Paradigms for computational nucleic acid design

DNA nanostructure decoration: a how-to tutorial

DNA Nanotechnology is being applied to multiple research fields. The functionality of DNA nanostructures is significantly enhanced by decorating them with nanoscale moieties including: proteins, metallic nanoparticles, quantum dots, and chromophores. Decoration is a complex process and developing protocols for reliable attachment routinely requires extensive trial and error. Additionally, the granular nature of scientific communication makes it difficult to discern general principles in DNA nanostructure decoration. This tutorial is a guidebook designed to minimize experimental bottlenecks and avoid dead-ends for those wishing to decorate DNA nanostructures. We supplement the reference material on available technical tools and procedures with a conceptual framework required to make efficient and effective decisions in the lab. Together these resources should aid both the novice and the expert to develop and execute a rapid, reliable decoration protocols.

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.

Self-assembly of DNA parallel double-crossover motifs

DNA double-crossover motifs, including parallel and antiparallel crossovers, serve as the structural foundation for the creation of diverse nanostructures and dynamic devices in DNA nanotechnology. Parallel crossover motifs have unique advantages over the widely used antiparallel crossover design but have not developed as substantially due to the difficulties in assembly. Here we created 29 designs of parallel double-crossover motifs varying in hybridization pathways, central domain lengths, and crossover locations to investigate their assembly mechanism. Arrays were successfully formed in four distinct designs, and large tubular structures were obtained in seven designs with predefined pathways and central domains appoximately 16 nucleotides in length. The nanotubes obtained from parallel crossover design showed improved nuclease resistance than the ones from the antiparallel counterpart design. Overall, our study provides a basis for the development of generalized assembly rules of DNA parallel crossover systems and opens new opportunities for their potential use in biological systems.

Modified RNAs and predictions with the ViennaRNA Package

MOTIVATION

In living organisms, many RNA molecules are modified post-transcriptionally. This turns the widely known four-letter RNA alphabet ACGU into a much larger one with currently more than 300 known distinct modified bases. The roles for the majority of modified bases remain uncertain, but many are already well-known for their ability to influence the preferred structures that an RNA may adopt. In fact, tRNAs sometimes require certain modifications to fold into their cloverleaf shaped structure. However, predicting the structure of RNAs with base modifications is still difficult due to the lack of efficient algorithms that can deal with the extended sequence alphabet, as well as missing parameter sets that account for the changes in stability induced by the modified bases.

RESULTS

We present an approach to include sparse energy parameter data for modified bases into the ViennaRNA Package. Our method does not require any changes to the underlying efficient algorithms but instead uses a set of plug-in constraints that adapt the predictions in terms of loop evaluation at runtime. These adaptations are efficient in the sense that they are only performed for loops where additional parameters are actually available for. In addition, our approach also facilitates the inclusion of more modified bases as soon as further parameters become available.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation are available at https://www.tbi.univie.ac.at/RNA.

Generation of DNA oligomers with similar chemical kinetics via in-silico optimization

Networks of interacting DNA oligomers are useful for applications such as biomarker detection, targeted drug delivery, information storage, and photonic information processing. However, differences in the chemical kinetics of hybridization reactions, referred to as kinetic dispersion, can be problematic for some applications. Here, it is found that limiting unnecessary stretches of Watson-Crick base pairing, referred to as unnecessary duplexes, can yield exceptionally low kinetic dispersions. Hybridization kinetics can be affected by unnecessary intra-oligomer duplexes containing only 2 base-pairs, and such duplexes explain up to 94% of previously reported kinetic dispersion. As a general design rule, it is recommended that unnecessary intra-oligomer duplexes larger than 2 base-pairs and unnecessary inter-oligomer duplexes larger than 7 base-pairs be avoided. Unnecessary duplexes typically scale exponentially with network size, and nearly all networks contain unnecessary duplexes substantial enough to affect hybridization kinetics. A new method for generating networks which utilizes in-silico optimization to mitigate unnecessary duplexes is proposed and demonstrated to reduce in-vitro kinetic dispersions as much as 96%. The limitations of the new design rule and generation method are evaluated in-silico by creating new oligomers for several designs, including three previously programmed reactions and one previously engineered structure.

