Complete RNA inverse folding: computational design of functional hammerhead ribozymes

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models

MOTIVATION

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation.

RESULTS

In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/ml4bio/RiboDiffusion.

Deep generative design of RNA family sequences

RNA engineering has immense potential to drive innovation in biotechnology and medicine. Despite its importance, a versatile platform for the automated design of functional RNA is still lacking. Here, we propose RNA family sequence generator (RfamGen), a deep generative model that designs RNA family sequences in a data-efficient manner by explicitly incorporating alignment and consensus secondary structure information. RfamGen can generate novel and functional RNA family sequences by sampling points from a semantically rich and continuous representation. We have experimentally demonstrated the versatility of RfamGen using diverse RNA families. Furthermore, we confirmed the high success rate of RfamGen in designing functional ribozymes through a quantitative massively parallel assay. Notably, RfamGen successfully generates artificial sequences with higher activity than natural sequences. Overall, RfamGen significantly improves our ability to design functional RNA and opens up new potential for generative RNA engineering in synthetic biology.

Computational design and experimental verification of pseudoknotted ribozymes

The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.

MoiRNAiFold: a novel tool for complex in silico RNA design

Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Inverse RNA Folding Workflow to Design and Test Ribozymes that Include Pseudoknots

Ribozymes are RNAs that catalyze reactions. They occur in nature, and can also be evolved in vitro to catalyze novel reactions. This chapter provides detailed protocols for using inverse folding software to design a ribozyme sequence that will fold to a known ribozyme secondary structure and for testing the catalytic activity of the sequence experimentally. This protocol is able to design sequences that include pseudoknots, which is important as all naturally occurring full-length ribozymes have pseudoknots. The starting point is the known pseudoknot-containing secondary structure of the ribozyme and knowledge of any nucleotides whose identity is required for function. The output of the protocol is a set of sequences that have been tested for function. Using this protocol, we were previously successful at designing highly active double-pseudoknotted HDV ribozymes.

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

Computer-Aided Design of Active Pseudoknotted Hammerhead Ribozymes

Pseudoknots are important motifs for stabilizing the structure of functional RNAs. As an example, pseudoknotted hammerhead ribozymes are highly active compared to minimal ribozymes. The design of new RNA sequences that retain the function of a model RNA structure includes taking in account pseudoknots presence in the structure, which is usually a challenge for bioinformatics tools. Our method includes using "Enzymer," a software for designing RNA sequences with desired secondary structures that may include pseudoknots. Enzymer implements an efficient stochastic search and optimization algorithm to sample RNA sequences from low ensemble defect mutational landscape of an initial design template to generate an RNA sequence that is predicted to fold into the desired target structure.

Cellular Concentrations of Nucleotide Diphosphate-Chelated Magnesium Ions Accelerate Catalysis by RNA and DNA Enzymes

RNAs are involved in myriad cellular events. In general, RNA function is affected by cellular conditions. For instance, molecular crowding promotes RNA folding through compaction of the RNA. Metabolites generally destabilize RNA secondary structure, which improves RNA folding cooperativity and increases the fraction of functional RNA. Our recent studies demonstrate that cellular concentrations of amino acid-chelated magnesium (aaCM) stimulate RNA folding and catalysis while protecting RNAs from magnesium ion-induced degradation. However, effects of other cellular magnesium ion chelators on RNA function have not been tested. Herein, we report that nucleotide diphosphate-chelated magnesium, which is of intermediate strength, promotes RNA catalysis much like aaCM. Nucleotides are some of the major metabolites in cells and have one to three phosphates, which have increasingly tight binding of magnesium. On the basis of binding calculations, ∼85% ATP, ∼40% ADP, and only 5% AMP are estimated to possess a magnesium ion under cellular conditions of 0.50 mM Mg²⁺_free. We tested the self-cleaving activity of the hammerhead ribozyme in the presence of these chelated magnesium species. Our results indicate that NTP-chelated magnesium and NMP-chelated magnesium do not appreciably stimulate RNA catalysis, whereas NDP-chelated magnesium promotes RNA catalysis up to 6.5-fold. Inspired by NDP, we observed similar stimulatory effects for several other Mg²⁺ diphosphate-containing metabolites, including UDP-GlcNAC and UDP-Glc; in addition, we found similar effects for a DNAzyme. Thus, rate stimulatory effects are general with respect to the diphosphate and nucleic acid enzyme. These results implicate magnesium-chelated diphosphate metabolites as general facilitators of RNA function inside cells.

