parameters, including domain lengths, ribozyme sequences, and the order in which gate strands are transcribed. This characterization provides a library of sequence domains for engineering ctRSD components, i.e., a toolkit, enabling circuits with up to 4-fold more inputs than previously possible. We also identify specific failure modes and systematically develop design approaches that reduce the likelihood of failure across different gate sequences. Lastly, we show the ctRSD gate design is robust to changes in transcriptional encoding, opening a broad design space for applications in more complex environments. Together, these results deliver an expanded toolkit and design approaches for building ctRSD circuits that will dramatically extend capabilities and potential applications.

Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions

The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network's various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. These elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

Cotranscriptionally encoded RNA strand displacement circuits

Engineered molecular circuits that process information in biological systems could address emerging human health and biomanufacturing needs. However, such circuits can be difficult to rationally design and scale. DNA-based strand displacement reactions have demonstrated the largest and most computationally powerful molecular circuits to date but are limited in biological systems due to the difficulty in genetically encoding components. Here, we develop scalable cotranscriptionally encoded RNA strand displacement (ctRSD) circuits that are rationally programmed via base pairing interactions. ctRSD circuits address the limitations of DNA-based strand displacement circuits by isothermally producing circuit components via transcription. We demonstrate circuit programmability in vitro by implementing logic and amplification elements, as well as multilayer cascades. Furthermore, we show that circuit kinetics are accurately predicted by a simple model of coupled transcription and strand displacement, enabling model-driven design. We envision ctRSD circuits will enable the rational design of powerful molecular circuits that operate in biological systems, including living cells.

Transient Protein-RNA Interactions Guide Nascent Ribosomal RNA Folding

Ribosome assembly is an efficient but complex and heterogeneous process during which ribosomal proteins assemble on the nascent rRNA during transcription. Understanding how the interplay between nascent RNA folding and protein binding determines the fate of transcripts remains a major challenge. Here, using single-molecule fluorescence microscopy, we follow assembly of the entire 3' domain of the bacterial small ribosomal subunit in real time. We find that co-transcriptional rRNA folding is complicated by the formation of long-range RNA interactions and that r-proteins self-chaperone the rRNA folding process prior to stable incorporation into a ribonucleoprotein (RNP) complex. Assembly is initiated by transient rather than stable protein binding, and the protein-RNA binding dynamics gradually decrease during assembly. This work questions the paradigm of strictly sequential and cooperative ribosome assembly and suggests that transient binding of RNA binding proteins to cellular RNAs could provide a general mechanism to shape nascent RNA folding during RNP assembly.

A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past 40 years. Although many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges arising from two main factors: 1) no physical model exists to estimate the loop entropies of complex intramolecular pseudoknots, and 2) their NP-complete enumeration has impeded their study. Here, we address both challenges. First, we develop a polymer physics model that can address arbitrarily complex pseudoknots using only two parameters corresponding to concrete physical quantities-over an order of magnitude fewer than the sparsest state-of-the-art phenomenological methods. Second, by coupling this model to exhaustive enumeration of the set of possible structures, we compute the entire free energy landscape of secondary structures resulting from a primary RNA sequence. We demonstrate that for RNA structures of ∼80 nucleotides, with minimal heuristics, the complete enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem. We further show that despite our loop entropy model's parametric sparsity, it performs better than or on par with previously published methods in predicting both pseudoknotted and non-pseudoknotted structures on a benchmark data set of RNA structures of ≤80 nucleotides. We suggest ways in which the accuracy of the model can be further improved.

Viroid research and its significance for RNA technology and basic biochemistry

Viroids were described 47 years ago as the smallest RNA molecules capable of infecting plants and autonomously self-replicating without an encoded protein. Work on viroids initiated the development of a number of innovative methods. Novel chromatographic and gelelectrophoretic methods were developed for the purification and characterization of viroids; these methods were later used in molecular biology, gene technology and in prion research. Theoretical and experimental studies of RNA folding demonstrated the general biological importance of metastable structures, and nuclear magnetic resonance spectroscopy of viroid RNA showed the partially covalent nature of hydrogen bonds in biological macromolecules. RNA biochemistry and molecular biology profited from viroid research, such as in the detection of RNA as template of DNA-dependent polymerases and in mechanisms of gene silencing. Viroids, the first circular RNA detected in nature, are important for studies on the much wider spectrum of circular RNAs and other non-coding RNAs.