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.

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.

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.

Current Developments of N-Heterocyclic Carbene Au(I)/Au(III) Complexes toward Cancer Treatment

Since their first discovery, N-heterocyclic carbenes have had a significant impact on organometallic chemistry. Due to their nature as strong σ-donor and π-acceptor ligands, they are exceptionally well suited to stabilize Au(I) and Au(III) complexes in biological environments. Over the last decade, the development of rationally designed NHCAu(I/III) complexes to specifically target DNA has led to a new “gold rush” in bioinorganic chemistry. This review aims to summarize the latest advances of NHCAu(I/III) complexes that are able to interact with DNA. Furthermore, the latest advancements on acyclic diamino carbene gold complexes with anticancer activity are presented as these typically overlooked NHC alternatives offer great additional design possibilities in the toolbox of carbene-stabilized gold complexes for targeted therapy.

DNA nanotechnology-empowered finite state machines

A finite state machine (FSM, or automaton) is an abstract machine that can switch among a finite number of states in response to temporally ordered inputs, which allows storage and processing of information in an order-sensitive manner. In recent decades, DNA molecules have been actively exploited to develop information storage and nanoengineering materials, which hold great promise for smart nanodevices and nanorobotics under the framework of FSM. In this review, we summarize recent progress in utilizing DNA self-assembly and DNA nanostructures to implement FSMs. We describe basic principles for representative DNA FSM prototypes and highlight their advantages and potential in diverse applications. The challenges in this field and future directions have also been discussed.

Riboswitch-inspired toehold riboregulators for gene regulation in Escherichia coli

Regulatory RNA molecules have been widely investigated as components for synthetic gene circuits, complementing the use of protein-based transcription factors. Among the potential advantages of RNA-based gene regulators are their comparatively simple design, sequence-programmability, orthogonality, and their relatively low metabolic burden. In this work, we developed a set of riboswitch-inspired riboregulators in Escherichia coli that combine the concept of toehold-mediated strand displacement (TMSD) with the switching principles of naturally occurring transcriptional and translational riboswitches. Specifically, for translational activation and repression, we sequestered anti-anti-RBS or anti-RBS sequences, respectively, inside the loop of a stable hairpin domain, which is equipped with a single-stranded toehold region at its 5' end and is followed by regulated sequences on its 3' side. A trigger RNA binding to the toehold region can invade the hairpin, inducing a structural rearrangement that results in translational activation or deactivation. We also demonstrate that TMSD can be applied in the context of transcriptional regulation by switching RNA secondary structure involved in Rho-dependent termination. Our designs expand the repertoire of available synthetic riboregulators by a set of RNA switches with no sequence limitation, which should prove useful for the development of robust genetic sensors and circuits.

Assembly of RNA Nanostructures from Double-Crossover Tiles

Artificial self-assembling RNA scaffolds can be produced from many types of RNA motifs that are rationally designed. These scaffolds are of interest as nanoscale organizers, with applications in drug delivery and synthetic cells. Here we describe design strategies, production methods, and imaging of micrometer-sized RNA nanotubes and lattices that assemble from RNA tiles comprising multiple distinct strands.

Lateral flow colorimetric biosensor for detection of Vibrio parahaemolyticus based on hybridization chain reaction and aptamer

Vibrio parahaemolyticus (V. parahaemolyticus) is the causative agent for acute hepatopancreatic necrosis disease (AHPND) of shrimp, and it is also a common seafood-borne pathogen for humans. Rapid and accurate identification of V. parahaemolyticus is helpful to diagnose the AHPND and ensure food safety. Common detection methods suffer the deficiency of time-consuming and complexed operation. Based on the increased development of aptamer and our previous study, a new detection assay of V. parahaemolyticus was introduced, in which the aptamer combined with magnetic nanoparticles (MNPs) was the recognizer, hybridization chain reaction (HCR) was the signal amplifier, and lateral flow nucleotide biosensor (LFNB) was the signal exporter. The assay possessed high specificity of distinguishing the target with other bacteria, and the calculated limit of detection was 2.6 × 10³ cells. Furthermore, the whole process just needs 67 min, free of thermocycle instruments and signal readout instruments, which means it is suitable for low-resource laboratories or districts.

