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Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”—given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms largely rely on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs are also investigated and recently been shown to outperform free energy–based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework that can easily incorporate an arbitrary number of rewards (free energy or nonparametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions of nonpseudoknotted secondary structures than the structure prediction algorithm used, thus validating the promise of the approach. Summary of Contribution: ExpertRNA is a new algorithm inspired by a biological problem. It is applied to solve the problem of secondary structure prediction for RNA molecules given an input sequence. The computational contribution is given by the design of a multibranch, multiexpert rollout algorithm that enables the use of several state-of-the-art approaches as base heuristics and allowing several experts to evaluate partial candidate solutions generated, thus avoiding assuming the reward being optimized by an RNA molecule when folding. Our implementation allows for the effective use of parallel computational resources as well as to control the size of the rollout tree as the algorithm progresses. The problem of RNA secondary structure prediction is of primary importance within the biology field because the molecule structure is strongly related to its functionality. Whereas the contribution of the paper is in the algorithm, the importance of the application makes ExpertRNA a showcase of the relevance of computationally efficient algorithms in supporting scientific discovery.

We present here the combination of experimental and computational modeling tools for the design and characterization of protein–DNA hybrid nanostructures. Our work incorporates several features in the design of these nanostructures: (1) modeling of the protein–DNA linker identity and length; (2) optimizing the design of protein–DNA cages to account for mechanical stresses; (3) probing the incorporation efficiency of protein–DNA conjugates into DNA nanostructures. The modeling tools were experimentally validated using structural characterization methods like cryo-TEM and AFM. Our method can be used for fitting low-resolution electron density maps when structural insights cannot be deciphered from experiments, as well as enable in-silico validation of nanostructured systems before their experimental realization. These tools will facilitate the design of complex hybrid protein–DNA nanostructures that seamlessly integrate the two different biomolecules.

We introduce a new online database of nucleic acid nanostructures for the field of DNA and RNA nanotechnology. The database implements an upload interface, searching and database browsing. Each deposited nanostructures includes an image of the nanostructure, design file, an optional 3D view, and additional metadata such as experimental data, protocol or literature reference. The database accepts nanostructures in any preferred format used by the uploader for the nanostructure design. We further provide a set of conversion tools that encourage design file conversion into common formats (oxDNA and PDB) that can be used for setting up simulations, interactive editing or 3D visualization. The aim of the repository is to provide to the DNA/RNA nanotechnology community a resource for sharing their designs for further reuse in other systems and also to function as an archive of the designs that have been achieved in the field so far. Nanobase.org is available at https://nanobase.org/.

The self-assembly of colloidal diamond (CD) crystals is considered as one of the most coveted goals of nanotechnology, both from the technological and fundamental points of view. For applications, colloidal diamond is a photonic crystal which can open new possibilities of manipulating light for information processing. From a fundamental point of view, its unique symmetry exacerbates a series of problems that are commonly faced during the self-assembly of target structures, such as the presence of kinetic traps and the formation of crystalline defects and alternative structures (polymorphs). Here we demonstrate that all these problems can be systematically addressed via SAT-assembly, a design framework that converts self-assembly into a satisfiability problem. Contrary to previous solutions (requiring four or more components), we prove that the assembly of the CD crystal only requires a binary mixture. Moreover, we use molecular dynamics simulations of a system composed by nearly a million nucleotides to test a DNA nanotechnology design that constitutes a promising candidate for experimental realization., 12 pages, 4 figures

OxDNA and oxRNA are popular coarse-grained models used by the DNA/RNA nanotechnology community to prototype, analyze and rationalize designed DNA and RNA nanostructures. Here, we present oxDNA.org, a graphical web interface for running, visualizing and analyzing oxDNA and oxRNA molecular dynamics simulations on a GPU-enabled high performance computing server. OxDNA.org automatically generates simulation files, including a multi-step relaxation protocol for structures exported in non-physical states from DNA/RNA design tools. Once the simulation is complete, oxDNA.org provides an interactive visualization and analysis interface using the browser-based visualizer oxView to facilitate the understanding of simulation results for a user’s specific structure. This online tool significantly lowers the entry barrier of integrating simulations in the nanostructure design pipeline for users who are not experts in the technical aspects of molecular simulation. The webserver is freely available at oxdna.org., Graphical Abstract Graphical AbstractOxDNA.org is a server for DNA/RNA nanotechnology simulations. Nanostructures can be imported from popular nanotechnology design tools. The server then runs oxDNA/oxRNA simulations and provides a graphical user interface to visualize and analyze the results.

