Nucleic acid sequence design via efficient ensemble defect optimization

DNA Crosslinked Mucin Hydrogels Allow for On-Demand Gel Disintegration and Triggered Particle Release

Whereas hydrogels created from synthetic polymers offer a high level of control over their stability and mechanical properties, their biomedical activity is typically limited. In contrast, biopolymers have evolved over billions of years to integrate a broad range of functionalities into a single design. Thus, biopolymeric hydrogels can show remarkable capabilities such as regulatory behavior, selective barrier properties, or antimicrobial effects. Still, despite their widespread use in numerous biomedical applications, achieving a meticulous control over the physical properties of macroscopic biopolymeric networks remains a challenge. Here, a macroscopic, DNA-crosslinked mucin hydrogel with tunable viscoelastic properties that responds to two types of triggers: temperature alterations and DNA displacement strands, is presented. As confirmed with bulk rheology and single particle tracking, the hybridized base pairs governing the stability of the hydrogel can be opened, thus allowing for a precise control over the hydrogel stiffness and even enabling a full gel-to-sol transition. As those DNA-crosslinked mucin hydrogels possess tunable mechanical properties and can be disintegrated on demand, they can not only be considered for controlled cargo release but may also serve as a role model for the development of smart biomedical materials in applications such as tissue engineering and wound healing.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

Modified Nucleotides and RNA Structure Prediction

Nucleotide modifications are occurrent in all types of RNA and play an important role in RNA structure formation and stability. Modified bases not only possess the ability to shift the RNA structure ensemble towards desired functional confirmations. By changes in the base pairing partner preference, they may even enlarge or reduce the conformational space, i.e., the number and types of structures the RNA molecule can adopt. However, most methods to predict RNA secondary structure do not provide the means to include the effect of modifications on the result. With the help of a heavily modified transfer RNA (tRNA) molecule, this chapter demonstrates how to include the effect of different base modifications into secondary structure prediction using the ViennaRNA Package. The constructive approach demonstrated here allows for the calculation of minimum free energy structure and suboptimal structures at different levels of modified base support. In particular we, show how to incorporate the isomerization of uridine to pseudouridine ( Ψ ) and the reduction of uridine to dihydrouridine (D).

RNA Secondary Structure Thermodynamics

Several different ways to predict RNA secondary structures have been suggested in the literature. Statistical methods, such as those that utilize stochastic context-free grammars (SCFGs), or approaches based on machine learning aim to predict the best representative structure for the underlying ensemble of possible conformations. Their parameters have therefore been trained on larger subsets of well-curated, known secondary structures. Physics-based methods, on the other hand, usually refrain from using optimized parameters. They model secondary structures from loops as individual building blocks which have been assigned a physical property instead: the free energy of the respective loop. Such free energies are either derived from experiments or from mathematical modeling. This rigorous use of physical properties then allows for the application of statistical mechanics to describe the entire state space of RNA secondary structures in terms of equilibrium probabilities. On that basis, and by using efficient algorithms, many more descriptors of the conformational state space of RNA molecules can be derived to investigate and explain the many functions of RNA molecules. Moreover, compared to other methods, physics-based models allow for a much easier extension with other properties that can be measured experimentally. For instance, small molecules or proteins can bind to an RNA and their binding affinity can be assessed experimentally. Under certain conditions, existing RNA secondary structure prediction tools can be used to model this RNA-ligand binding and to eventually shed light on its impact on structure formation and function.

Multiplexed CRISPR-Based Nucleic Acid Detection Using a Single Cas Protein

Thanks to its ease, speed, and sensitivity, CRISPR-based nucleic acid detection has been increasingly explored for molecular diagnostics. However, one of its major limitations is lack of multiplexing capability because the detection relies on the trans-cleavage activity of the Cas protein, which necessitates the use of multiple orthogonal Cas proteins for multiplex detection. Here we report the development of a multiplexed CRISPR-based nucleic acid detection system with single-nucleotide resolution using a single Cas protein (Cas12a). This method, termed as CRISPR-TMSD, integrates the toehold-mediated strand displacement (TMSD) reaction, and the cis-cleavage activity of the Cas protein and multiplexed detection are achieved using a single Cas protein owing to the use of target-specific reporters. A set of computational simulation toolkits was used to design the TMSD reporter, allowing for highly sensitive and specific identification of target sequences. In combination with the recombinase polymerase amplification (RPA), the detection limit can reach as low as 1 copy/μL. As proof of concept, CRISPR-TMSD was subsequently used to detect an oncogenic gene and SARS-CoV-2 RNA with a single-nucleotide resolution. This work represents a conceptually new strategy for designing a CRISPR-based diagnostic system and has great potential to expand the application of CRISPR-based diagnostics.