Design of highly active double-pseudoknotted ribozymes: a combined computational and experimental study

Design of RNA sequences that adopt functional folds establishes principles of RNA folding and applications in biotechnology. Inverse folding for RNAs, which allows computational design of sequences that adopt specific structures, can be utilized for unveiling RNA functions and developing genetic tools in synthetic biology. Although many algorithms for inverse RNA folding have been developed, the pseudoknot, which plays a key role in folding of ribozymes and riboswitches, is not addressed in most algorithms. For the few algorithms that attempt to predict pseudoknot-containing ribozymes, self-cleavage activity has not been tested. Herein, we design double-pseudoknot HDV ribozymes using an inverse RNA folding algorithm and test their kinetic mechanisms experimentally. More than 90% of the positively designed ribozymes possess self-cleaving activity, whereas more than 70% of negative control ribozymes, which are predicted to fold to the necessary structure but with low fidelity, do not possess it. Kinetic and mutation analyses reveal that these RNAs cleave site-specifically and with the same mechanism as the WT ribozyme. Most ribozymes react just 50- to 80-fold slower than the WT ribozyme, and this rate can be improved to near WT by modification of a junction. Thus, fast-cleaving functional ribozymes with multiple pseudoknots can be designed computationally.

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.

Solving the RNA design problem with reinforcement learning

We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure, design a sequence that folds to that structure in silico. Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna, allowing it to solve some very difficult structures. On the other hand, it has failed to learn other strategies, possibly because they were not required for the targets in the training set. This suggests the possibility that future improvements to the training protocol may yield further gains in performance.

Design of RNAs: comparing programs for inverse RNA folding

Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.

RNA inverse folding using Monte Carlo tree search

Background

Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases.

Result

We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding.

Conclusion

MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1882-7) contains supplementary material, which is available to authorized users.

RNA folding kinetics using Monte Carlo and Gillespie algorithms

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny ([Formula: see text]20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm. Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to [Formula: see text] times that of the Gillespie algorithm, where [Formula: see text] denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by [Formula: see text]; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated. Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence-see http://bioinformatics.bc.edu/clote/RNAexpNumNbors .

RNAiFold2T: Constraint Programming design of thermo-IRES switches

MOTIVATION

RNA thermometers (RNATs) are cis-regulatory elements that change secondary structure upon temperature shift. Often involved in the regulation of heat shock, cold shock and virulence genes, RNATs constitute an interesting potential resource in synthetic biology, where engineered RNATs could prove to be useful tools in biosensors and conditional gene regulation.

RESULTS

Solving the 2-temperature inverse folding problem is critical for RNAT engineering. Here we introduce RNAiFold2T, the first Constraint Programming (CP) and Large Neighborhood Search (LNS) algorithms to solve this problem. Benchmarking tests of RNAiFold2T against existent programs (adaptive walk and genetic algorithm) inverse folding show that our software generates two orders of magnitude more solutions, thus allowing ample exploration of the space of solutions. Subsequently, solutions can be prioritized by computing various measures, including probability of target structure in the ensemble, melting temperature, etc. Using this strategy, we rationally designed two thermosensor internal ribosome entry site (thermo-IRES) elements, whose normalized cap-independent translation efficiency is approximately 50% greater at 42 °C than 30 °C, when tested in reticulocyte lysates. Translation efficiency is lower than that of the wild-type IRES element, which on the other hand is fully resistant to temperature shift-up. This appears to be the first purely computational design of functional RNA thermoswitches, and certainly the first purely computational design of functional thermo-IRES elements.

AVAILABILITY

RNAiFold2T is publicly available as part of the new release RNAiFold3.0 at https://github.com/clotelab/RNAiFold and http://bioinformatics.bc.edu/clotelab/RNAiFold, which latter has a web server as well. The software is written in C ++ and uses OR-Tools CP search engine.

CONTACT

clote@bc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The in vitro selection world

Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provides the ability to isolate molecules of desired properties and function from large pools (libraries) of random molecules with as many as 10(16) distinct species. This review, in recognition of a quarter of century of scientific discoveries made through in vitro selection, starts with a brief overview of the method and its history. It further covers recent developments in in vitro selection with a focus on tools that enhance the capabilities of in vitro selection and its expansion from being purely a nucleic acids selection to that of polypeptides and proteins. In addition, we cover how next generation sequencing and modern biological computational tools are being used to complement in vitro selection experiments. On the very least, sequencing and computational tools can translate the large volume of information associated with in vitro selection experiments to manageable, analyzable, and exploitable information. Finally, in vivo selection is briefly compared and contrasted to in vitro selection to highlight the unique capabilities of each method.