E2EDNA: Simulation Protocol for DNA Aptamers with Ligands

We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments. As a rule, they do not offer flexibility with respect to the choice of the theoretical engine or direct access to the simulation platform. Practical aptamer optimization often requires higher accuracy predictions for only a small subset of sequences suggested, e.g., by SELEX experiments, but in the absence of a streamlined procedure, this task is extremely time and expertise intensive. We address this gap by introducing E2EDNA, a computational framework that accepts a DNA sequence in the FASTA format and the structures of the desired ligands and performs approximate folding followed by a refining step, analyte complexation, and molecular dynamics sampling at the desired level of accuracy. As a case study, we simulate a DNA-UTP (uridine triphosphate) complex in water using the state-of-the-art AMOEBA polarizable force field. The code is available at https://github.com/InfluenceFunctional/E2EDNA.

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.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

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.

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.

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.

A minimalist's approach for DNA nanoconstructions

Structural DNA nanotechnology takes DNA, a biopolymer, far beyond being the molecule that stores and transmits genetic information in biological systems. DNA has been employed as building blocks for the assembly of designed, nanoscaled, supramolecular DNA architectures for applications in biophysics, structure determination, synthetic biology, diagnostics, and drug delivery. Herein, we review a symmetric approach of tile-based DNA self-assembly. This approach allows the construction of DNA nanostructures from minimal numbers of different types of DNA strands based on sequence and structural symmetries. Some examples of the applications of this approach in siRNA delivery are discussed as well.

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.

Revealing the Presence of a Symbolic Sequence Representing Multiple Nucleotides Based on K-Means Clustering of Oligonucleotides

In biological systems, a few sequence differences diversify the hybridization profile of nucleotides and enable the quantitative control of cellular metabolism in a cooperative manner. In this respect, the information required for a better understanding may not be in each nucleotide sequence, but representative information contained among them. Existing methodologies for nucleotide sequence design have been optimized to track the function of the genetic molecule and predict interaction with others. However, there has been no attempt to extract new sequence information to represent their inheritance function. Here, we tried to conceptually reveal the presence of a representative sequence from groups of nucleotides. The combined application of the K-means clustering algorithm and the social network analysis theorem enabled the effective calculation of the representative sequence. First, a “common sequence” is made that has the highest hybridization property to analog sequences. Next, the sequence complementary to the common sequence is designated as a ‘representative sequence’. Based on this, we obtained a representative sequence from multiple analog sequences that are 8–10-bases long. Their hybridization was empirically tested, which confirmed that the common sequence had the highest hybridization tendency, and the representative sequence better alignment with the analogs compared to a mere complementary.

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

A pipeline for computational design of novel RNA-like topologies

Designing novel RNA topologies is a challenge, with important therapeutic and industrial applications. We describe a computational pipeline for design of novel RNA topologies based on our coarse-grained RNA-As-Graphs (RAG) framework. RAG represents RNA structures as tree graphs and describes RNA secondary (2D) structure topologies (currently up to 13 vertices, ≈260 nucleotides). We have previously identified novel graph topologies that are RNA-like among these. Here we describe a systematic design pipeline and illustrate design for six broad design problems using recently developed tools for graph-partitioning and fragment assembly (F-RAG). Following partitioning of the target graph, corresponding atomic fragments from our RAG-3D database are combined using F-RAG, and the candidate atomic models are scored using a knowledge-based potential developed for 3D structure prediction. The sequences of the top scoring models are screened further using available tools for 2D structure prediction. The results indicate that our modular approach based on RNA-like topologies rather than specific 2D structures allows for greater flexibility in the design process, and generates a large number of candidate sequences quickly. Experimental structure probing using SHAPE-MaP for two sequences agree with our predictions and suggest that our combined tools yield excellent candidates for further sequence and experimental screening.