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

We show that in silico design of DNA secondary structures is improved by extending the base pairing alphabet beyond A-T and G-C to include the pair between 2-amino-8-(1'-β-d-2'-deoxyribofuranosyl)-imidazo-[1,2-a]-1,3,5-triazin-(8H)-4-one and 6-amino-3-(1'-β-d-2'-deoxyribofuranosyl)-5-nitro-(1H)-pyridin-2-one, abbreviated as P and Z. To obtain the thermodynamic parameters needed to include P-Z pairs in the designs, we performed 47 optical melting experiments and combined the results with previous work to fit free energy and enthalpy nearest neighbor folding parameters for P-Z pairs and G-Z wobble pairs. We find G-Z pairs have stability comparable to that of A-T pairs and should therefore be included as base pairs in structure prediction and design algorithms. Additionally, we extrapolated the set of loop, terminal mismatch, and dangling end parameters to include the P and Z nucleotides. These parameters were incorporated into the RNAstructure software package for secondary structure prediction and analysis. Using the RNAstructure Design program, we solved 99 of the 100 design problems posed by Eterna using the ACGT alphabet or supplementing it with P-Z pairs. Extending the alphabet reduced the propensity of sequences to fold into off-target structures, as evaluated by the normalized ensemble defect (NED). The NED values were improved relative to those from the Eterna example solutions in 91 of 99 cases in which Eterna-player solutions were provided. P-Z-containing designs had average NED values of 0.040, significantly below the 0.074 of standard-DNA-only designs, and inclusion of the P-Z pairs decreased the time needed to converge on a design. This work provides a sample pipeline for inclusion of any expanded alphabet nucleotides into prediction and design workflows.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

Molecular system for an exponentially fast growing programmable synthetic polymer

In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement "internal" parallel insertion that does not rely on adding successive layers to "external" edges for growth. Approaches like this can produce enhanced exponential growth behavior that is less limited by volume and external surface interference, for an early step toward efficiently building two and three dimensional shapes in logarithmic time. We experimentally demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism and results in the exponential growth of a population of polymers per unit time. In the supplementary material, we note that an "extension" beyond conventional Turing machine theory is needed to theoretically analyze exponential growth itself in programmable physical systems. Sequential physical Turing Machines that run a roughly constant number of Turing steps per unit time cannot achieve an exponential growth of structure per time. In contrast, the "active" self-assembly model in this paper, computationally equivalent to a Push-Down Automaton, is exponentially fast when implemented in molecules, but is taxonomically less powerful than a Turing machine. In this sense, a physical Push-Down Automaton can be more powerful than a sequential physical Turing Machine, even though the Turing Machine can compute any computable function. A need for an "extended" computational/physical theory arises, described in the supplementary material section S1.

RNA design via structure-aware multifrontier ensemble optimization

MOTIVATION

RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases.

RESULTS

We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs.

AVAILABILITY AND IMPLEMENTATION

Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

We show that in silico design of DNA secondary structures is improved by extending the base pairing alphabet beyond A-T and G-C to include the pair between 2-amino-8-(1'-β-D-2'-deoxyribofuranosyl)-imidazo-[1,2- a ]-1,3,5-triazin-(8 H )-4-one and 6-amino-3-(1'-β-D-2'-deoxyribofuranosyl)-5-nitro-(1 H )-pyridin-2-one, simply P and Z. To obtain the thermodynamic parameters needed to include P-Z pairs in the designs, we performed 47 optical melting experiments and combined the results with previous work to fit a new set of free energy and enthalpy nearest neighbor folding parameters for P-Z pairs and G-Z wobble pairs. We find that G-Z pairs have stability comparable to A-T pairs and therefore should be considered quantitatively by structure prediction and design algorithms. Additionally, we extrapolated the set of loop, terminal mismatch, and dangling end parameters to include P and Z nucleotides. These parameters were incorporated into the RNAstructure software package for secondary structure prediction and analysis. Using the RNAstructure Design program, we solved 99 of the 100 design problems posed by Eterna using the ACGT alphabet or supplementing with P-Z pairs. Extending the alphabet reduced the propensity of sequences to fold into off-target structures, as evaluated by the normalized ensemble defect (NED). The NED values were improved relative to those from the Eterna example solutions in 91 of 99 cases where Eterna-player solutions were provided. P-Z-containing designs had average NED values of 0.040, significantly below the 0.074 of standard-DNA-only designs, and inclusion of the P-Z pairs decreased the time needed to converge on a design. This work provides a sample pipeline for inclusion of any expanded alphabet nucleotides into prediction and design workflows.

Computer-Aided Design and Production of RNA Origami as Protein Scaffolds and Biosensors

RNA nanotechnology is able to take advantage of the modularity of RNA to build a wide variety of structures and functional devices from a common set of structural modules. The RNA origami architecture harnesses the property of RNA to fold as it is being enzymatically synthesized by the RNA polymerase and enables the design of single-stranded devices that integrate multiple structural and functional RNA motifs. Here, we provide detailed procedures on how to design and characterize RNA origami structures. The process is illustrated by two examples: one that forms lattices and another example that acts as biosensors.

LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.

Algorithmic Design of 3D Wireframe RNA Polyhedra

We address the problem of de novo design and synthesis of nucleic acid nanostructures, a challenge that has been considered in the area of DNA nanotechnology since the 1980s and more recently in the area of RNA nanotechnology. Toward this goal, we introduce a general algorithmic design process and software pipeline for rendering 3D wireframe polyhedral nanostructures in single-stranded RNA. To initiate the pipeline, the user creates a model of the desired polyhedron using standard 3D graphic design software. As its output, the pipeline produces an RNA nucleotide sequence whose corresponding RNA primary structure can be transcribed from a DNA template and folded in the laboratory. As case examples, we design and characterize experimentally three 3D RNA nanostructures: a tetrahedron, a triangular bipyramid, and a triangular prism. The design software is openly available and also provides an export of the targeted 3D structure into the oxDNA molecular dynamics simulator for easy simulation and visualization.

aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights

BACKGROUND

We study in this work the inverse folding problem for RNA, which is the discovery of sequences that fold into given target secondary structures.

