Efficient computation of co-transcriptional RNA-ligand interaction dynamics

Landscape Zooming toward the Prediction of RNA Cotranscriptional Folding

RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here, we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into "partitions" based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the interpartition kinetic network, and for each interpartition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of an RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.

RNAxplorer: Harnessing the Power of Guiding Potentials to Sample RNA Landscapes

Motivation

Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space.

Method

We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling toward underrepresented or unexplored regions of the structure space.

Results

We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics.

Availabilityand implementation

https://github.com/ViennaRNA/RNAxplorer/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quantitative modeling of the function of kinetically driven transcriptional riboswitches

Riboswitches are cis-acting regulatory mRNA elements in bacteria, that modulate the expression of their associated genes in response to a cognate metabolite, operating either on the level of translation or transcription. Transcriptional riboswitches have to fold into functional structures as they are being synthesized and, only if transcription rates and ligand binding kinetics match, structured transcription intermediates are enabled to undergo ligand-dependent conformational refolding as a prerequisite for ligand-mediated gene expression. Therefore, transcription rates are of essential importance for functional riboswitch-mediated gene regulation. Here, we propose a generalized modeling framework for the kinetic mechanisms of transcriptional riboswitches. The formalism accommodates time-dependent transcription rates and changes of metabolite concentration and permits incorporation of variations in transcription rate depending on transcript length. We derive explicit analytical expressions for the fraction of transcripts that determine repression or activation of gene expression as a function of pause site location and its slowing down of transcription for the case of the (2'dG)-sensing riboswitch from Mesoplasma florum. Our modeling challenges the current view on the exclusive importance of metabolite binding to transcripts containing only the aptamer domain. Numerical simulations of transcription proceeding in a continuous manner under time-dependent changes of metabolite concentration further suggest that rapid modulations in concentration result in a reduced dynamic range for riboswitch function regardless of transcription rate, while a combination of slow modulations and small transcription rates ensures a wide range of finely tuneable regulatory outcomes.

Ant colony optimization for predicting RNA folding pathways

RNA folding dynamics plays important roles in various functions of RNAs. To date, coarse-grained modeling has been successfully employed to simulate RNA folding dynamics on the energy landscape composed of secondary structures. In such a modeling, the energy barrier height between metastable structures is a key parameter that crucially affects the simulation results. Although a number of approaches ranging from the exact method to heuristic ones are available to predict the barrier heights, developing an efficient heuristic for this purpose is still an algorithmic challenge. We developed a novel RNA folding pathway prediction method, ACOfoldpath, based on Ant Colony Optimization (ACO). ACO is a widely used powerful combinatorial optimization algorithm inspired from the food-seeking behavior of ants. In ACOfoldpath, to accelerate the folding pathway prediction, we reduce the search space by utilizing originally devised structure generation rules. To evaluate the performance of the proposed method, we benchmarked ACOfoldpath on the known nineteen conformational RNA switches. As a result, ACOfoldpath successfully predicted folding pathways better than or comparable to the previous heuristics. The results of RNA folding dynamics simulations and pseudoknotted pathway predictions are also presented.

Evolving methods for rational de novo design of functional RNA molecules

Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.

Engineering CRISPR guide RNA riboswitches for in vivo applications

CRISPR-based genome editing provides a simple and scalable toolbox for a variety of therapeutic and biotechnology applications. Whilst the fundamental properties of CRISPR proved easily transferable from the native prokaryotic hosts to eukaryotic and multicellular organisms, the tight control of the CRISPR-editing activity remains a major challenge. Here we summarise recent developments of CRISPR and riboswitch technologies and recommend novel functionalised synthetic-gRNA (sgRNA) designs to achieve inducible and spatiotemporal regulation of CRISPR-based genetic editors in response to cellular or extracellular stimuli. We believe that future advances of these tools will have major implications for both basic and applied research, spanning from fundamental genetic studies and synthetic biology to genetic editing and gene therapy.

Computational approaches for alternative and transient secondary structures of ribonucleic acids

Transient and alternative structures of ribonucleic acids (RNAs) play essential roles in various regulatory processes, such as translation regulation in living cells. Because experimental analyses for RNA structures are difficult and time-consuming, computational approaches based on RNA secondary structures are promising. In this article, we review computational methods for detecting and analyzing transient/alternative secondary structures of RNAs, including static approaches based on probabilistic distributions of RNA secondary structures and dynamic approaches such as kinetic folding and folding pathway predictions.

In silico design of ligand triggered RNA switches

This contribution sketches a work flow to design an RNA switch that is able to adapt two structural conformations in a ligand-dependent way. A well characterized RNA aptamer, i.e., knowing its K_d and adaptive structural features, is an essential ingredient of the described design process. We exemplify the principles using the well-known theophylline aptamer throughout this work. The aptamer in its ligand-binding competent structure represents one structural conformation of the switch while an alternative fold that disrupts the binding-competent structure forms the other conformation. To keep it simple we do not incorporate any regulatory mechanism to control transcription or translation. We elucidate a commonly used design process by explicitly dissecting and explaining the necessary steps in detail. We developed a novel objective function which specifies the mechanistics of this simple, ligand-triggered riboswitch and describe an extensive in silico analysis pipeline to evaluate important kinetic properties of the designed sequences. This protocol and the developed software can be easily extended or adapted to fit novel design scenarios and thus can serve as a template for future needs.