Chimeric XNA: An Unconventional Design for Orthogonal Informational Systems

The paradigm of homogenous-sugar-backbone of RNA and DNA has reliably guided the construction of many functional and useful xeno nucleic acid (XNA) systems to date. Deviations from this monotonous and canonical design, in many cases, results in oligonucleotide systems that lack base pairing with themselves, or with RNA or DNA. Here we show that nucleotides of two such compromised XNA systems can be combined with RNA and DNA in specific patterns to produce chimeric-backbone oligonucleotides, which in certain cases demonstrate base pairing properties comparable to-or stronger than-canonical systems, while also altering the conventional Watson-Crick pairing behavior. The unorthodox pairing properties generated from these chimeric sugar-backbone oligonucleotides suggest a counterintuitive approach of creating modules consisting of non-base pairing XNAs with RNA/DNA in a set pattern. This strategy has the potential to increase the diversity of unconventional nucleic acids leading to orthogonal backbone-sequence-controlled informational systems.

Individually addressable and dynamic DNA gates for multiplexed cell sorting

The ability to analyze and isolate cells based on the expression of specific surface markers has increased our understanding of cell biology and produced numerous applications for biomedicine. However, established cell-sorting platforms rely on labels that are limited in number due to biophysical constraints, such as overlapping emission spectra of fluorophores in FACS. Here, we establish a framework built on a system of orthogonal and extensible DNA gates for multiplexed cell sorting. These DNA gates label target cell populations by antibodies to allow magnetic bead isolation en masse and then selectively unlock by strand displacement to sort cells. We show that DNA gated sorting (DGS) is triggered to completion within minutes on the surface of cells and achieves target cell purity, viability, and yield equivalent to that of commercial magnetic sorting kits. We demonstrate multiplexed sorting of three distinct immune cell populations (CD8⁺, CD4⁺, and CD19⁺) from mouse splenocytes to high purity and show that recovered CD8⁺ T cells retain proliferative potential and target cell-killing activity. To broaden the utility of this platform, we implement a double positive sorting scheme using DNA gates on peptide-MHC tetramers to isolate antigen-specific CD8⁺ T cells from mice infected with lymphocytic choriomeningitis virus (LCMV). DGS can potentially be expanded with fewer biophysical constraints to large families of DNA gates for applications that require analysis of complex cell populations, such as host immune responses to disease.

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.

Hybridization Chain Reaction Design and Biosensor Implementation

DNA biosensors could overcome some of the common drawbacks of lab-based techniques for nucleic acids detection for diagnostics purposes. One of the main impediments for such applications of DNA biosensors is their lack of sensitivity: this can prevent their full exploitation in the diagnostic analytical field. DNA nanotechnology could enhance DNA biosensors and let them perform at the required high sensitivity. Well-designed, programmable self-assembly reactions can be triggered by a specific nucleic acid target. The Hybridization Chain Reaction (HCR) is a self-assembly strategy in which the target nucleic acid sequence triggers the formation of long nicked double-stranded DNA nanostructures. This can be performed in solution or on a surface, and the process can be coupled to different signal transduction schemes. We here describe the methods to design and test HCR reactions for the detection of different nucleic acid targets in solution and the procedures to exploit this strategy on surfaces with an electrochemical biosensing platform.