Nucleation in systems with a metastable liquid-gas critical point is the prototypical example of a two-step nucleation process, in which the appearance of the critical nucleus is preceded by the formation of a liquid-like density fluctuation. So far, the majority of studies on colloidal and protein crystallization have focused on one-component systems, and we are lacking a clear description of two-step nucleation processes in multicomponent systems, where critical fluctuations involve coupled density and concentrations inhomogeneities. Here, we examine the nucleation process of a binary mixture of patchy particles designed to nucleate into a diamond lattice. By combining Gibbs-ensemble simulations and direct nucleation simulations over a wide range of thermodynamic conditions, we are able to pin down the role of the liquid-gas metastable phase diagram on the nucleation process. In particular, we show that the strongest enhancement of crystallization occurs at an azeotropic point with the same stoichiometric composition of the crystal., 9 pages, 7 figures

The identity and timing of environmental stimulus play a pivotal role in living organisms in programming their signaling networks and developing specific phenotypes. The ability to unveil history-dependent signals will advance our understanding of temporally regulated biological processes. Here, we have developed a two-input, five-state DNA finite-state machine (FSM) to sense and record the temporally ordered inputs. The spatial organization of the processing units on DNA origami enables facile modulation of the energy landscape of DNA strand displacement reactions, allowing precise control of the reactions along predefined paths for different input orders. The use of spatial constraints brings about a simple, modular design for the FSM with a minimum set of orthogonal components and confers minimized leaky reactions and fast kinetics. The FSM demonstrates the capability of sensing the temporal orders of two microRNAs, highlighting its potential for temporally resolved biosensing and smart therapeutics.

Nucleic acid interactions under crowded environments are of great importance for biological processes and nanotechnology. However, the kinetics and thermodynamics of nucleic acid interactions in a crowded environment remain poorly understood. We use a coarse-grained model of DNA to study the kinetics and thermodynamics of DNA duplex and hairpin formation in crowded environments. We find that crowders can increase the melting temperature of both an 8-mer DNA duplex and a hairpin with a stem of 6-nt depending on the excluded volume fraction of crowders in solution and the crowder size. The crowding induced stability originates from the entropic effect caused by the crowding particles in the system. Additionally, we study the hybridization kinetics of DNA duplex formation and the formation of hairpin stems, finding that the reaction rate kon is increased by the crowding effect, while koff is changed only moderately. The increase in kon mostly comes from increasing the probability of reaching a transition state with one base pair formed. A DNA strand displacement reaction in a crowded environment is also studied with the model and we find that rate of toehold association is increased, with possible applications to speeding up strand displacement cascades in nucleic acid nanotechnology.

DNA origami has emerged as a highly programmable method to construct customized objects and functional devices in the 10–100 nm scale. Scaling up the size of the DNA origami would enable many potential applications, which include metamaterial construction and surface-based biophysical assays. Here we demonstrate that a six-helix bundle DNA origami nanostructure in the submicrometre scale (meta-DNA) could be used as a magnified analogue of single-stranded DNA, and that two meta-DNAs that contain complementary ‘meta-base pairs’ can form double helices with programmed handedness and helical pitches. By mimicking the molecular behaviours of DNA strands and their assembly strategies, we used meta-DNA building blocks to form diverse and complex structures on the micrometre scale. Using meta-DNA building blocks, we constructed a series of DNA architectures on a submicrometre-to-micrometre scale, which include meta-multi-arm junctions, three-dimensional (3D) polyhedrons, and various 2D/3D lattices. We also demonstrated a hierarchical strand-displacement reaction on meta-DNA to transfer the dynamic features of DNA into the meta-DNA. This meta-DNA self-assembly concept may transform the microscopic world of structural DNA nanotechnology. A six-helix bundle DNA structure called meta-DNA has now been assembled and shown to possess some structural properties similar to those of single-stranded DNA. Two meta-DNAs containing complementary ‘meta-base pairs’ are shown to form double helices. Meta-DNA building blocks are also used to construct a series of DNA architectures and to perform a hierarchical strand-displacement reaction.

