Structural basis for human mitochondrial tRNA maturation.

The human mitochondrial genome is transcribed into two RNAs, containing mRNAs, rRNAs and tRNAs, all dedicated to produce essential proteins of the respiratory chain. The precise excision of tRNAs by the mitochondrial endoribonucleases (mt-RNase), P and Z, releases all RNA species from the two RNA transcripts. The tRNAs then undergo 3'-CCA addition. In metazoan mitochondria, RNase P is a multi-enzyme assembly that comprises the endoribonuclease PRORP and a tRNA methyltransferase subcomplex. The requirement for this tRNA methyltransferase subcomplex for mt-RNase P cleavage activity, as well as the mechanisms of pre-tRNA 3'-cleavage and 3'-CCA addition, are still poorly understood. Here, we report cryo-EM structures that visualise four steps of mitochondrial tRNA maturation: 5' and 3' tRNA-end processing, methylation and 3'-CCA addition, and explain the defined sequential order of the tRNA processing steps. The methyltransferase subcomplex recognises the pre-tRNA in a distinct mode that can support tRNA-end processing and 3'-CCA addition, likely resulting from an evolutionary adaptation of mitochondrial tRNA maturation complexes to the structurally-fragile mitochondrial tRNAs. This subcomplex can also ensure a tRNA-folding quality-control checkpoint before the sequential docking of the maturation enzymes. Altogether, our study provides detailed molecular insight into RNA-transcript processing and tRNA maturation in human mitochondria.

Folding molecular origami from ribosomal RNA.

Approximately 80 percent of the total RNA in cells is ribosomal RNA (rRNA), making it an abundant and inexpensive natural source of long, single-stranded nucleic acid, which could be used as raw material for the fabrication of molecular origami. In this study, we demonstrate efficient and robust construction of 2D and 3D origami nanostructures utilizing cellular rRNA as a scaffold and DNA oligonucleotide staples. We present calibrated protocols for the robust folding of contiguous shapes from one or two rRNA subunits that are efficient to allow folding using crude extracts of total RNA. We also show that RNA maintains stability within the folded structure. Lastly, we present a novel and comprehensive analysis and insights into the stability of RNA:DNA origami nanostructures and demonstrate their enhanced stability when coated with polylysine-polyethylene glycol in different temperatures, low Mg2+ concentrations, human serum, and in the presence of nucleases (DNase I or RNase H). Thus, laying the foundation for their potential implementation in emerging biomedical applications, where folding rRNA into stable structures outside and inside cells would be desired.

Design and Synthesis of DNA Origami Nanostructures to Control TNF Receptor Activation.

Clustering of type II tumor necrosis factor (TNF) receptors (TNFRs) is essential for their activation, yet currently available drugs fail to activate signaling. Some strategies aim to cluster TNFR by using multivalent streptavidin or scaffolds based on dextran or graphene. However, these strategies do not allow for control of the valency or spatial organization of the ligands, and consequently control of the TNFR activation is not optimal. DNA origami nanostructures allow nanometer-precise control of the spatial organization of molecules and complexes, with defined spacing, number and valency. Here, we demonstrate the design and characterization of a DNA origami nanostructure that can be decorated with engineered single-chain TNF-related apoptosis-inducing ligand (SC-TRAIL) complexes, which show increased cell killing compared to SC-TRAIL alone on Jurkat cells. The information in this chapter can be used as a basis to decorate DNA origami nanostructures with various proteins, complexes, or other biomolecules.

Synthetic molecular switches driven by DNA-modifying enzymes.

Taking inspiration from natural systems, in which molecular switches are ubiquitous in the biochemistry regulatory network, we aim to design and construct synthetic molecular switches driven by DNA-modifying enzymes, such as DNA polymerase and nicking endonuclease. The enzymatic treatments on our synthetic DNA constructs controllably switch ON or OFF the sticky end cohesion and in turn cascade to the structural association or disassociation. Here we showcase the concept in multiple DNA nanostructure systems with robust assembly/disassembly performance. The switch mechanisms are first illustrated in minimalist systems with a few DNA strands. Then the ON/OFF switches are realized in complex DNA lattice and origami systems with designated morphological changes responsive to the specific enzymatic treatments.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments.

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. We use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization.

Diamond-lattice photonic crystals assembled from DNA origami.

Colloidal self-assembly allows rational design of structures on the micrometer and submicrometer scale. One architecture that can generate complete three-dimensional photonic bandgaps is the diamond cubic lattice, which has remained difficult to realize at length scales comparable with the wavelength of visible or ultraviolet light. In this work, we demonstrate three-dimensional photonic crystals self-assembled from DNA origami that act as precisely programmable patchy colloids. Our DNA-based nanoscale tetrapods crystallize into a rod-connected diamond cubic lattice with a periodicity of 170 nanometers. This structure serves as a scaffold for atomic-layer deposition of high-refractive index materials such as titanium dioxide, yielding a tunable photonic bandgap in the near-ultraviolet.