Enzyme-free nucleic acid dynamical systems

Chemistries exhibiting complex dynamics—from inorganic oscillators to gene regulatory networks—have been long known but either cannot be reprogrammed at will or rely on the sophisticated enzyme chemistry underlying the central dogma. Can simpler molecular mechanisms, designed from scratch, exhibit the same range of behaviors? Abstract chemical reaction networks have been proposed as a programming language for complex dynamics, along with their systematic implementation using short synthetic DNA molecules. We developed this technology for dynamical systems by identifying critical design principles and codifying them into a compiler automating the design process. Using this approach, we built an oscillator containing only DNA components, establishing that Watson-Crick base-pairing interactions alone suffice for complex chemical dynamics and that autonomous molecular systems can be designed via molecular programming languages.

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling

In the past few years, the study of therapeutic RNA nanotechnology has expanded tremendously to encompass a large group of interdisciplinary sciences. It is now evident that rationally designed programmable RNA nanostructures offer unique advantages in addressing contemporary therapeutic challenges such as distinguishing target cell types and ameliorating disease. However, to maximize the therapeutic benefit of these nanostructures, it is essential to understand the immunostimulatory aptitude of such tools and identify potential complications. This paper presents a set of 16 nanoparticle platforms that are highly configurable. These novel nucleic acid based polygonal platforms are programmed for controllable self-assembly from RNA and/or DNA strands via canonical Watson-Crick interactions. It is demonstrated that the immunostimulatory properties of these particular designs can be tuned to elicit the desired immune response or lack thereof. To advance the current understanding of the nanoparticle properties that contribute to the observed immunomodulatory activity and establish corresponding designing principles, quantitative structure-activity relationship modeling is conducted. The results demonstrate that molecular weight, together with melting temperature and half-life, strongly predicts the observed immunomodulatory activity. This framework provides the fundamental guidelines necessary for the development of a new library of nanoparticles with predictable immunomodulatory activity.

Self-assembly of multi-stranded RNA motifs into lattices and tubular structures

Rational design of nucleic acid molecules yields self-assembling scaffolds with increasing complexity, size and functionality. It is an open question whether design methods tailored to build DNA nanostructures can be adapted to build RNA nanostructures with comparable features. Here we demonstrate the formation of RNA lattices and tubular assemblies from double crossover (DX) tiles, a canonical motif in DNA nanotechnology. Tubular structures can exceed 1 μm in length, suggesting that this DX motif can produce very robust lattices. Some of these tubes spontaneously form with left-handed chirality. We obtain assemblies by using two methods: a protocol where gel-extracted RNA strands are slowly annealed, and a one-pot transcription and anneal procedure. We identify the tile nick position as a structural requirement for lattice formation. Our results demonstrate that stable RNA structures can be obtained with design tools imported from DNA nanotechnology. These large assemblies could be potentially integrated with a variety of functional RNA motifs for drug or nanoparticle delivery, or for colocalization of cellular components.

Biophysics of Artificially Expanded Genetic Information Systems. Thermodynamics of DNA Duplexes Containing Matches and Mismatches Involving 2-Amino-3-nitropyridin-6-one (Z) and Imidazo[1,2-a]-1,3,5-triazin-4(8H)one (P)

Synthetic nucleobases presenting non-Watson-Crick arrangements of hydrogen bond donor and acceptor groups can form additional nucleotide pairs that stabilize duplex DNA independent of the standard A:T and G:C pairs. The pair between 2-amino-3-nitropyridin-6-one 2'-deoxyriboside (presenting a {donor-donor-acceptor} hydrogen bonding pattern on the Watson-Crick face of the small component, trivially designated Z) and imidazo[1,2-a]-1,3,5-triazin-4(8H)one 2'-deoxyriboside (presenting an {acceptor-acceptor-donor} hydrogen bonding pattern on the large component, trivially designated P) is one of these extra pairs for which a substantial amount of molecular biology has been developed. Here, we report the results of UV absorbance melting measurements and determine the energetics of binding of DNA strands containing Z and P to give short duplexes containing Z:P pairs as well as various mismatches comprising Z and P. All measurements were done at 1 M NaCl in buffer (10 mM Na cacodylate, 0.5 mM EDTA, pH 7.0). Thermodynamic parameters (ΔH°, ΔS°, and ΔG°₃₇) for oligonucleotide hybridization were extracted. Consistent with the Watson-Crick model that considers both geometric and hydrogen bonding complementarity, the Z:P pair was found to contribute more to duplex stability than any mismatches involving either nonstandard nucleotide. Further, the Z:P pair is more stable than a C:G pair. The Z:G pair was found to be the most stable mismatch, forming either a deprotonated mismatched pair or a wobble base pair analogous to the stable T:G mismatch. The C:P pair is less stable, perhaps analogous to the wobble pair observed for C:O⁶-methyl-G, in which the pyrimidine is displaced into the minor groove. The Z:A and T:P mismatches are much less stable. Parameters for predicting the thermodynamics of oligonucleotides containing Z and P bases are provided. This represents the first case where this has been done for a synthetic genetic system.