RESULTS

We implement a Lévy mutation scheme in an updated version of aRNAque an evolutionary inverse folding algorithm and apply it to the design of RNAs with and without pseudoknots. We find that the Lévy mutation scheme increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. Compared to antaRNA, aRNAque CPU time is higher but more successful in finding designed sequences that fold correctly into the target structures.

CONCLUSION

We propose that a Lévy flight offers a better standard mutation scheme for optimizing RNA design. Our new version of aRNAque is available on GitHub as a python script and the benchmark results show improved performance on both Pseudobase++ and the Eterna100 datasets, compared to existing inverse folding tools.

Aptamer-antibody hybrid ELONA that uses hybridization chain reaction to detect a urinary biomarker EN2 for bladder and prostate cancer

We report an EN2-specific (K_d = 8.26 nM) aptamer, and a sensitive and specific enzyme-linked oligonucleotide assay (ELONA) for rapid and sensitive colorimetric detection of bladder and prostate cancer biomarker EN2 in urine. The assay relies on an aptamer-mediated hybridization chain reaction (HCR) to generate DNA nanostructures that bind to EN2 and simultaneously amplify signals. The assay can be performed within 2.5 h, and has a limit of detection of 0.34 nM in buffer and 2.69 nM in artificial urine. Moreover, this assay showed high specificity as it did not detect other urinary proteins, including biomarkers of other cancers. The proposed ELONA is inexpensive, highly reproducible, and has great chemical stability, so it may enable development of a simple, sensitive and accurate diagnostic tool to detect bladder and prostate cancers early.

A rational design of a cascaded DNA circuit for nanoparticle assembly and its application in the discrimination of single-base changes

In the field of dynamic DNA nanotechnology, a designable DNA assembly circuit based on the toehold-mediated strand displacement reaction has demonstrated its ability to program the self-assembly of nanoparticles. However, the laborious work for the modification of nanoparticles with oligonucleotides, the long assembly time, and the circuit leakage prevent its further and scalable applications. To this end, cascaded circuits composed of two recycling circles are rationally designed in this study. Through the pre-initiation of the autonomous reaction, nanoparticles as sensing elements and no additionally exposed bases on the substrate hybridized with fuel strand, the real assembly time and signal leakage for diagnostic application can be effectively reduced and eliminated, thus offering a promising methodology for future point-of-care testing.

Nearest neighbor rules for RNA helix folding thermodynamics: improved end effects

Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated 5′GGUC3′ paired to 3′CUGG5′ separately from other 5′GU3′/3′UG5′ stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37°C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.

Cellular Computational Logic Using Toehold Switches

The development of computational logic that carries programmable and predictable features is one of the key requirements for next-generation synthetic biological devices. Despite considerable progress, the construction of synthetic biological arithmetic logic units presents numerous challenges. In this paper, utilizing the unique advantages of RNA molecules in building complex logic circuits in the cellular environment, we demonstrate the RNA-only bitwise logical operation of XOR gates and basic arithmetic operations, including a half adder, a half subtractor, and a Feynman gate, in Escherichia coli. Specifically, de-novo-designed riboregulators, known as toehold switches, were concatenated to enhance the functionality of an OR gate, and a previously utilized antisense RNA strategy was further optimized to construct orthogonal NIMPLY gates. These optimized synthetic logic gates were able to be seamlessly integrated to achieve final arithmetic operations on small molecule inputs in cells. Toehold-switch-based ribocomputing devices may provide a fundamental basis for synthetic RNA-based arithmetic logic units or higher-order systems in cells.

Toward Mail-in-Sensors for SARS-CoV-2 Detection: Interfacing Gel Switch Resonators with Cell-Free Toehold Switches

The COVID-19 pandemic has emphasized the importance of widespread testing to control the spread of infectious diseases. The rapid development, scale-up, and deployment of viral and antibody detection methods since the beginning of the pandemic have greatly increased testing capacity. Desirable attributes of detection methods are low product costs, self-administered protocols, and the ability to be mailed in sealed envelopes for the safe analysis and subsequent logging to public health databases. Herein, such a platform is demonstrated with a screen-printed, inductor-capacitor (LC) resonator as a transducer and a toehold switch coupled with cell-free expression as the biological selective recognition element. In the presence of the N-gene from SARS-CoV-2, the toehold switch relaxes, protease enzyme is expressed, and it degrades a gelatin switch that ultimately shifts the resonant frequency of the planar resonant sensor. The gelatin switch resonator (GSR) can be analyzed through a sealed envelope allowing for assessment without the need for careful sample handling with personal protective equipment or the need for workup with other reagents. The toehold switch used in this sensor demonstrated selectivity to SARS-CoV-2 virus over three seasonal coronaviruses and SARS-CoV-1, with a limit of detection of 100 copies/μL. The functionality of the platform and assessment in a sealed envelope with an automated scanner is shown with overnight shipment, and further improvements are discussed to increase signal stability and further simplify user protocols toward a mail-in platform.