Real-time assembly of ribonucleoprotein complexes on nascent RNA transcripts

Cellular protein-RNA complexes assemble on nascent transcripts, but methods to observe transcription and protein binding in real time and at physiological concentrations are not available. Here, we report a single-molecule approach based on zero-mode waveguides that simultaneously tracks transcription progress and the binding of ribosomal protein S15 to nascent RNA transcripts during early ribosome biogenesis. We observe stable binding of S15 to single RNAs immediately after transcription for the majority of the transcripts at 35 °C but for less than half at 20 °C. The remaining transcripts exhibit either rapid and transient binding or are unable to bind S15, likely due to RNA misfolding. Our work establishes the foundation for studying transcription and its coupled co-transcriptional processes, including RNA folding, ligand binding, and enzymatic activity such as in coupling of transcription to splicing, ribosome assembly or translation.

The early steps of ribosome assembly occur co-transcriptionally on the nascent ribosomal RNA. Here the authors demonstrate an approach that allows simultaneous monitoring of transcription and ribosomal protein assembly at the single-molecule level in real time.

Ensemble simulations: folding, unfolding and misfolding of a high-efficiency frameshifting RNA pseudoknot

Massive all-atom molecular dynamics simulations were conducted across a distributed computing network to study the folding, unfolding, misfolding and conformational plasticity of the high-efficiency frameshifting double mutant of the 26 nt potato leaf roll virus RNA pseudoknot. Our robust sampling, which included over 40 starting structures spanning the spectrum from the extended unfolded state to the native fold, yielded nearly 120 μs of cumulative sampling time. Conformational microstate transitions on the 1.0 ns to 10.0 μs timescales were observed, with post-equilibration sampling providing detailed representations of the conformational free energy landscape and the complex folding mechanism inherent to the pseudoknot motif. Herein, we identify and characterize two alternative native structures, three intermediate states, and numerous misfolded states, the latter of which have not previously been characterized via atomistic simulation techniques. While in line with previous thermodynamics-based models of a general RNA folding mechanism, our observations indicate that stem-strand-sequence-separation may serve as an alternative predictor of the order of stem formation during pseudoknot folding. Our results contradict a model of frameshifting based on structural rigidity and resistance to mechanical unfolding, and instead strongly support more recent studies in which conformational plasticity is identified as a determining factor in frameshifting efficiency.

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones.

Efficient approximations of RNA kinetics landscape using non-redundant sampling

MOTIVATION

Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computational demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, obtained using sampling strategies that strive to generate the key landmarks of the landscape topology. However, such methods are impeded by a large level of redundancy within sampled sets. Such a redundancy is uninformative, and obfuscates important intermediate states, leading to an incomplete vision of RNA dynamics.

RESULTS

We introduce RNANR, a new set of algorithms for the exploration of RNA kinetics landscapes at the secondary structure level. RNANR considers locally optimal structures, a reduced set of RNA conformations, in order to focus its sampling on basins in the kinetic landscape. Along with an exhaustive enumeration, RNANR implements a novel non-redundant stochastic sampling, and offers a rich array of structural parameters. Our tests on both real and random RNAs reveal that RNANR allows to generate more unique structures in a given time than its competitors, and allows a deeper exploration of kinetics landscapes.

AVAILABILITY AND IMPLEMENTATION

RNANR is freely available at https://project.inria.fr/rnalands/rnanr .

CONTACT

yann.ponty@lix.polytechnique.fr.

Using RNA as Molecular Code for Programming Cellular Function

RNA is involved in a wide-range of important molecular processes in the cell, serving diverse functions: regulatory, enzymatic, and structural. Together with its ease and predictability of design, these properties can lead RNA to become a useful handle for biological engineers with which to control the cellular machinery. By modifying the many RNA links in cellular processes, it is possible to reprogram cells toward specific design goals. We propose that RNA can be viewed as a molecular programming language that, together with protein-based execution platforms, can be used to rewrite wide ranging aspects of cellular function. In this review, we catalogue developments in the use of RNA parts, methods, and associated computational models that have contributed to the programmability of biology. We discuss how RNA part repertoires have been combined to build complex genetic circuits, and review recent applications of RNA-based parts and circuitry. We explore the future potential of RNA engineering and posit that RNA programmability is an important resource for firmly establishing an era of rationally designed synthetic biology.

Coarse-grained modeling of RNA 3D structure

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.

