Design strategies for the self-assembly of polyhedral shells.

The control over the self-assembly of complex structures is a long-standing challenge of material science, especially at the colloidal scale, as the desired assembly pathway is often kinetically derailed by the formation of amorphous aggregates. Here, we investigate in detail the problem of the self-assembly of the three Archimedean shells with five contact points per vertex, i.e., the icosahedron, the snub cube, and the snub dodecahedron. We use patchy particles with five interaction sites (or patches) as model for the building blocks and recast the assembly problem as a Boolean satisfiability problem (SAT) for the patch-patch interactions. This allows us to find effective designs for all targets and to selectively suppress unwanted structures. By tuning the geometrical arrangement and the specific interactions of the patches, we demonstrate that lowering the symmetry of the building blocks reduces the number of competing structures, which in turn can considerably increase the yield of the target structure. These results cement SAT-assembly as an invaluable tool to solve inverse design problems.

DNA-Nanostructure-Guided Assembly of Proteins into Programmable Shapes.

The development of methods to synthesize artificial protein complexes with precisely controlled configurations will enable diverse biological and medical applications. Using DNA to link proteins provides programmability that can be difficult to achieve with other methods. Here, we use DNA origami as an "assembler" to guide the linking of protein-DNA conjugates using a series of oligonucleotide hybridization and displacement operations. We constructed several isomeric protein nanostructures, including a dimer, two types of trimer structures, and three types of tetramer assemblies, on a DNA origami platform by using a C3-symmetric building block composed of a protein trimer modified with DNA handles. Our approach expands the scope for the precise assembly of protein-based nanostructures and will enable the formulation of functional protein complexes with stoichiometric and geometric control.

Inverse Design of Self-Folding 3D Shells.

Self-folding is an emerging paradigm for the inverse design of three-dimensional structures. While most efforts have concentrated on the shape of the net, our approach introduces a new design dimension-bond specificity between the edges. We transform this design process into a Boolean satisfiability problem to derive solutions for various target structures. This method significantly enhances the yield of the folding process. Furthermore, by linearly combining independent solutions, we achieve designs for shape-shifting nets wherein the dominant structure evolves with varying external conditions. This approach is demonstrated through coarse-grained simulations on two examples of triangular and square nets capable of folding into multiple target shapes.

Designing 3D multicomponent self-assembling systems with signal-passing building blocks.

We introduce an allostery-mimetic building block model for the self-assembly of 3D structures. We represent the building blocks as patchy particles, where each binding site (patch) can be irreversibly activated or deactivated by binding of the particle's other controlling patches to another particle. We show that these allostery-mimetic systems can be designed to increase yields of target structures by disallowing misassembled states and can further decrease the smallest number of distinct species needed to assemble a target structure. Next, we show applications to design a programmable nanoparticle swarm for multifarious assembly: a system of particles that stores multiple possible target structures and a particular structure is recalled by presenting an external trigger signal. Finally, we outline a possible pathway for realization of such structures at nanoscale using DNA nanotechnology devices.

Coarse-grained modeling of DNA-RNA hybrids.

We introduce oxNA, a new model for the simulation of DNA-RNA hybrids that is based on two previously developed coarse-grained models-oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes, including their structure, persistence length, and force-extension characteristics. By parameterizing the DNA-RNA hydrogen bonding interaction, we fit the model's thermodynamic properties to experimental data using both average-sequence and sequence-dependent parameters. To demonstrate the model's applicability, we provide three examples of its use-calculating the free energy profiles of hybrid strand displacement reactions, studying the resolution of a short R-loop, and simulating RNA-scaffolded wireframe origami.

Nanobase.org: a repository for DNA and RNA nanostructures.

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/.

DNA Origami Tessellations.

Molecular tessellation research aims to elucidate the underlying principles that govern intricate patterns in nature and to leverage these principles to create precise and ordered structures across multiple scales, thereby facilitating the emergence of novel functionalities. DNA origami nanostructures are excellent building blocks for constructing tessellation patterns. However, the size and complexity of DNA origami tessellation systems are currently limited by several unexplored factors relevant to the accuracy of essential design parameters, the applicability of design strategies, and the compatibility between different tiles. Here, we present a general method for creating DNA origami tiles that grow into tessellation patterns with micrometer-scale order and nanometer-scale precision. Interhelical distance (D) was identified as a critical design parameter determining tile conformation and tessellation outcome. Finely tuned D facilitated the accurate geometric design of monomer tiles with minimized curvature and improved tessellation capability, enabling the formation of single-crystalline lattices ranging from tens to hundreds of square micrometers. The general applicability of the design method was demonstrated by 9 tile geometries, 15 unique tile designs, and 12 tessellation patterns covering Platonic, Laves, and Archimedean tilings. Particularly, we took two strategies to increase the complexity of DNA origami tessellation, including reducing the symmetry of monomer tiles and coassembling tiles of different geometries. Both yielded various tiling patterns that rivaled Platonic tilings in size and quality, indicating the robustness of the optimized tessellation system. This study will promote DNA-templated, programmable molecular and material patterning and open up new opportunities for applications in metamaterial engineering, nanoelectronics, and nanolithography.

CytoDirect: A Nucleic Acid Nanodevice for Specific and Efficient Delivery of Functional Payloads to the Cytoplasm.