Computational Design of RNA Toehold-Mediated Translation Activators

Translation activators are an important class of riboregulators that respond to nucleic acid signals by activating gene expression. Toehold switches and single-nucleotide-specific programmable riboregulators (SNIPRs) are two types of translation activators that can detect nearly any nucleic acid sequence using interactions initiated by single-stranded domains known as toeholds. Toehold switches operate with high dynamic range, orthogonality, and programmability, making them capable of detecting a variety of pathogens in paper-based cell-free diagnostic assays. SNIPRs are designed to enable the accurate detection of single-nucleotide mutations, making them valuable tools for mutation and drug-resistance assays. Here we describe the computational design process for generating toehold switches and SNIPRs active against different pathogens and mutations of interest. Such riboregulators can be deployed in paper-based diagnostic assays to enable rapid and low-cost disease detection.

RNA Structure Prediction, Analysis, and Design: An Introduction to Web-Based Tools

Understanding RNA structure has become critical in the study of RNA in their roles as mediators of biological processes. To aid in these studies, computational algorithms that utilize thermodynamics have been developed to predict RNA secondary structure. Due to the importance of intermolecular interactions, the algorithms have been expanded to determine and predict RNA-RNA hybridization. This chapter discusses popular webservers with the tools for RNA secondary structure prediction, RNA-RNA hybridization, and design. We address key features that distinguish common-functioning programs and their purposes for the interests of the user. Ultimately, we hope this review elucidates web-based tools researchers may take advantage of in their investigations of RNA structure and function.

Advanced Design of Structural RNAs Using RNARedPrint

RNA design addresses the need to build novel RNAs, e.g., for biotechnological applications in synthetic biology, equipped with desired functional properties. This chapter describes how to use the software RNARedPrint for the de novo rational design of RNA sequences adopting one or several desired secondary structures. Depending on the application, these structures could represent alternate configurations or kinetic pathways. The software makes such design convenient and sufficiently fast for practical routine, where it even overcomes notorious problems in the application of RNA design, e.g., it maintains realistic GC content.

High-Performance Allosteric Conditional Guide RNAs for Mammalian Cell-Selective Regulation of CRISPR/Cas

The activity of a conditional guide RNA (cgRNA) is dependent on the presence or absence of an RNA trigger, enabling cell-selective regulation of CRISPR/Cas function. cgRNAs are programmable at two levels, with the target-binding sequence controlling the target of Cas activity (edit, silence, or induce a gene of choice) and the trigger-binding sequence controlling the scope of Cas activity (subset of cells expressing the trigger RNA). Allosteric cgRNA mechanisms enable independent design of the target and trigger sequences, providing the flexibility to select the regulatory target and scope independently. Building on prior advances in dynamic RNA nanotechnology that demonstrated the cgRNA concept, here we set the goal of engineering high-performance allosteric cgRNA mechanisms for the mammalian setting, pursuing both ON → OFF logic (conditional inactivation by an RNA trigger) and OFF → ON logic (conditional activation by an RNA trigger). For each mechanism, libraries of orthogonal cgRNA/trigger pairs were designed using NUPACK. In HEK 293T cells expressing cgRNAs, triggers, and inducing dCas9: (1) a library of four ON → OFF "terminator switch" cgRNAs exhibit a median fold-change of ≈50×, a median fractional dynamic range of ≈20%, and a median crosstalk modulus of ≈9%; (2) a library of three OFF → ON "split-terminator switch" cgRNAs exhibit a median fold-change of ≈150×, a median fractional dynamic range of ≈50%, and a median crosstalk modulus of ≈4%. Further, we demonstrate that xrRNA elements that protect viral RNAs from degradation by exoribonucleases can dramatically enhance the performance of RNA synthetic biology. The high-performance allosteric cgRNAs demonstrated here for ON → OFF and OFF → ON logic in mammalian cells provide a foundation for pursuing applications of programmable cell-selective regulation.

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/).

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.

LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

Motivation

RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime.

Results

Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart).

Availability and implementation

Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition.

Supplementary information

Supplementary data are available at Bioinformatics online.

Specificity of RNA Folding and Its Association with Evolutionarily Adaptive mRNA Secondary Structures

The secondary structure is a fundamental feature of both noncoding and messenger RNAs. However, our understanding of the secondary structure of mRNA, especially that of the coding regions, remains elusive, likely due to translation and the lack of RNA-binding proteins that sustain the consensus structure, such as those that bind to noncoding RNA. Indeed, mRNA has recently been found to adopt diverse alternative structures, the overall functional significance of which remains untested. We hereby approached this problem by estimating the folding specificity, i.e., the probability that a fragment of RNA folds back to the same partner once refolded. We showed that the folding specificity of mRNA is lower than that of noncoding RNA and exhibits moderate evolutionary conservation. Notably, we found that specific rather than alternative folding is likely evolutionarily adaptive since specific folding is frequently associated with functionally important genes or sites within a gene. Additional analysis in combination with ribosome density suggests the ability to modulate ribosome movement as one potential functional advantage provided by specific folding. Our findings revealed a novel facet of the RNA structurome with important functional and evolutionary implications and indicated a potential method for distinguishing the mRNA secondary structures maintained by natural selection from molecular noise.