Molecular engineering seeks to create functional entities for the modular use in the bottom-up design of nanoassemblies that can perform complex tasks. Such systems require fuel-consuming nanomotors that can actively drive downstream passive followers. Most molecular motors are driven by Brownian motion, but the generated forces are scattered and insufficient for efficient transfer to passive second-tier components, which is why nanoscale driver-follower systems have not been realized. Here, we describe bottom-up construction of a DNA-nanomachine that engages in an active, autonomous and rhythmical pulsing motion of two rigid DNA-origami arms, driven by chemical energy. We show the straightforward coupling of the active nanomachine to a passive follower unit, to which it then transmits its own motion, thus constituting a genuine driver-follower pair. Our work introduces a versatile fuel-consuming nanomachine that can be coupled with passive modules in nanoassemblies, the function of which depends on downstream sequences of motion.

We propose a general framework for solving inverse self-assembly problems, i.e. designing interactions between elementary units such that they assemble spontaneously into a predetermined structure. Our approach uses patchy particles as building blocks, where the different units bind at specific interaction sites (the patches), and we exploit the possibility of having mixtures with several components. The interaction rules between the patches is determined by transforming the combinatorial problem into a Boolean satisfiability problem (SAT) which searches for solutions where all bonds are formed in the target structure. Additional conditions, such as the non-satisfiability of competing structures (e.g. metastable states) can be imposed, allowing to effectively design the assembly path in order to avoid kinetic traps. We demonstrate this approach by designing and numerically simulating a cubic diamond structure from four particle species that assembles without competition from other polymorphs, including the hexagonal structure., Comment: 12 pages, 4 figures

An emerging hallmark across many human diseases - such as cancer, autoimmune and neurodegenerative disorders - is the aberrant transcription of typically silenced repetitive elements. Once transcribed they can mimic pathogen-associated molecular patterns and bind pattern recognition receptors, thereby engaging the innate immune system and triggering inflammation in a process known as "viral mimicry". Yet how to quantify pathogen mimicry, and the degree to which it is shaped by natural selection, remains a gap in our understanding of both genome evolution and the immunological basis of disease. Here we propose a theoretical framework that combines recent biological observations with statistical physics and population genetics to quantify the selective forces on virus-like features generated by repeats and integrate these forces into predictive evolutionary models. We establish that many repeat families have evolutionarily maintained specific classes of viral mimicry. We show that for HSATII and intact LINE-1 selective forces maintain CpG motifs, while for a set of SINE and LINE elements the formation of long double-stranded RNA is more prevalent than expected from a neutral evolutionary model. We validate our models by showing predicted immunostimulatory inverted SINE elements bind the MDA5 receptor under conditions of epigenetic dysregulation and that they are disproportionately present during intron retention when RNA splicing is pharmacologically inhibited. We conclude viral mimicry is a general evolutionary mechanism whereby genomes co-opt features generated by repetitive sequences to trigger the immune system, acting as a quality control system to flag genome dysregulation. We demonstrate these evolutionary principles can be learned and applied to predictive models. Our work therefore serves as a resource to identify repeats with candidate immunostimulatory features and leverage them therapeutically.