Modeling Three-Dimensional Structural Motifs of Viral IRES

RNA virus genomes are reservoirs of a wide diversity of RNA structural elements. In particular, specific regions of the viral genome have evolved to adopt specialized three-dimensional (3D) structures, which can act in concert with host factors and/or viral proteins to recruit the translation machinery on viral RNA using a mechanism that is independent on the 5' end. This strategy relies on cis-acting RNA sequences designated as internal ribosome entry site (IRES) elements. IRES elements that are found in the genome of different groups of RNA viruses perform the same function despite differing in primary sequence and secondary RNA structure and host factor requirement to recruit the translation machinery internally. Evolutionarily conserved motifs tend to preserve sequences in each group of RNA viruses impacting on RNA structure and RNA-protein interactions important for IRES function. However, due to the lack of sequence homology among genetically distant IRES elements, accurate modeling of 3D IRES structure is currently a challenging task. In addition, as a universal RNA motif unique to IRES elements has not been found, a better understanding of viral IRES structural motifs could greatly assist in the detection of IRES-like motifs hidden in genome sequences. The focus of this review is to describe recent advances in modeling viral IRES tertiary structural motifs and also novel approaches to detect sequences potentially folding as IRES-like motifs.

RNA Thermodynamic Structural Entropy

Conformational entropy for atomic-level, three dimensional biomolecules is known experimentally to play an important role in protein-ligand discrimination, yet reliable computation of entropy remains a difficult problem. Here we describe the first two accurate and efficient algorithms to compute the conformational entropy for RNA secondary structures, with respect to the Turner energy model, where free energy parameters are determined from UV absorption experiments. An algorithm to compute the derivational entropy for RNA secondary structures had previously been introduced, using stochastic context free grammars (SCFGs). However, the numerical value of derivational entropy depends heavily on the chosen context free grammar and on the training set used to estimate rule probabilities. Using data from the Rfam database, we determine that both of our thermodynamic methods, which agree in numerical value, are substantially faster than the SCFG method. Thermodynamic structural entropy is much smaller than derivational entropy, and the correlation between length-normalized thermodynamic entropy and derivational entropy is moderately weak to poor. In applications, we plot the structural entropy as a function of temperature for known thermoswitches, such as the repression of heat shock gene expression (ROSE) element, we determine that the correlation between hammerhead ribozyme cleavage activity and total free energy is improved by including an additional free energy term arising from conformational entropy, and we plot the structural entropy of windows of the HIV-1 genome. Our software RNAentropy can compute structural entropy for any user-specified temperature, and supports both the Turner’99 and Turner’04 energy parameters. It follows that RNAentropy is state-of-the-art software to compute RNA secondary structure conformational entropy. Source code is available at https://github.com/clotelab/RNAentropy/; a full web server is available at http://bioinformatics.bc.edu/clotelab/RNAentropy, including source code and ancillary programs.

Searching the Sequence Space for Potent Aptamers Using SELEX in Silico

To isolate functional nucleic acids that bind to defined targets with high affinity and specificity, which are known as aptamers, the systematic evolution of ligands by exponential enrichment (SELEX) methodology has emerged as the preferred approach. Here, we propose a computational approach, SELEX in silico, that allows the sequence space to be more thoroughly explored regarding binding of a certain target. Our approach consists of two steps: (i) secondary structure-based sequence screening, which aims to collect the sequences that can form a desired RNA motif as an enhanced initial library, followed by (ii) sequence enrichment regarding target binding by molecular dynamics simulation-based virtual screening. Our SELEX in silico method provided a practical computational solution to three key problems in aptamer sequence searching: design of nucleic acid libraries, knowledge of sequence enrichment, and identification of potent aptamers. Six potent theophylline-binding aptamers, which were isolated by SELEX in silico from a sequence space containing 4(13) sequences, were experimentally verified to bind theophylline with high affinity: Kd ranging from 0.16 to 0.52 μM, compared with the dissociation constant of the original aptamer-theophylline, 0.32 μM. These results demonstrate the significant potential of SELEX in silico as a new method for aptamer discovery and optimization.