Detection of Coronaviruses Using RNA Toehold Switch Sensors

A rapid, sensitive and simple point-of-care (POC) nucleic acid diagnostic test is needed to prevent spread of infectious diseases. Paper-based toehold reaction, a recently emerged colorimetric POC nucleic acid diagnostic test, has been widely used for pathogen detection and microbiome profiling. Here, we introduce an amplification method called reverse transcription loop-mediated amplification (RT-LAMP) prior to the toehold reaction and modify it to enable more sensitive and faster colorimetric detection of RNA viruses. We show that incorporating the modified RT-LAMP to the toehold reaction detects as few as 120 copies of coronavirus RNA in 70 min. Cross-reactivity test against other coronaviruses indicates this toehold reaction with the modified RT-LAMP is highly specific to the target RNA. Overall, the paper-based toehold switch sensors with the modified RT-LAMP allow fast, sensitive, specific and colorimetric coronavirus detection.

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.

A Unified Dynamic Programming Framework for the Analysis of Interacting Nucleic Acid Strands: Enhanced Models, Scalability, and Speed

Dynamic programming algorithms within the NUPACK software suite enable analysis of nucleic acid sequences over complex and test tube ensembles containing arbitrary numbers of interacting strand species, serving the needs of researchers in molecular programming, nucleic acid nanotechnology, synthetic biology, and across the life sciences. Here, to enhance the underlying physical model, ensure scalability for large calculations, and achieve dramatic speedups when calculating diverse physical quantities over complex and test tube ensembles, we introduce a unified dynamic programming framework that combines three ingredients: (1) recursions that specify the dependencies between subproblems and incorporate the details of the structural ensemble and the free energy model, (2) evaluation algebras that define the mathematical form of each subproblem, (3) operation orders that specify the computational trajectory through the dependency graph of subproblems. The physical model is enhanced using new recursions that operate over the complex ensemble including coaxial and dangle stacking subensembles. The recursions are coded generically and then compiled with a quantity-specific evaluation algebra and operation order to generate an executable for each physical quantity: partition function, equilibrium base-pairing probabilities, MFE energy and proxy structure, suboptimal proxy structures, and Boltzmann sampled structures. For large complexes (e.g., 30 000 nt), scalability is achieved for partition function calculations using an overflow-safe evaluation algebra, and for equilibrium base-pairing probabilities using a backtrack-free operation order. A new blockwise operation order that treats subcomplex blocks for the complex species in a test tube ensemble enables dramatic speedups (e.g., 20-120× ) using vectorization and caching. With these performance enhancements, equilibrium analysis of substantial test tube ensembles can be performed in ≤ 1 min on a single computational core (e.g., partition function and equilibrium concentration for all complex species of up to six strands formed from two strand species of 300 nt each, or for all complex species of up to two strands formed from 80 strand species of 100 nt each). A new sampling algorithm simultaneously samples multiple structures from the complex ensemble to yield speedups of an order of magnitude or more as the number of structures increases above ≈10³. These advances are available within the NUPACK 4.0 code base (www.nupack.org) which can be flexibly scripted using the all-new NUPACK Python module.

incaRNAfbinv 2.0: a webserver and software with motif control for fragment-based design of RNAs

SUMMARY

RNA design has conceptually evolved from the inverse RNA folding problem. In the classical inverse RNA problem, the user inputs an RNA secondary structure and receives an output RNA sequence that folds into it. Although modern RNA design methods are based on the same principle, a finer control over the resulting sequences is sought. As an important example, a substantial number of non-coding RNA families show high preservation in specific regions, while being more flexible in others and this information should be utilized in the design. By using the additional information, RNA design tools can help solve problems of practical interest in the growing fields of synthetic biology and nanotechnology. incaRNAfbinv 2.0 utilizes a fragment-based approach, enabling a control of specific RNA secondary structure motifs. The new version allows significantly more control over the general RNA shape, and also allows to express specific restrictions over each motif separately, in addition to other advanced features.

AVAILABILITY AND IMPLEMENTATION

incaRNAfbinv 2.0 is available through a standalone package and a web-server at https://www.cs.bgu.ac.il/incaRNAfbinv. Source code, command-line and GUI wrappers can be found at https://github.com/matandro/RNAsfbinv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sequence-to-function deep learning frameworks for engineered riboregulators

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04-0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

RNAdetect: efficient computational detection of novel non-coding RNAs

MOTIVATION

Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance.

RESULTS

In this paper, we propose RNAdetect, a computational method incorporating novel features for accurate detection of ncRNAs in combination with comparative genome analysis. Given a sequence alignment, RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the conformation of the individual folding structures to a common consensus structure. Furthermore, n-gram models (NGMs) are used to extract features that can effectively capture sequence homology to known ncRNA families. Utilization of NGMs can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrate that RNAdetect can accurately and reliably detect novel ncRNAs, outperforming the current state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The source code for RNAdetect and the benchmark data used in this paper can be downloaded at https://github.com/bjyoontamu/RNAdetect.