Nature-inspired molecular machines can exert mechanical forces by controlling and varying the distance between two molecular subunits in response to different inputs. Here, we present an automated molecular linear actuator composed of T7 RNA polymerase (T7RNAP) and a DNA [2]rotaxane. A T7 promoter region and terminator sequences are introduced into the rotaxane axle to achieve automated and iterative binding and detachment of T7RNAP in a self-controlled fashion. Transcription by T7RNAP is exploited to control the release of the macrocycle from a single-stranded (ss) region in the T7 promoter to switch back and forth from a static state (hybridized macrocycle) to a dynamic state (movable macrocycle). During transcription, the T7RNAP keeps restricting the movement range on the axle available for the interlocked macrocycle and prevents its return to the promotor region. Since this range is continuously depleted as T7RNAP moves along, a directional and active movement of the macrocycle occurs. When it reaches the transcription terminator, the polymerase detaches, and the system can reset as the macrocycle moves back to hybridize again to the ss-promoter docking site. The hybridization is required for the initiation of a new transcription cycle. The rotaxane actuator runs autonomously and repeats these self-controlled cycles of transcription and movement as long as NTP-fuel is available.

Molecular simulation has become an integral part of the DNA/RNA nanotechnology research pipeline. In particular, understanding the dynamics of structures and single-molecule events has improved the precision of nanoscaffolds and diagnostic tools. Here we present oxView, a design tool for visualization, design, editing and analysis of simulations of DNA, RNA and nucleic acid-protein nanostructures. oxView provides an accessible software platform for designing novel structures, tweaking existing designs, preparing them for simulation in the oxDNA/RNA molecular simulation engine and creating visualizations of simulation results. In several examples, we present procedures for using the tool, including its advanced features that couple the design capabilities with a coarse-grained simulation engine and scripting interface that can programmatically edit structures and facilitate design of complex structures from multiple substructures. These procedures provide a practical basis from which researchers, including experimentalists with limited computational experience, can integrate simulation and 3D visualization into their existing research programs.

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

In dynamic nucleic acids nanotechnology, strand displacement is a widely used mechanism where one strand from a hybridized duplex is exchanged with an invading strand which binds to a toehold, a single-stranded region on the duplex. With proper design and kinetic control, strand displacement is used to perform logic operations on molecular level to trigger the conformational change in nanostructures, initiate cascaded reactions, or even for in vivo diagnostics and treatments. While systematic experimental studies have been carried out to probe the kinetics of strand displacement in DNA, there has not been a comparable systematic work done for RNA or RNA-DNA hybrid systems. Here, we experimentally study how toehold length, toehold location (5′ or 3′ end of the strand) and mismatches influence the strand displacement kinetics. Through comparing the reaction rates, combined with previous theoretical studies, we observed reaction acceleration with increasing toehold length and placement of toehold at 5′end of the substrate. We find that mismatches closer to the interface of toehold and duplex slow down the reaction more than remote mismatches. Comparison of RNA displacement and DNA displacement with hybrid displacement (RNA invading DNA or DNA invading RNA) is in part explainable by the thermodynamic stabilities of the respective toehold regions, but also suggest that the rearrangement from B-form to A-form helix in case of RNA invading DNA might play a role in the kinetics. The measured kinetics of toehold-mediated strand displacement will be important in understanding and construction of more complex dynamic nucleic acid systems.

Recent advances in biotechnology are beginning to generate whole immunome datasets, which will enable the comparison of immune repertoires between individuals, e.g., to assess immunocompetence. Existing algorithms cluster cell types based on the relative expression abundance of about 20 000 genes, but such algorithms have limited utility when comparing immunome datasets with many higher orders of magnitude (>1012) of variation, such as occurs in immunoreceptor sequences in highly polyclonal naive repertoires.In this paper we present a method for comparing immune repertoires by identifying macro-level features that are conserved between similar individuals. Our method allows us to detect some blind spots in naive populations and to assess whether a repertoire is likely complete by examining only a sample of its sequences.Author SummaryIn this paper we present a method for comparing the immune repertoire of different individuals. Repertoires are represented by a sample of genetic sequences. Our technique coarse grains each individual’s data into groups, matches groups between individual’s and finds significant differences.