Design of nucleic acid strands with long low-barrier folding pathways

A major goal of natural computing is to design biomolecules, such as nucleic acid sequences, that can be used to perform computations. We design sequences of nucleic acids that are "guaranteed" to have long folding pathways relative to their length. This particular sequences with high probability follow low-barrier folding pathways that visit a large number of distinct structures. Long folding pathways are interesting, because they demonstrate that natural computing can potentially support long and complex computations. Formally, we provide the first scalable designs of molecules whose low-barrier folding pathways, with respect to a simple, stacked pair energy model, grow superlinearly with the molecule length, but for which all significantly shorter alternative folding pathways have an energy barrier that is [Formula: see text] times that of the low-barrier pathway for any [Formula: see text] and a sufficiently long sequence.

Single-stranded DNA aptamer targeting and neutralization of anti-D alloantibody: a potential therapeutic strategy for haemolytic diseases caused by Rhesus alloantibody

BACKGROUND

Rhesus (Rh) D antigen is the most important antigen in the Rh blood group system because of its strong immunogenicity. When RhD-negative individuals are exposed to RhD-positive blood, they may produce anti-D alloantibody, potentially resulting in delayed haemolytic transfusion reactions and Rh haemolytic disease of the foetus and newborn, which are difficult to treat. Inhibition of the binding of anti-D antibody with RhD antigens on the surface of red blood cells may effectively prevent immune haemolytic diseases.

MATERIALS AND METHODS

In this study, single-stranded (ss) DNA aptamers, specifically binding to anti-D antibodies, were selected via systematic evolution of ligands by exponential enrichment (SELEX) technology. After 14 rounds of selection, the purified ssDNA was sequenced using a Personal Genome Machine system. Haemagglutination inhibition assays were performed to screen aptamers for biological activity in terms of blocking antigen-antibody reactions: the affinity and specificity of the aptamers were also determined.

RESULTS

In addition to high specificity, the aptamers which were selected showed high affinity for anti-D antibodies with dissociation constant (K_d) values ranging from 51.46±14.90 to 543.30±92.59 nM. By the combined use of specific ssDNA aptamer 7 and auxiliary ssDNA aptamer 2, anti-D could be effectively neutralised at low concentrations of the aptamers.

DISCUSSION

Our results demonstrate that ssDNA aptamers may be a novel, promising strategy for the treatment of delayed haemolytic transfusion reactions and Rh haemolytic disease of the foetus and newborn.

Computational 3D Assembling Methods for DNA: A Survey

DNA encodes the genetic information of most living beings, except viruses that use RNA. Unlike other types of molecules, DNA is not usually described by its atomic structure being instead usually described by its base-pair sequence, i.e., the textual sequence of its subsidiary molecules known as nucleotides ( adenine (A), cytosine (C), guanine (G), and thymine (T)). The three-dimensional assembling of DNA molecules based on its base-pair sequence has been, for decades, a topic of interest for many research groups all over the world. In this paper, we survey the major methods found in the literature to assemble and visualize DNA molecules from their base-pair sequences. We divided these methods into three categories: predictive methods, adaptive methods, and thermodynamic methods . Predictive methods aim to predict a conformation of the DNA from its base pair sequence, while the goal of adaptive methods is to assemble DNA base-pairs sequences along previously known conformations, as needed in scenarios such as DNA Monte Carlo simulations. Unlike these two geometric methods, thermodynamic methods are energy-based and aim to predict secondary structural motifs of DNA in cases where hydrogen bonds between base pairs might be broken because of temperature changes. We also present the major software tools that implements predictive, adaptive, and thermodynamic methods.