Improved Nucleic Acid Therapy with Advanced Nanoscale Biotechnology

Due to a series of systemic and intracellular obstacles in nucleic acid (NA) therapy, including fast degradation in blood, renal clearance, poor cellular uptake, and inefficient endosomal escape, NAs may need delivery methods to transport to the cell nucleus or cytosol to be effective. Advanced nanoscale biotechnology-associated strategies, such as controlling the particle size, charge, drug loading, response to environmental signals, or other physical/chemical properties of delivery carriers, have provided great help for the in vivo and in vitro delivery of NA therapeutics. In this review, we introduce the characteristics of different NA modalities and illustrate how advanced nanoscale biotechnology assists NA therapy. The specific features and challenges of various nanocarriers in clinical and preclinical studies are summarized and discussed. With the help of advanced nanoscale biotechnology, some of the major barriers to the development of NA therapy will eventually be overcome in the near future.

An Artificial Intelligent Signal Amplification System for in vivo Detection of miRNA

MicroRNAs (miRNA) have been identified as oncogenic drivers and tumor suppressors in every major cancer type. In this work, we design an artificial intelligent signal amplification (AISA) system including double-stranded SQ (S, signal strand; Q, quencher strand) and FP (F, fuel strand; P, protect strand) according to thermodynamics principle for sensitive detection of miRNA in vitro and in vivo. In this AISA system for miRNA detection, strand S carries a quenched imaging marker inside the SQ. Target miRNA is constantly replaced by a reaction intermediate and circulatively participates in the reaction, similar to enzyme. Therefore, abundant fluorescent substances from S and SP are dissociated from excessive SQ for in vitro and in vivo visualization. The versatility and feasibility for disease diagnosis using this system were demonstrated by constructing two types of AISA system to detect Hsa-miR-484 and Hsa-miR-100, respectively. The minimum target concentration detected by the system in vitro (10 min after mixing) was 1/10th that of the control group. The precancerous lesions of liver cancer were diagnosed, and the detection accuracy were larger than 94% both in terms of location and concentration. The ability to establish this design framework for AISA system with high specificity provides a new way to monitor tumor progression and to assess therapeutic responses.

Aptamers as Modular Components of Therapeutic Nucleic Acid Nanotechnology

Nucleic acids play a central role in all domains of life, either as genetic blueprints or as regulators of various biochemical pathways. The chemical makeup of ribonucleic acid (RNA) or deoxyribonucleic acid (DNA), generally represented by a sequence of four monomers, also provides precise instructions for folding and higher-order assembly of these biopolymers that, in turn, dictate biological functions. The sequence-based specific 3D structures of nucleic acids led to the development of the directed evolution of oligonucleotides, SELEX (systematic evolution of ligands by exponential enrichment), against a chosen target molecule. Among the variety of functions, selected oligonucleotides named aptamers also allow targeting of cell-specific receptors with antibody-like precision and can deliver functional RNAs without a transfection agent. The advancements in the field of customizable nucleic acid nanoparticles (NANPs) opened avenues for the design of nanoassemblies utilizing aptamers for triggering or blocking cell signaling pathways or using aptamer-receptor combinations to activate therapeutic functionalities. A recent selection of fluorescent aptamers enables real-time tracking of NANP formation and interactions. The aptamers are anticipated to contribute to the future development of technologies, enabling an efficient assembly of functional NANPs in mammalian cells or in vivo. These research topics are of top importance for the field of therapeutic nucleic acid nanotechnology with the promises to scale up mass production of NANPs suitable for biomedical applications, to control the intracellular organization of biological materials to enhance the efficiency of biochemical pathways, and to enhance the therapeutic potential of NANP-based therapeutics while minimizing undesired side effects and toxicities.

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.

Conditional Guide RNAs: Programmable Conditional Regulation of CRISPR/Cas Function in Bacterial and Mammalian Cells via Dynamic RNA Nanotechnology

A guide RNA (gRNA) directs the function of a CRISPR protein effector to a target gene of choice, providing a versatile programmable platform for engineering diverse modes of synthetic regulation (edit, silence, induce, bind). However, the fact that gRNAs are constitutively active places limitations on the ability to confine gRNA activity to a desired location and time. To achieve programmable control over the scope of gRNA activity, here we apply principles from dynamic RNA nanotechnology to engineer conditional guide RNAs (cgRNAs) whose activity is dependent on the presence or absence of an RNA trigger. These cgRNAs are programmable at two levels, with the trigger-binding sequence controlling the scope of the effector activity and the target-binding sequence determining the subject of the effector activity. We demonstrate molecular mechanisms for both constitutively active cgRNAs that are conditionally inactivated by an RNA trigger (ON → OFF logic) and constitutively inactive cgRNAs that are conditionally activated by an RNA trigger (OFF → ON logic). For each mechanism, automated sequence design is performed using the reaction pathway designer within NUPACK to design an orthogonal library of three cgRNAs that respond to different RNA triggers. In E. coli expressing cgRNAs, triggers, and silencing dCas9 as the protein effector, we observe a median conditional response of ≈4-fold for an ON → OFF "terminator switch" mechanism, ≈15-fold for an ON → OFF "splinted switch" mechanism, and ≈3-fold for an OFF → ON "toehold switch" mechanism; the median crosstalk within each cgRNA/trigger library is <2%, ≈2%, and ≈20% for the three mechanisms. To test the portability of cgRNA mechanisms prototyped in bacteria to mammalian cells, as well as to test generalizability to different effector functions, we implemented the terminator switch in HEK 293T cells expressing inducing dCas9 as the protein effector, observing a median ON → OFF conditional response of ≈4-fold with median crosstalk of ≈30% for three orthogonal cgRNA/trigger pairs. By providing programmable control over both the scope and target of protein effector function, cgRNA regulators offer a promising platform for synthetic biology.