SARS-CoV-2 infection can lead to acute respiratory syndrome in patients, which can be due in part to dysregulated immune signalling. We analyze here the occurrences of CpG dinucleotides, which are putative pathogen-associated molecular patterns, along the viral sequence. Carrying out a comparative analysis with other ssRNA viruses and within the Coronaviridae family, we find the CpG content of SARS-CoV-2, while low compared to other betacoronaviruses, widely fluctuates along its primary sequence. While the CpG relative abundance and its associated CpG force parameter are low for the spike protein (S) and comparable to circulating seasonal coronaviruses such as HKU1, they are much greater and comparable to SARS and MERS for the 3'-end of the viral genome. In particular, the nucleocapsid protein (N), whose transcripts are relatively abundant in the cytoplasm of infected cells and present in the 3'UTRs of all subgenomic RNA, has high CpG content. We speculate this dual nature of CpG content can confer to SARS-CoV-2 high ability to both enter the host and trigger pattern recognition receptors (PRRs) in different contexts. We then investigate the evolution of synonymous mutations since the outbreak of the COVID-19 pandemic. Using a new application of selective forces on dinucleotides to estimate context driven mutational processes, we find that synonymous mutations seem driven both by the viral codon bias and by the high value of the CpG force in the N protein, leading to a loss in CpG content. Sequence motifs preceding these CpG-loss-associated loci match recently identified binding patterns of the Zinc Finger anti-viral Protein (ZAP) protein. Funding: This work was partially supported by the ANR19 Decrypted CE30-0021-01 grants. B.G. was supported by National Institutes of Health grants 7R01AI081848-04, 1R01CA240924-01, a Stand Up to Cancer - Lustgarten Foundation Convergence Dream Team Grant, and The Pershing Square Sohn Prize - Mark Foundation Fellow supported by funding from The Mark Foundation for Cancer Research.

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”: given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets.In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions than the structure prediction algorithm used, thus validating the promise of the approach.

The emerging field of hybrid DNA–protein nanotechnology brings with it the potential for many novel materials which combine the addressability of DNA nanotechnology with the versatility of protein interactions. However, the design and computational study of these hybrid structures is difficult due to the system sizes involved. To aid in the design and in silico analysis process, we introduce here a coarse-grained DNA/RNA–protein model that extends the oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on an anisotropic network model representation. Fully equipped with analysis scripts and visualization, our model aims to facilitate hybrid nanomaterial design towards eventual experimental realization, as well as enabling study of biological complexes. We further demonstrate its usage by simulating DNA–protein nanocage, DNA wrapped around histones, and a nascent RNA in polymerase.

Morphospaces—representations of phenotypic characteristics—are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype–phenotype map, a type of developmental bias or “findability constraint,” which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to “find.”

One of the fundamental goals of nanotechnology is to exploit selective and directional interactions between molecules to design particles that self-assemble into desired structures, from capsids, to nano-clusters, to fully formed crystals with target properties (e.g. optical, mechanical, etc.). Here we provide a general framework which transforms the inverse problem of self-assembly of colloidal crystals into a Boolean satisfiability problem for which solutions can be found numerically. Given a reference structure and the desired number of components, our approach produces designs for which the target structure is an energy minimum, and also allows to exclude solutions that correspond to competing structures. We demonstrate the effectiveness of our approach by designing model particles that spontaneously nucleate milestone structures such as the cubic diamond, the pyrochlore and the clathrate lattices., Comment: 12 pages, 3 figures

Nucleic acid interactions under crowded environments are of great importance for biological processes and nanotechnology. However, the kinetics and thermodynamics of nucleic acid interactions in a crowded environment remain poorly understood. We use a coarse-grained model of DNA to study the kinetics and thermodynamics of DNA duplex and hairpin formation in crowded environments. We find that crowders can increase the melting temperature of both an 8-mer DNA duplex and a hairpin with a stem of 6-nt depending on the excluded volume fraction of crowders in solution and the crowder size. The crowding induced stability originates from the entropic effect caused by the crowding particles in the system. Additionally, we study the hybridization kinetics of DNA duplex formation and the formation of hairpin stems, finding that the reaction ratekonis increased by the crowding effect, whilekoffis changed only moderately. The increase inkonmostly comes from increasing the probability of reaching a transition state with one base pair formed. A DNA strand displacement reaction in a crowded environment is also studied with the model and we find that rate of toehold association is increased, with possible applications to speeding up strand displacement cascades in nucleic acid nanotechnology.