StemSearch: RNA search tool based on stem identification and indexing

The discovery and functional analysis of noncoding RNA (ncRNA) systems in different organisms motivates the development of tools for aiding ncRNA research. Several tools exist that search for occurrences of a given RNA structural profile in genomic sequences. Yet, there is a need for an "RNA BLAST" tool, i.e., a tool that takes a putative functional RNA sequence as input, and efficiently searches for similar sequences in genomic databases, taking into consideration potential secondary structure features of the input query sequence. This work aims at providing such a tool. Our tool, denoted StemSearch, is based on a structural representation of an RNA sequence by its potential stems. Potential stems in genomic sequences are identified in a preprocessing stage, and indexed. A user-provided query sequence is likewise processed, and stems from the target genomes that are similar to the query stems are retrieved from the index. Then, relevant genomic regions are identified and ranked according to their similarity to the query stem-set while enforcing conservation of cross-stem topology. Experiments using RFAM families show significantly improved recall for StemSearch over BLAST, with small loss of precision. We further demonstrate our system's capability to handle eukaryotic genomes by successfully searching for members of the 7SK family in chromosome 2 of the human genome. StemSearch is freely available on the web at: http://www.cs.bgu.ac.il/∼negevcb/StemSearch.

A single-stranded architecture for cotranscriptional folding of RNA nanostructures

Artificial DNA and RNA structures have been used as scaffolds for a variety of nanoscale devices. In comparison to DNA structures, RNA structures have been limited in size, but they also have advantages: RNA can fold during transcription and thus can be genetically encoded and expressed in cells. We introduce an architecture for designing artificial RNA structures that fold from a single strand, in which arrays of antiparallel RNA helices are precisely organized by RNA tertiary motifs and a new type of crossover pattern. We constructed RNA tiles that assemble into hexagonal lattices and demonstrated that lattices can be made by annealing and/or cotranscriptional folding. Tiles can be scaled up to 660 nucleotides in length, reaching a size comparable to that of large natural ribozymes.

Programmable folding of fusion RNA in vivo and in vitro driven by pRNA 3WJ motif of phi29 DNA packaging motor

Misfolding and associated loss of function are common problems in constructing fusion RNA complexes due to changes in energy landscape and the nearest-neighbor principle. Here we report the incorporation and application of the pRNA-3WJ motif of the phi29 DNA packaging motor into fusion RNA with controllable and predictable folding. The motif included three discontinuous ∼18 nucleotide (nt) fragments, displayed a distinct low folding energy (Shu D et al., Nature Nanotechnology, 2011, 6:658–667), and folded spontaneously into a leading core that enabled the correct folding of other functionalities fused to the RNA complex. Three individual fragments dispersed at any location within the sequence allowed the other RNA functional modules to fold into their original structures with authentic functions, as tested by Hepatitis B virus ribozyme, siRNA, and aptamers for malachite green (MG), spinach, and streptavidin (STV). Only nine complementary nucleotides were present for any two of the three ∼18-nt fragments, but the three 9 bp branches were so powerful that they disrupted other double strands with more than 15 bp within the fusion RNA. This system enabled the production of fusion complexes harboring multiple RNA functionalities with correct folding for potential applications in biotechnology, nanomedicine and nanotechnology. We also applied this system to investigate the principles governing the folding of RNA in vivo and in vitro. Temporal production of RNA sequences during in vivo transcription caused RNA to fold into different conformations that could not be predicted with routine principles derived from in vitro studies.

Ultrastable pRNA hexameric ring gearing hexameric phi29 DNA-packaging motor by revolving without rotating and coiling

Biomotors have previously been classified into two categories: linear and rotational motors. It has long been popularly believed that viral DNA packaging motors are rotation motors. We have recently found that the DNA-packaging motor of bacteriophage phi29 uses a third mechanism: revolution without rotation. phi29 motor consists of three-coaxial rings of hexameric RNA, a hexameric ATPase, and a dodecameric channel. The motor uses six ATP to revolve one helical turn of dsDNA around the hexameric ring of ATPase gp16. Each dodecameric segment tilts at a 30°-angle and runs anti-parallel to the dsDNA helix to facilitate translation in one direction. The negatively charged phosphate backbone interacts with four positively charged lysine rings, resulting in four steps of transition. This review will discuss how the novel pRNA meets motor requirements for translocation concerning structure, stoichiometry, and thermostability; how pRNA studies have led to the generation of the concept of RNA nanotechnology; and how pRNA is fabricated into nanoparticles to deliver siRNA, miRNA, and ribozymes to cancer and virus-infected cells.