A Nucleus-Targeting DNA Aptamer for Dead Cell Indication

Dead cells always accompany with live cells in vivo and in cell culture. It is important to distinguish dead cells from live cells in various biological studies. Currently, the probes for dead cells are mainly nucleic acid-intercalators, most of which have low affinity and potential toxicity to live cells. In this work, we report a novel aptameric probe (Ch4-1) for the first time, which binds cell nuclei with high affinity (apparent K_d = 6.65 ± 3.40 nM). Ch4-1 was generated by Cell-SELEX process, it was identified to target nucleoproteins in cell nuclei. As an oligonucleotide, Ch4-1 cannot penetrate the integrated cell membrane; therefore, it only binds to dead cells rather than live cells. Compared with traditional DNA-targeting nuclear dyes, Ch4-1 possesses a high affinity to the nucleus, no toxicity to live cells, and can be easily labeled with different fluorescent dyes. It was demonstrated to serve as a probe for distinguishing dead cells from live cells in apoptosis assay, as well as for the nuclear staining of tissue sections.

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.

Computational approaches for the discovery of splicing regulatory RNA structures

Global RNA structure and local functional motifs mediate interactions important in determining the rates and patterns of mRNA splicing. In this review, we overview approaches for the computational prediction of RNA secondary structure with a special emphasis on the discovery of motifs important to RNA splicing. The process of identifying and modeling potential splicing regulatory structures is illustrated using a recently-developed approach for RNA structural motif discovery, the ScanFold pipeline, which is applied to the identification of a known splicing regulatory structure in influenza virus.

Advances in engineered trans-acting regulatory RNAs and their application in bacterial genome engineering

Small noncoding RNAs, a large class of ancient posttranscriptional regulators, are increasingly recognized and utilized as key modulators of gene expression in a broad range of microorganisms. Owing to their small molecular size and the central role of Watson-Crick base pairing in defining their interactions, structure and function, numerous diverse types of trans-acting RNA regulators that are functional at the DNA, mRNA and protein levels have been experimentally characterized. It has become increasingly clear that most small RNAs play critical regulatory roles in many processes and are, therefore, considered to be powerful tools for genetic engineering and synthetic biology. The trans-acting regulatory RNAs accelerate this ability to establish potential framework for genetic engineering and genome-scale engineering, which allows RNA structure characterization, easier to design and model compared to DNA or protein-based systems. In this review, we summarize recent advances in engineered trans-acting regulatory RNAs that are used in bacterial genome-scale engineering and in novel cellular capabilities as well as their implementation in wide range of biotechnological, biological and medical applications.

Principles and Applications of Nucleic Acid Strand Displacement Reactions

Dynamic DNA nanotechnology, a subfield of DNA nanotechnology, is concerned with the study and application of nucleic acid strand-displacement reactions. Strand-displacement reactions generally proceed by three-way or four-way branch migration and initially were investigated for their relevance to genetic recombination. Through the use of toeholds, which are single-stranded segments of DNA to which an invader strand can bind to initiate branch migration, the rate with which strand displacement reactions proceed can be varied by more than 6 orders of magnitude. In addition, the use of toeholds enables the construction of enzyme-free DNA reaction networks exhibiting complex dynamical behavior. A demonstration of this was provided in the year 2000, in which strand displacement reactions were employed to drive a DNA-based nanomachine (Yurke, B.; et al. Nature 2000, 406, 605-608). Since then, toehold-mediated strand displacement reactions have been used with ever increasing sophistication and the field of dynamic DNA nanotechnology has grown exponentially. Besides molecular machines, the field has produced enzyme-free catalytic systems, all DNA chemical oscillators and the most complex molecular computers yet devised. Enzyme-free catalytic systems can function as chemical amplifiers and as such have received considerable attention for sensing and detection applications in chemistry and medical diagnostics. Strand-displacement reactions have been combined with other enzymatically driven processes and have also been employed within living cells (Groves, B.; et al. Nat. Nanotechnol. 2015, 11, 287-294). Strand-displacement principles have also been applied in synthetic biology to enable artificial gene regulation and computation in bacteria. Given the enormous progress of dynamic DNA nanotechnology over the past years, the field now seems poised for practical application.