RNAdualPF: software to compute the dual partition function with sample applications in molecular evolution theory

Background

RNA inverse folding is the problem of finding one or more sequences that fold into a user-specified target structure s₀, i.e. whose minimum free energy secondary structure is identical to the target s₀. Here we consider the ensemble of all RNA sequences that have low free energy with respect to a given target s₀.

Results

We introduce the program RNAdualPF, which computes the dual partition functionZ^∗, defined as the sum of Boltzmann factors exp(−E(a,s₀)/RT) of all RNA nucleotide sequences a compatible with target structure s₀. Using RNAdualPF, we efficiently sample RNA sequences that approximately fold into s₀, where additionally the user can specify IUPAC sequence constraints at certain positions, and whether to include dangles (energy terms for stacked, single-stranded nucleotides). Moreover, since we also compute the dual partition functionZ^∗(k) over all sequences having GC-content k, the user can require that all sampled sequences have a precise, specified GC-content.

Using Z^∗, we compute the dual expected energy 〈E^∗〉, and use it to show that natural RNAs from the Rfam 12.0 database have higher minimum free energy than expected, thus suggesting that functional RNAs are under evolutionary pressure to be only marginally thermodynamically stable.

We show that C. elegans precursor microRNA (pre-miRNA) is significantly non-robust with respect to mutations, by comparing the robustness of each wild type pre-miRNA sequence with 2000 [resp. 500] sequences of the same GC-content generated by RNAdualPF, which approximately [resp. exactly] fold into the wild type target structure. We confirm and strengthen earlier findings that precursor microRNAs and bacterial small noncoding RNAs display plasticity, a measure of structural diversity.

Conclusion

We describe RNAdualPF, which rapidly computes the dual partition functionZ^∗ and samples sequences having low energy with respect to a target structure, allowing sequence constraints and specified GC-content. Using different inverse folding software, another group had earlier shown that pre-miRNA is mutationally robust, even controlling for compositional bias. Our opposite conclusion suggests a cautionary note that computationally based insights into molecular evolution may heavily depend on the software used.

C/C++-software for RNAdualPF is available at http://bioinformatics.bc.edu/clotelab/RNAdualPF.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1280-6) contains supplementary material, which is available to authorized users.

An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures

Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer.

Design principles for rapid folding of knotted DNA nanostructures

Knots are some of the most remarkable topological features in nature. Self-assembly of knotted polymers without breaking or forming covalent bonds is challenging, as the chain needs to be threaded through previously formed loops in an exactly defined order. Here we describe principles to guide the folding of highly knotted single-chain DNA nanostructures as demonstrated on a nano-sized square pyramid. Folding of knots is encoded by the arrangement of modules of different stability based on derived topological and kinetic rules. Among DNA designs composed of the same modules and encoding the same topology, only the one with the folding pathway designed according to the ‘free-end' rule folds efficiently into the target structure. Besides high folding yield on slow annealing, this design also folds rapidly on temperature quenching and dilution from chemical denaturant. This strategy could be used to design folding of other knotted programmable polymers such as RNA or proteins.

Driven by complementary base pairing, artificial single-chain DNA is capable of forming complex 3D architectures if an appropriate folding pathway can be realised. Here, the authors describe the design principles for rapidly folding structures, exemplified through fabrication of a nanosized square pyramid.