Electron-deficient chlorine covalently immobilised on an amido group of hyaluronic acid (HA) can be potentially exceptional for applications requiring biodegradable and biocompatible polymers with enhanced antibacterial or antiviral activity. This expectation is supported by the assumption that a small amount of HA chloramide (HACl) is formed in the extracellular matrix under inflammatory conditions by a reaction of endogenous HA with hypochlorous acid (HClO) generated by a myeloperoxidase/H2O2/Cl- system. HACl synthesis optimisation showed significant limitations of HClO as an oxidative agent where only lower degrees of substitution (DS) was achieved. Commercially available oxidative agents based on chlorinated isocyanuric acid were successfully tested, producing the HA chain with almost entirely chlorinated amidic groups. The structure of the final HACl was thoroughly studied using advanced 2-dimensional NMR methodologies and LC/MS. Stability of HACl at different temperatures was monitored over 12 months. Preliminary antimicrobial and antiviral tests demonstrated the potential of HACl for applications in biomedicine.

This work seeks to remedy two deficiencies in the current nucleic acid nanotechnology software environment: the lack of both a fast and user-friendly visualization tool and a standard for common structural analyses of simulated systems. We introduce here oxView, a web browser-based visualizer that can load structures with over 1 million nucleotides, create videos from simulation trajectories, and allow users to perform basic edits to DNA and RNA designs. We additionally introduce open-source software tools for extracting common structural parameters to characterize large DNA/RNA nanostructures simulated using the coarse-grained modeling tool, oxDNA, which has grown in popularity in recent years and is frequently used to prototype new nucleic acid nanostructural designs, model biophysics of DNA/RNA processes, and rationalize experimental results. The newly introduced software tools facilitate the computational characterization of DNA/RNA designs by providing multiple analysis scripts, including mean structures and structure flexibility characterization, hydrogen bond fraying, and interduplex angles. The output of these tools can be loaded into oxView, allowing users to interact with the simulated structure in a 3D graphical environment and modify the structures to achieve the required properties. We demonstrate these newly developed tools by applying them toin silicodesign, optimization and analysis of a range of DNA and RNA nanostructures.

In this paper, we are proposing a new formulation of dissipated energy of hard rubbers as a function of the deformation energy expressed by the Yeoh hyperelastic model. Torsion deformation is considered as a planar deformation of a simple shear on the surface of a cylinder. Thus the deformation energy is dependent only on the first invariant of strain. Based on the experiment, a “hyperelastic proportional damping” (HPD) is proposed for hard rubbers under finite strains. Such damping is analogical to the model of proportional damping in the linear theory of viscoelasticity, i.e. the dissipated energy is proportional to the deformation energymultiplied by the frequency of dynamic harmonic loading. To obtain the experimental data, samples of hard EPDM rubbers of different harnesses were dynamically tested on a torsional test rig for different frequencies and amplitudes. The Yeoh model is chosen since the deformation function is dependent only on the first strain invariant for the description of the simple shear of a surface cylinder. The Yeoh constants are evaluated by curve fitting of the analytical stress function to the experimental torsion stress-deformation curve. The constants are used to express the deformation energy of the Yeoh model for specific cases of tested rubbers. The coefficients of hyperelastic proportional damping are evaluated on the basis of experimental results.

This paper describes the methodology and results of the complex modulus estimation of rubber segments pressed between the disk and the rim of a rubber-damped railway wheel. The solution comes out from the method proposed and validated at rubber constant identification on the case of the steel beam with rubber layer. In the vicinity of eigenfrequency its dependence to Young modulus is quasilinear and can be solved effectively by the gradient method. The similar behavior showed itself also for dependence of the modal damping constant on the damping coefficient. The ascertained results of tuned rubber material constants, i.e. the modulus of elasticity and a loss factor, of the rail rubber segments are very valuable for prediction of modal behavior of the new resilient wheels and they are qualitatively in accordance with the behavior of hard synthetic rubbers.