Measurement and modeling of intrinsic transcription terminators

The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

Uniqueness, advantages, challenges, solutions, and perspectives in therapeutics applying RNA nanotechnology

The field of RNA nanotechnology is rapidly emerging. RNA can be manipulated with the simplicity characteristic of DNA to produce nanoparticles with a diversity of quaternary structures by self-assembly. Additionally RNA is tremendously versatile in its function and some RNA molecules display catalytic activities much like proteins. Thus, RNA has the advantage of both worlds. However, the instability of RNA has made many scientists flinch away from RNA nanotechnology. Other concerns that have deterred the progress of RNA therapeutics include the induction of interferons, stimulation of cytokines, and activation of other immune systems, as well as short pharmacokinetic profiles in vivo. This review will provide some solutions and perspectives on the chemical and thermodynamic stability, in vivo half-life and biodistribution, yield and production cost, in vivo toxicity and side effect, specific delivery and targeting, as well as endosomal trapping and escape.

The kinetics and folding pathways of intramolecular G-quadruplex nucleic acids

The folding kinetics of G-quadruplex forming sequences is critical to their capacity to influence biological function. While G-quadruplex structure and stability have been relatively well studied, little is known about the kinetics of their folding. We employed a stopped-flow mixing technique to systematically investigate the potassium-dependent folding kinetics of telomeric RNA and DNA G-quadruplexes and RNA G-quadruplexes containing only two G-quartets formed from sequences r[(GGA)(3)GG] and r[(GGUUA)(3)GG]. Our findings suggest a folding mechanism that involves two kinetic steps with initial binding of a single K(+), irrespective of the number of G-quartets involved or whether the G-quadruplex is formed from RNA or DNA. The folding rates for telomeric RNA and DNA G-quadruplexes are comparable at near physiological [K(+)] (90 mM) (τ = ~60 ms). The folding of a 2-quartet RNA G-quadruplex with single nucleotide A loops is considerably slower (τ = ~700 ms), and we found that the time required to fold a UUA looped variant (τ > 100 s, 500 mM K(+)) exceeds the lifetimes of some regulatory RNAs. We discuss the implications of these findings with respect to the fundamental properties of G-quadruplexes and their potential functions in biology.

Efficient procedures for the numerical simulation of mid-size RNA kinetics

Motivation

Methods for simulating the kinetic folding of RNAs by numerically solving the chemical master equation have been developed since the late 90's, notably the programs Kinfold and Treekin with Barriers that are available in the Vienna RNA package. Our goal is to formulate extensions to the algorithms used, starting from the Gillespie algorithm, that will allow numerical simulations of mid-size (~ 60–150 nt) RNA kinetics in some practical cases where numerous distributions of folding times are desired. These extensions can contribute to analyses and predictions of RNA folding in biologically significant problems.

Results

By describing in a particular way the reduction of numerical simulations of RNA folding kinetics into the Gillespie stochastic simulation algorithm for chemical reactions, it is possible to formulate extensions to the basic algorithm that will exploit memoization and parallelism for efficient computations. These can be used to advance forward from the small examples demonstrated to larger examples of biological interest.

Software

The implementation that is described and used for the Gillespie algorithm is freely available by contacting the authors, noting that the efficient procedures suggested may also be applicable along with Vienna's Kinfold.

Uniqueness, advantages, challenges, solutions, and perspectives in therapeutics applying RNA nanotechnology

The field of RNA nanotechnology is rapidly emerging. RNA can be manipulated with the simplicity characteristic of DNA to produce nanoparticles with a diversity of quaternary structures by self-assembly. Additionally RNA is tremendously versatile in its function and some RNA molecules display catalytic activities much like proteins. Thus, RNA has the advantage of both worlds. However, the instability of RNA has made many scientists flinch away from RNA nanotechnology. Other concerns that have deterred the progress of RNA therapeutics include the induction of interferons, stimulation of cytokines, and activation of other immune systems, as well as short pharmacokinetic profiles in vivo. This review will provide some solutions and perspectives on the chemical and thermodynamic stability, in vivo half-life and biodistribution, yield and production cost, in vivo toxicity and side effect, specific delivery and targeting, as well as endosomal trapping and escape.