Pseudoknots in RNA folding landscapes

MOTIVATION

The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the folding process itself. To model and understand these processes, it is necessary to go beyond ground state structures. The study of rugged RNA folding landscapes holds the key to answer these questions. Efficient coarse-graining methods are required to reduce the intractably vast energy landscapes into condensed representations such as barrier trees or basin hopping graphs : BHG) that convey an approximate but comprehensive picture of the folding kinetics. So far, exact and heuristic coarse-graining methods have been mostly restricted to the pseudoknot-free secondary structures. Pseudoknots, which are common motifs and have been repeatedly hypothesized to play an important role in guiding folding trajectories, were usually excluded.

RESULTS

We generalize the BHG framework to include pseudoknotted RNA structures and systematically study the differences in predicted folding behavior depending on whether pseudoknotted structures are allowed to occur as folding intermediates or not. We observe that RNAs with pseudoknotted ground state structures tend to have more pseudoknotted folding intermediates than RNAs with pseudoknot-free ground state structures. The occurrence and influence of pseudoknotted intermediates on the folding pathway, however, appear to depend very strongly on the individual RNAs so that no general rule can be inferred.

AVAILABILITY AND IMPLEMENTATION

The algorithms described here are implemented in C++ as standalone programs. Its source code and Supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html.

CONTACT

qin@bioinf.uni-leipzig.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Self-assembly of large RNA structures: learning from DNA nanotechnology

Nucleic acid nanotechnology offers many methods to build self-assembled structures using RNA and DNA. These scaffolds are valuable in multiple applications, such as sensing, drug delivery and nanofabrication. Although RNA and DNA are similar molecules, they also have unique chemical and structural properties. RNA is generally less stable than DNA, but it folds into a variety of tertiary motifs that can be used to produce complex and functional nanostructures. Another advantage of using RNA over DNA is its ability to be encoded into genes and to be expressed in vivo. Here we review existing approaches for the self-assembly of RNA and DNA nanostructures and specifically methods to assemble large RNA structures. We describe de novo design approaches used in DNA nanotechnology that can be ported to RNA. Lastly, we discuss some of the challenges yet to be solved to build micron-scale, multi stranded RNA scaffolds.

Secondary Structure Prediction of Single Sequences Using RNAstructure

RNA secondary structure is often predicted using folding thermodynamics. RNAstructure is a software package that includes structure prediction by free energy minimization, prediction of base pairing probabilities, prediction of structures composed of highly probably base pairs, and prediction of structures with pseudoknots. A user-friendly graphical user interface is provided, and this interface works on Windows, Apple OS X, and Linux. This chapter provides protocols for using RNAstructure for structure prediction.

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.

Sequence Design for a Test Tube of Interacting Nucleic Acid Strands

We describe an algorithm for designing the equilibrium base-pairing properties of a test tube of interacting nucleic acid strands. A target test tube is specified as a set of desired "on-target" complexes, each with a target secondary structure and target concentration, and a set of undesired "off-target" complexes, each with vanishing target concentration. Sequence design is performed by optimizing the test tube ensemble defect, corresponding to the concentration of incorrectly paired nucleotides at equilibrium evaluated over the ensemble of the test tube. To reduce the computational cost of accepting or rejecting mutations to a random initial sequence, the structural ensemble of each on-target complex is hierarchically decomposed into a tree of conditional subensembles, yielding a forest of decomposition trees. Candidate sequences are evaluated efficiently at the leaf level of the decomposition forest by estimating the test tube ensemble defect from conditional physical properties calculated over the leaf subensembles. As optimized subsequences are merged toward the root level of the forest, any emergent defects are eliminated via ensemble redecomposition and sequence reoptimization. After successfully merging subsequences to the root level, the exact test tube ensemble defect is calculated for the first time, explicitly checking for the effect of the previously neglected off-target complexes. Any off-target complexes that form at appreciable concentration are hierarchically decomposed, added to the decomposition forest, and actively destabilized during subsequent forest reoptimization. For target test tubes representative of design challenges in the molecular programming and synthetic biology communities, our test tube design algorithm typically succeeds in achieving a normalized test tube ensemble defect ≤1% at a design cost within an order of magnitude of the cost of test tube analysis.