Pattern recognition receptors (PRRs) protect against host invasion by detecting specific molecular patterns found in pathogens and initiating an immune response. While microbial-derived PRR ligands have been extensively characterized, the contribution and relevance of endogenous ligands to PRR activation during viral infection remain overlooked. In this work, we characterize the landscape of endogenous ligands that engage RIG-I-like receptors (RLRs) upon infection by a positive-sense RNA virus, a negative-sense RNA virus or a retrovirus. We found that several endogenous RNAs transcribed by RNA polymerase 3 (Pol3) specifically engage RLRs, and in particular the family of small non-coding repeats Y-RNAs, which presents the highest affinity as RIG-I ligands. We show that this recognition is dependent on Y-RNA mimicking viral secondary structure and its 5’-triphosphate extremity. Further, we found that HIV-1 infection triggers a VPR-dependent downregulation of RNA triphosphatase DUSP11in vitroandin vivo, leading to an increase of Y-RNA 5’-triphosphorylation that enables their immunogenicity. Importantly, we show that altering DUSP11 expression is sufficient to induce a type-I interferon and T cell activation transcriptional program associated with HIV-1 infection. Overall, our work uncovers the critical contribution of endogenous repeat RNAs ligands to antiviral immunity and demonstrates the role of this pathway in HIV-1 infection.

Proteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective – motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

DNA origami has emerged as a highly programmable method to construct customized objects and functional devices in the 10-100 nm scale. Scaling up the size of the DNA origami would enable many potential applications, which include metamaterial construction and surface-based biophysical assays. Here we demonstrate that a six-helix bundle DNA origami nanostructure in the submicrometre scale (meta-DNA) could be used as a magnified analogue of single-stranded DNA, and that two meta-DNAs that contain complementary 'meta-base pairs' can form double helices with programmed handedness and helical pitches. By mimicking the molecular behaviours of DNA strands and their assembly strategies, we used meta-DNA building blocks to form diverse and complex structures on the micrometre scale. Using meta-DNA building blocks, we constructed a series of DNA architectures on a submicrometre-to-micrometre scale, which include meta-multi-arm junctions, three-dimensional (3D) polyhedrons, and various 2D/3D lattices. We also demonstrated a hierarchical strand-displacement reaction on meta-DNA to transfer the dynamic features of DNA into the meta-DNA. This meta-DNA self-assembly concept may transform the microscopic world of structural DNA nanotechnology.

The numerical solution of the turbine bladed wheel with tie-bosses based on 3D finite element method with surface to surface dry friction contact model is proposed. The Augmented Lagrangian method was used to compute contact normal pressures and friction stresses. The friction coupling was modeled by the Isotropic Coulomb’s law. To validate the numerical results, the experimental set-up for studying dynamical behavior of the bladed wheel with pre-stressed dry-friction contacts in tie-bosses was built. Both experimental and numerical solutions of the non-linear dynamics of bladed wheel with tie-bosses are described. Comparison of experimental and numerical results of dynamical behavior and damping estimation of our bladed wheel design yielded a reasonable agreement. However, the non-linear solution due to dry friction contacts leads to high performance computations and still long computation times.

Simulations of nucleic acids at different levels of structural details are increasingly used to complement and interpret experiments in different fields, from biophysics to medicine and materials science. However, the various structural models currently available for DNA and RNA and their accompanying suites of computational tools can be very rarely used in a synergistic fashion. The tacoxDNA webserver and standalone software package presented here are a step toward a long-sought interoperability of nucleic acids models. The webserver offers a simple interface for converting various common input formats of DNA structures and setting up molecular dynamics (MD) simulations. Users can, for instance, design DNA rings with different topologies, such as knots, with and without supercoiling, by simply providing an XYZ coordinate file of the DNA centre-line. More complex DNA geometries, as designable in the cadnano, CanDo and Tiamat tools, can also be converted to all-atom or oxDNA representations, which can then be used to run MD simulations. Though the latter are currently geared toward the native and LAMMPS oxDNA representations, the open-source package is designed to be further expandable. TacoxDNA is available at http://tacoxdna.sissa.it. © 2019 Wiley Periodicals, Inc.