Signal Transduction in Human Cell Lysate via Dynamic RNA Nanotechnology

Dynamic RNA nanotechnology with small conditional RNAs (scRNAs) offers a promising conceptual approach to introducing synthetic regulatory links into endogenous biological circuits. Here, we use human cell lysate containing functional Dicer and RNases as a testbed for engineering scRNAs for conditional RNA interference (RNAi). scRNAs perform signal transduction via conditional shape change: detection of a subsequence of mRNA input X triggers formation of a Dicer substrate that is processed to yield small interfering RNA (siRNA) output anti-Y targeting independent mRNA Y for destruction. Automated sequence design is performed using the reaction pathway designer within NUPACK to encode this conditional hybridization cascade into the scRNA sequence subject to the sequence constraints imposed by X and Y. Because it is difficult for secondary structure models to predict which subsequences of mRNA input X will be accessible for detection, here we develop the RNAhyb method to experimentally determine accessible windows within the mRNA that are provided to the designer as sequence constraints. We demonstrate the programmability of scRNA regulators by engineering scRNAs for transducing in both directions between two full-length mRNAs X and Y, corresponding to either the forward molecular logic “if X then not Y” (X  Y) or the reverse molecular logic “if Y then not X” (Y  X). In human cell lysate, we observe a strong OFF/ON conditional response with low crosstalk, corresponding to a ≈20-fold increase in production of the siRNA output in response to the cognate versus noncognate full-length mRNA input. 2′OMe-RNA chemical modifications protect signal transduction reactants and intermediates against RNase degradation while enabling Dicer processing of signal transduction products. Because diverse biological pathways interact with RNA, scRNAs that transduce between detection of endogenous RNA inputs and production of biologically active RNA outputs hold great promise as a synthetic regulatory paradigm.

Accelerated RNA secondary structure design using preselected sequences for helices and loops

Nucleic acids can be designed to be nano-machines, pharmaceuticals, or probes. RNA secondary structures can form the basis of self-assembling nanostructures. There are only four natural RNA bases, therefore it can be difficult to design sequences that fold to a single, specified structure because many other structures are often possible for a given sequence. One approach taken by state-of-the-art sequence design methods is to select sequences that fold to the specified structure using stochastic, iterative refinement. The goal of this work is to accelerate design. Many existing iterative methods select and refine sequences one base pair and one unpaired nucleotide at a time. Here, the hypothesis that sequences can be preselected in order to accelerate design was tested. To this aim, a database was built of helix sequences that demonstrate thermodynamic features found in natural sequences and that also have little tendency to cross-hybridize. Additionally, a database was assembled of RNA loop sequences with low helix-formation propensity and little tendency to cross-hybridize with either the helices or other loops. These databases of preselected sequences accelerate the selection of sequences that fold with minimal ensemble defect by replacing some of the trial and error of current refinement approaches. When using the database of preselected sequences as compared to randomly chosen sequences, sequences for natural structures are designed 36 times faster, and random structures are designed six times faster. The sequences selected with the aid of the database have similar ensemble defect as those sequences selected at random. The sequence database is part of RNAstructure package at http://rna.urmc.rochester.edu/RNAstructure.html.

Low-cost detection of norovirus using paper-based cell-free systems and synbody-based viral enrichment

Noroviruses are a primary cause of gastroenteritis and foodborne illness with cases that affect millions of people worldwide each year. Inexpensive tests for norovirus that do not require sophisticated laboratory equipment are important tools for ensuring that patients receive timely treatment and for containing outbreaks. Herein, we demonstrate a low-cost colorimetric assay that detects norovirus from clinical samples by combining paper-based cell-free transcription-translation systems, isothermal amplification and virus enrichment by synbodies. Using isothermal amplification and cell-free RNA sensing with toehold switches, we demonstrate that the assay enables detection of norovirus GII.4 Sydney from stool down to concentrations of 270 aM in reactions that can be directly read by eye. Furthermore, norovirus-binding synbodies and magnetic beads are used to concentrate the virus and provide a 1000-fold increase in assay sensitivity extending its detection limit to 270 zM. These results demonstrate the utility of paper-based cell-free diagnostic systems for identification of foodborne pathogens and provide a versatile diagnostic assay that can be applied to the concentration, amplification and detection of a broad range of infectious agents.

RNAblueprint: flexible multiple target nucleic acid sequence design

Motivation

Realizing the value of synthetic biology in biotechnology and medicine requires the design of molecules with specialized functions. Due to its close structure to function relationship, and the availability of good structure prediction methods and energy models, RNA is perfectly suited to be synthetically engineered with predefined properties. However, currently available RNA design tools cannot be easily adapted to accommodate new design specifications. Furthermore, complicated sampling and optimization methods are often developed to suit a specific RNA design goal, adding to their inflexibility.

Results

We developed a C ++  library implementing a graph coloring approach to stochastically sample sequences compatible with structural and sequence constraints from the typically very large solution space. The approach allows to specify and explore the solution space in a well defined way. Our library also guarantees uniform sampling, which makes optimization runs performant by not only avoiding re-evaluation of already found solutions, but also by raising the probability of finding better solutions for long optimization runs. We show that our software can be combined with any other software package to allow diverse RNA design applications. Scripting interfaces allow the easy adaption of existing code to accommodate new scenarios, making the whole design process very flexible. We implemented example design approaches written in Python to demonstrate these advantages.

Availability and implementation

RNAblueprint, Python implementations and benchmark datasets are available at github: https://github.com/ViennaRNA.

Supplementary information

Supplementary data are available at Bioinformatics online.