Putting RNA to work: Translating RNA fundamentals into biotechnological engineering practice

Synthetic biology, in close concert with systems biology, is revolutionizing the field of metabolic engineering by providing novel tools and technologies to rationally, in a standardized way, reroute metabolism with a view to optimally converting renewable resources into a broad range of bio-products, bio-materials and bio-energy. Increasingly, these novel synthetic biology tools are exploiting the extensive programmable nature of RNA, vis-à-vis DNA- and protein-based devices, to rationally design standardized, composable, and orthogonal parts, which can be scaled and tuned promptly and at will. This review gives an extensive overview of the recently developed parts and tools for i) modulating gene expression ii) building genetic circuits iii) detecting molecules, iv) reporting cellular processes and v) building RNA nanostructures. These parts and tools are becoming necessary armamentarium for contemporary metabolic engineering. Furthermore, the design criteria, technological challenges, and recent metabolic engineering success stories of the use of RNA devices are highlighted. Finally, the future trends in transforming metabolism through RNA engineering are critically evaluated and summarized.

Multi-objective optimization for RNA design with multiple target secondary structures

Background

RNAs are attractive molecules as the biological parts for synthetic biology. In particular, the ability of conformational changes, which can be encoded in designer RNAs, enables us to create multistable molecular switches that function in biological circuits. Although various algorithms for designing such RNA switches have been proposed, the previous algorithms optimize the RNA sequences against the weighted sum of objective functions, where empirical weights among objective functions are used. In addition, an RNA design algorithm for multiple pseudoknot targets is currently not available.

Results

We developed a novel computational tool for automatically designing RNA sequences which fold into multiple target secondary structures. Our algorithm designs RNA sequences based on multi-objective genetic algorithm, by which we can explore the RNA sequences having good objective function values without empirical weight parameters among the objective functions. Our algorithm has great flexibility by virtue of this weight-free nature. We benchmarked our multi-target RNA design algorithm with the datasets of two, three, and four target structures and found that our algorithm shows better or comparable design performances compared with the previous algorithms, RNAdesign and Frnakenstein. In addition to the benchmarks with pseudoknot-free datasets, we benchmarked MODENA with two-target pseudoknot datasets and found that MODENA can design the RNAs which have the target pseudoknotted secondary structures whose free energies are close to the lowest free energy. Moreover, we applied our algorithm to a ribozyme-based ON-switch which takes a ribozyme-inactive secondary structure when the theophylline aptamer structure is assumed.

Conclusions

Currently, MODENA is the only RNA design software which can be applied to multiple pseudoknot targets. Successful design results for the multiple targets and an RNA device indicate usefulness of our multi-objective RNA design algorithm.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0706-x) contains supplementary material, which is available to authorized users.

RNAiFold 2.0: a web server and software to design custom and Rfam-based RNA molecules

Several algorithms for RNA inverse folding have been used to design synthetic riboswitches, ribozymes and thermoswitches, whose activity has been experimentally validated. The RNAiFold software is unique among approaches for inverse folding in that (exhaustive) constraint programming is used instead of heuristic methods. For that reason, RNAiFold can generate all sequences that fold into the target structure or determine that there is no solution. RNAiFold 2.0 is a complete overhaul of RNAiFold 1.0, rewritten from the now defunct COMET language to C++. The new code properly extends the capabilities of its predecessor by providing a user-friendly pipeline to design synthetic constructs having the functionality of given Rfam families. In addition, the new software supports amino acid constraints, even for proteins translated in different reading frames from overlapping coding sequences; moreover, structure compatibility/incompatibility constraints have been expanded. With these features, RNAiFold 2.0 allows the user to design single RNA molecules as well as hybridization complexes of two RNA molecules. Availability: the web server, source code and linux binaries are publicly accessible at http://bioinformatics.bc.edu/clotelab/RNAiFold2.0.

Fast, approximate kinetics of RNA folding

In this article, we introduce the software suite Hermes, which provides fast, novel algorithms for RNA secondary structure kinetics. Using the fast Fourier transform to efficiently compute the Boltzmann probability that a secondary structure S of a given RNA sequence has base pair distance x (resp. y) from reference structure A (resp. B), Hermes computes the exact kinetics of folding from A to B in this coarse-grained model. In particular, Hermes computes the mean first passage time from the transition probability matrix by using matrix inversion, and also computes the equilibrium time from the rate matrix by using spectral decomposition. Due to the model granularity and the speed of Hermes, it is capable of determining secondary structure refolding kinetics for large RNA sequences, beyond the range of other methods. Comparative benchmarking of Hermes with other methods indicates that Hermes provides refolding kinetics of accuracy suitable for use in the computational design of RNA, an important area of synthetic biology. Source code and documentation for Hermes are available.