The parametric anti-resonance phenomenon as an active damping tool for suppression of externally excited resonant vibration is numerically studied herein. It is well known fact that the anti-resonance phenomenon, i.e. the stiffness periodic variation by subtractive, combination resonance frequency, brings stabilization and cancelling into self-excited vibrations. But this paper aims at a new possibility of its application, namely a damping of externally excited resonant vibration. For estimation of its effect we come both from a characteristic exponent of the analytical solution and numerical solution of forced vibration of 2DOF linear system with additional parametric excitation. The amplitude suppression owing to the parametric anti-resonance is studied on several parameters of the system: a depth of parametric excitation, mass ratio, damping coefficient and small frequency deviations from the parametric anti-resonance.

This paper deals with an experimental analysis of hard rubber dynamic tests under finite torsion deformations. The experimental test rig was designed and composed for torsion loading of rubber under defined amplitudes, frequencies and temperatures. The present paper brings an amplitude-temperature analysis of isoprene-butadiene rubber ascertained by the use of the test rig. The mathematical rheological model of rubber was designed for phenomenological description and parametric identification of rubber.

DNA and RNA are generally regarded as central molecules in molecular biology. Recent advancements in the field of DNA/RNA nanotechnology successfully used DNA/RNA as programmable molecules to construct molecular machines and nanostructures with predefined shapes and functions. The key mechanism for dynamic control of the conformations of these DNA/RNA nanodevices is a reaction called strand displacement, in which one strand in a formed duplex is replaced by a third invading strand. While DNA/RNA strand displacement has mainly been used to de novo design molecular devices, we argue in this review that this reaction is also likely to play a key role in multiple cellular events such as gene recombination, CRISPR-based genome editing, and RNA cotranscriptional folding. We introduce the general mechanism of strand displacement reaction, give examples of its use in the construction of molecular machines, and finally review natural processes having characteristic which suggest that strand displacement is occurring.

DNA tile-based assembly provides a promising bottom-up avenue to create designer two-dimensional (2D) and three-dimensional (3D) crystalline structures that may host guest molecules or nanoparticles to achieve novel functionalities. Herein, we introduce a new kind of DNA tiles (named layered-crossover tiles) that each consists of two or four pairs of layered crossovers to bridge DNA helices in two neighboring layers with precisely predetermined relative orientations. By providing proper matching rules for the sticky ends at the terminals, these layered-crossover tiles are able to assemble into 2D periodic lattices with precisely controlled angles ranging from 20° to 80°. The layered-crossover tile can be slightly modified and used to successfully assemble 3D lattice with dimensions of several hundred micrometers with tunable angles as well. These layered-crossover tiles significantly expand the toolbox of DNA nanotechnology to construct materials through bottom-up approaches.

Bladed wheel model with tie-boss couplings for numerical and experimental investigation of stiffening and friction damping between tie-bosses is introduced. The modal behavior of FE numerical models of the wheel for two contact limit states, i.e. open and bonded contacts, was ascertained. The experimental modal analysis of the wheel both for open and pre-stressed contacts were performed, too. For detail stiffening and damping effect investigation the physical model of three-blade-bundle was elaborated. The experiments were performed for different excitation forces, excitation frequencies and contact pre-stresses. The dynamics of the bundle with respect to different contact states was evaluated from vibration attenuation after short resonant excitation. It was observed that if the macroslips arise in contacts that eigen-frequencies of the bundle are very close to the eigen-frequencies of open contact model bundle and high damping effect is achieved. If the microslips arise in contacts the eigen-frequencies are close to eigen-frequencies of the bond contact model and low damping is achieved. Hence the stiffening effect is high only in the case of microslips. The slip transition is conditioned by the level of adhesion that must be exceeded by excitation force. The FE model of the wheel and blade triple model with dynamic frictional contacts in the tie-boss couplings were developed and calculated results are compared with experiment.