Minimizing Structural Heterogeneity in DNA Self-Assembled Dye Templating via DNA Origami-Tuned Conformations.

With recent advances in DNA-templated dye aggregation for leveraging and engineering molecular excitons, a need exists for minimizing structural heterogeneity. Holliday Junction complexes (HJ) are commonly used to covalently template dye aggregates on their core; however, the global conformation of HJ is detrimentally dynamic. Here, the global conformation of the HJ is selectively tuned by restricting its position and orientation by using a sheet-like DNA origami construct (DOC) physisorbed on glass. The HJ arms are fixed with four different designed interduplex angles (IDAs). Atomic force microscopy confirmed that the HJs are bound to the surface of DOC with tuned IDAs. Dye orientation distributions were determined by combining dipole imaging and super-resolution microscopy. All IDAs led to dye orientations having dispersed distributions along planes perpendicular to the HJ plane, suggesting that stacking occurred between the dye and the neighboring DNA bases. The dye-base stacking interpretation was supported by increasing the size of the core cavity. The narrowest IDA minimizes structural heterogeneity and suggests dye intercalation. A strong correlation is found between the IDA and the orientation of the dye along the HJ plane. These results show that the HJ imposes restrictions on the dye and that the dye-DNA interactions are always present regardless of global conformation. The implications of our results are discussed for the scalability of dye aggregates using DNA self-assembly. Our methodology provides an avenue for the solid-supported single-molecule characterization of molecular assemblies templated on biomolecules─such as DNA and protein templates involved in light-harvesting and catalysis─with tuned conformations and restricted in position and orientation.

Chemical Annealing Restructures RNA for Nanopore Detection.

RNA is a key biochemical marker, yet its chemical instability and complex secondary structure hamper its integration into DNA nanotechnology-based sensing platforms. Relying on the denaturation of the native RNA structure using urea, we show that restructured DNA/RNA hybrids can readily be prepared at room temperature. Using solid-state nanopore sensing, we demonstrate that the structures of our DNA/RNA hybrids conform to the design at the single-molecule level. Employing this chemical annealing procedure, we mitigate RNA self-cleavage, enabling the direct detection of restructured RNA molecules for biosensing applications.

Functionalizing DNA Origami by Triplex-Directed Site-Specific Photo-Cross-Linking.

Here, we present a cross-linking approach to covalently functionalize and stabilize DNA origami structures in a one-pot reaction. Our strategy involves adding nucleotide sequences to adjacent staple strands, so that, upon assembly of the origami structure, the extensions form short hairpin duplexes targetable by psoralen-labeled triplex-forming oligonucleotides bearing other functional groups (pso-TFOs). Subsequent irradiation with UVA light generates psoralen adducts with one or both hairpin staples leading to site-specific attachment of the pso-TFO (and attached group) to the origami with ca. 80% efficiency. Bis-adduct formation between strands in proximal hairpins further tethers the TFO to the structure and generates "superstaples" that improve the structural integrity of the functionalized complex. We show that directing cross-linking to regions outside of the origami core dramatically reduces sensitivity of the structures to thermal denaturation and disassembly by T7 RNA polymerase. We also show that the underlying duplex regions of the origami core are digested by DNase I and thus remain accessible to read-out by DNA-binding proteins. Our strategy is scalable and cost-effective, as it works with existing DNA origami structures, does not require scaffold redesign, and can be achieved with just one psoralen-modified oligonucleotide.

Molecular phylogeny and secondary structure analysis of hop stunt viroid (HSVd) associated with Mulberry (Morus alba) in India.

Hop stunt viroid (HSVd), a small, single stranded, circular, non-coding infectious RNA known to cause infection in various economically important crop plants. In the present investigation, a study was conducted in the southern part of Karnataka districts of India to detect the possible association of HSVd infection in mulberry plants. A total of 41 mulberry plants showing typical viroid-like symptoms along with asymptomatic samples were collected and screened using conventional Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) using a specific set of HSVd-Fw/ HSVd-Re primers. Out of 41 samples, the study confirmed the presence of HSVd in six samples of mulberry collected from Ramanagara (1 sample), Chikkaballapur (3 samples) and Doddaballapura (2 samples) regions with an expected HSVd amplicon size of ∼ 290-300 nucleotides. The mechanical transmission of HSVd was also confirmed on cucumber (cv. Suyo) seedlings through bioassay, which was reconfirmed by RT-PCR. The amplicons were cloned, sequenced, and the representative nucleotide sequences were deposited in the NCBI GenBank. Subsequently, molecular phylogenetic analysis showed that HSVd mulberry isolates from this study were most closely related to grapevine isolates, indicating a common origin. On the other hand, it was shown to belong to a different group from mulberry isolates so far reported from Iran, Italy, Lebanon, and China. The secondary structure analysis of HSVd mulberry Indian isolates exhibited substitutions in the terminal left, pathogenicity, and variable regions compared to those of the Indian grapevine isolates. As far as this study is concerned, HSVd was detected exclusively in some mulberry plants with viral-like symptoms, but the pathogenesis and symptom expression needs to be further investigated to establish the relationship between HSVd and the disease symptoms in the mulberry plants.