Statistical mechanical modeling of RNA folding: from free energy landscape to tertiary structural prediction

In spite of the success of computational methods for predicting RNA secondary structure, the problem of predicting RNA tertiary structure folding remains. Low-resolution structural models show promise as they allow for rigorous statistical mechanical computation for the conformational entropies, free energies, and the coarse-grained structures of tertiary folds. Molecular dynamics refinement of coarse-grained structures leads to all-atom 3D structures. Modeling based on statistical mechanics principles also has the unique advantage of predicting the full free energy landscape, including local minima and the global free energy minimum. The energy landscapes combined with the 3D structures form the basis for quantitative predictions of RNA functions. In this chapter, we present an overview of statistical mechanical models for RNA folding and then focus on a recently developed RNA statistical mechanical model -- the Vfold model. The main emphasis is placed on the physics underpinning the models, the computational strategies, and the connections to RNA biology.

Twins, quadruplexes, and more: functional aspects of native and engineered RNA self-assembly in vivo

The primacy and power of RNA in governing many processes of life has begun to be more fully appreciated in both the discovery and inventive sciences. A variety of RNA interactions regulate gene expression, and structural self-assembly underlies many of these processes. The understanding sparked by these discoveries has inspired and informed the engineering of novel RNA structures, control elements, and genetic circuits in cells. Many of these engineered systems are built up fundamentally from RNA–RNA interactions, often combining modular, rational design with functional selection and screening. It is therefore useful to review the particular class of RNA-based regulatory mechanisms that rely on RNA self-assembly either through homomeric (self–self) or heteromeric (self–nonself) RNA–RNA interactions. Structures and sequence elements within individual RNAs create a basis for the pairing interactions, and in some instances can even lead to the formation of RNA polymers. Example systems of dimers, multimers, and polymers are reviewed in this article in the context of natural systems, wherein the function and impact of self-assemblies are understood. Following this, a brief overview is presented of specific engineered RNA self-assembly systems implemented in vivo, with lessons learned from both discovery and engineering approaches to RNA–RNA self-assembly.

Model-driven engineering of RNA devices to quantitatively program gene expression

The models and simulation tools available to design functionally complex synthetic biological devices are very limited. We formulated a design-driven approach that used mechanistic modeling and kinetic RNA folding simulations to engineer RNA-regulated genetic devices that control gene expression. Ribozyme and metabolite-controlled, aptazyme-regulated expression devices with quantitatively predictable functions were assembled from components characterized in vitro, in vivo, and in silico. The models and design strategy were verified by constructing 28 Escherichia coli expression devices that gave excellent quantitative agreement between the predicted and measured gene expression levels (r = 0.94). These technologies were applied to engineer RNA-regulated controls in metabolic pathways. More broadly, we provide a framework for studying RNA functions and illustrate the potential for the use of biochemical and biophysical modeling to develop biological design methods.

A boost for the emerging field of RNA nanotechnology

This Nano Focus article highlights recent advances in RNA nanotechnology as presented at the First International Conference of RNA Nanotechnology and Therapeutics, which took place in Cleveland, OH, USA (October 23-25, 2010) ( http://www.eng.uc.edu/nanomedicine/RNA2010/ ), chaired by Peixuan Guo and co-chaired by David Rueda and Scott Tenenbaum. The conference was the first of its kind to bring together more than 30 invited speakers in the frontier of RNA nanotechnology from France, Sweden, South Korea, China, and throughout the United States to discuss RNA nanotechnology and its applications. It provided a platform for researchers from academia, government, and the pharmaceutical industry to share existing knowledge, vision, technology, and challenges in the field and promoted collaborations among researchers interested in advancing this emerging scientific discipline. The meeting covered a range of topics, including biophysical and single-molecule approaches for characterization of RNA nanostructures; structure studies on RNA nanoparticles by chemical or biochemical approaches, computation, prediction, and modeling of RNA nanoparticle structures; methods for the assembly of RNA nanoparticles; chemistry for RNA synthesis, conjugation, and labeling; and application of RNA nanoparticles in therapeutics. A special invited talk on the well-established principles of DNA nanotechnology was arranged to provide models for RNA nanotechnology. An Administrator from National Institutes of Health (NIH) National Cancer Institute (NCI) Alliance for Nanotechnology in Cancer discussed the current nanocancer research directions and future funding opportunities at NCI. As indicated by the feedback received from the invited speakers and the meeting participants, this meeting was extremely successful, exciting, and informative, covering many groundbreaking findings, pioneering ideas, and novel discoveries.

Computing the partition function for kinetically trapped RNA secondary structures

An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in time and space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server and source code available at http://bioinformatics.bc.edu/clotelab/RNAlocopt/.