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.

Classified Dynamic Programming in RNA Structure Analysis.

Analysis of the folding space of RNA generally suffers from its exponential size. With classified Dynamic Programming algorithms, it is possible to alleviate this burden and to analyse the folding space of RNA in great depth. Key to classified DP is that the search space is partitioned into classes based on an on-the-fly computed feature. A class-wise evaluation is then used to compute class-wide properties, such as the lowest free energy structure for each class, or aggregate properties, such as the class' probability. In this paper we describe the well-known shape and hishape abstraction of RNA structures, their power to help better understand RNA function and related methods that are based on these abstractions.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

LocARNA 2.0: Versatile Simultaneous Alignment and Folding of RNAs.

Generating accurate alignments of non-coding RNA sequences is indispensable in the quest for understanding RNA function. Nevertheless, aligning RNAs remains a challenging computational task. In the twilight-zone of RNA sequences with low sequence similarity, sequence homologies and compatible, favorable (a priori unknown) structures can be inferred only in dependency of each other. Thus, simultaneous alignment and folding (SA&F) remains the gold-standard of comparative RNA analysis, even if this method is computationally highly demanding. This text introduces to the recent release 2.0 of the software package LocARNA, focusing on its practical application. The package enables versatile, fast and accurate analysis of multiple RNAs. For this purpose, it implements SA&F algorithms in a specific, lightweight flavor that makes them routinely applicable in large scale. Its high performance is achieved by combining ensemble-based sparsification of the structure space and banding strategies. Probabilistic banding strongly improves the performance of LocARNA 2.0 even over previous releases, while simplifying its effective use. Enabling flexible application to various use cases, LocARNA provides tools to globally and locally compare, cluster, and multiply aligned RNAs based on optimization and probabilistic variants of SA&F, which optionally integrate prior knowledge, expressible by anchor and structure constraints.

VarGibbs Usage in the Optimization of Nearest-Neighbor Parameters and Prediction of Melting Temperature of RNA Duplexes.

The nearest-neighbor (NN) model is a general tool for the evaluation for oligonucleotide thermodynamic stability. It is primarily used for the prediction of melting temperatures but has also found use in RNA secondary structure prediction and theoretical models of hybridization kinetics. One of the key problems is to obtain the NN parameters from melting temperatures, and VarGibbs was designed to obtain those parameters directly from melting temperatures. Here we will describe the basic workflow from RNA melting temperatures to NN parameters with the use of VarGibbs. We start by a brief revision of the basic concepts of RNA hybridization and of the NN model and then show how to prepare the data files, run the parameter optimization, and interpret the results.

RNA Secondary Structure Modeling Following the IPANEMAP Workflow.

The structure of RNA molecules and their complexes are crucial for understanding biology at the molecular level. Resolving these structures holds the key to understanding their manifold structure-mediated functions ranging from regulating gene expression to catalyzing biochemical processes. Predicting RNA secondary structure is a prerequisite and a key step to accurately model their three dimensional structure. Although dedicated modelling software are making fast and significant progresses, predicting an accurate secondary structure from the sequence remains a challenge. Their performance can be significantly improved by the incorporation of experimental RNA structure probing data. Many different chemical and enzymatic probes have been developed; however, only one set of quantitative data can be incorporated as constraints for computer-assisted modelling. IPANEMAP is a recent workflow based on RNAfold that can take into account several quantitative or qualitative data sets to model RNA secondary structure. This chapter details the methods for popular chemical probing (DMS, CMCT, SHAPE-CE, and SHAPE-Map) and the subsequent analysis and structure prediction using IPANEMAP.

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).

Assessing the Quality of Cotranscriptional Folding Simulations.

Structural changes in RNAs are an important contributor to controlling gene expression not only at the posttranscriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5' region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Not only such elements occur naturally, but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labor-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach because of both the size of the molecules and the timescales of interest. Even at the simplified level of secondary structures, further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription-regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux® or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.

How Parameters Influence SHAPE-Directed Predictions.

The structure of an RNA sequence encodes information about its biological function. Dynamic programming algorithms are often used to predict the conformation of an RNA molecule from its sequence alone, and adding experimental data as auxiliary information improves prediction accuracy. This auxiliary data is typically incorporated into the nearest neighbor thermodynamic model22 by converting the data into pseudoenergies. Here, we look at how much of the space of possible structures auxiliary data allows prediction methods to explore. We find that for a large class of RNA sequences, auxiliary data shifts the predictions significantly. Additionally, we find that predictions are highly sensitive to the parameters which define the auxiliary data pseudoenergies. In fact, the parameter space can typically be partitioned into regions where different structural predictions predominate.