Direct and efficient delivery of functional payloads such as chemotherapy drugs, siRNA, or small-molecule inhibitors into the cytoplasm, bypassing the endo/lysosomal trapping, is a challenging task for intracellular medicine. Here, we take advantage of the programmability of DNA nanotechnology to develop a DNA nanodevice called CytoDirect, which incorporates disulfide units and human epidermal growth factor receptor 2 (HER2) affibodies into a DNA origami nanostructure, enabling rapid cytosolic uptake into targeted cancer cells and deep tissue penetration. We further demonstrated that therapeutic oligonucleotides and small-molecule chemotherapy drugs can be easily delivered by CytoDirect and showed notable effects on gene knockdown and cell apoptosis, respectively. This study demonstrates the synergistic effect of disulfide and HER2 affibody modifications on the rapid cytosolic delivery of DNA origami and its payloads to targeted cells and deep tissues, thereby expanding the delivery capabilities of DNA nanostructures in a new direction for disease treatment.

Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes.

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."

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM's performance with different supervised learning approaches that include random forests and several deep neural network architectures.

Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations.

Holliday junction (HJ) is a noncanonical four-way DNA structure with a prominent role in DNA repair, recombination, and DNA nanotechnology. By rearranging its four arms, HJ can adopt either closed or open state. With enzymes typically recognizing only a single state, acquiring detailed knowledge of the rearrangement process is an important step toward fully understanding the biological function of HJs. Here, we carried out standard all-atom molecular dynamics (MD) simulations of the spontaneous opening-closing transitions, which revealed complex conformational transitions of HJs with an involvement of previously unconsidered "half-closed" intermediates. Detailed free-energy landscapes of the transitions were obtained by sophisticated enhanced sampling simulations. Because the force field overstabilizes the closed conformation of HJs, we developed a system-specific modification which for the first time allows the observation of spontaneous opening-closing HJ transitions in unbiased MD simulations and opens the possibilities for more accurate HJ computational studies of biological processes and nanomaterials.

Two-step nucleation in a binary mixture of patchy particles.

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 concentration 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.

Desmocollin-1 is associated with pro-metastatic phenotype of luminal A breast cancer cells and is modulated by parthenolide.

BACKGROUND: Desmocollin-1 (DSC1) is a desmosomal transmembrane glycoprotein that maintains cell-to-cell adhesion. DSC1 was previously associated with lymph node metastasis of luminal A breast tumors and was found to increase migration and invasion of MCF7 cells in vitro. Therefore, we focused on DSC1 role in cellular and molecular mechanisms in luminal A breast cancer and its possible therapeutic modulation. METHODS: Western blotting was used to select potential inhibitor decreasing DSC1 protein level in MCF7 cell line. Using atomic force microscopy we evaluated effect of DSC1 overexpression and modulation on cell morphology. The LC-MS/MS analysis of total proteome on Orbitrap Lumos and RNA-Seq analysis of total transcriptome on Illumina NextSeq 500 were performed to study the molecular mechanisms associated with DSC1. Pull-down analysis with LC-MS/MS detection was carried out to uncover DSC1 protein interactome in MCF7 cells. RESULTS: Analysis of DSC1 protein levels in response to selected inhibitors displays significant DSC1 downregulation (p-value ≤ 0.01) in MCF7 cells treated with NF-κB inhibitor parthenolide. Analysis of mechanic cell properties in response to DSC1 overexpression and parthenolide treatment using atomic force microscopy reveals that DSC1 overexpression reduces height of MCF7 cells and conversely, parthenolide decreases cell stiffness of MCF7 cells overexpressing DSC1. The LC-MS/MS total proteome analysis in data-independent acquisition mode shows a strong connection between DSC1 overexpression and increased levels of proteins LACRT and IGFBP5, increased expression of IGFBP5 is confirmed by RNA-Seq. Pathway analysis of proteomics data uncovers enrichment of proliferative MCM_BIOCARTA pathway including CDK2 and MCM2-7 after DSC1 overexpression. Parthenolide decreases expression of LACRT, IGFBP5 and MCM_BIOCARTA pathway specifically in DSC1 overexpressing cells. Pull-down assay identifies DSC1 interactions with cadherin family proteins including DSG2, CDH1, CDH3 and tyrosine kinase receptors HER2 and HER3; parthenolide modulates DSC1-HER3 interaction. CONCLUSIONS: Our systems biology data indicate that DSC1 is connected to mechanisms of cell cycle regulation in luminal A breast cancer cells, and can be effectively modulated by parthenolide.

OxDNA.org: a public webserver for coarse-grained simulations of DNA and RNA nanostructures.

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.

The Heterogeneous Landscape and Early Evolution of Pathogen-Associated CpG Dinucleotides in SARS-CoV-2.

COVID-19 can lead to acute respiratory syndrome, which can be due to dysregulated immune signaling. We analyze the distribution of CpG dinucleotides, a pathogen-associated molecular pattern, in the SARS-CoV-2 genome. We characterize CpG content by a CpG force that accounts for statistical constraints acting on the genome at the nucleotidic and amino acid levels. The CpG force, as the CpG content, is overall low compared with other pathogenic betacoronaviruses; however, it widely fluctuates along the genome, with a particularly low value, comparable with the circulating seasonal HKU1, in the spike coding region and a greater value, comparable with SARS and MERS, in the highly expressed nucleocapside coding region (N ORF), whose transcripts are relatively abundant in the cytoplasm of infected cells and present in the 3'UTRs of all subgenomic RNA. This dual nature of CpG content could confer to SARS-CoV-2 the ability to avoid triggering pattern recognition receptors upon entry, while eliciting a stronger response during replication. We then investigate the evolution of synonymous mutations since the outbreak of the COVID-19 pandemic, finding a signature of CpG loss in regions with a greater CpG force. Sequence motifs preceding the CpG-loss-associated loci in the N ORF match recently identified binding patterns of the zinc finger antiviral protein. Using a model of the viral gene evolution under human host pressure, we find that synonymous mutations seem driven in the SARS-CoV-2 genome, and particularly in the N ORF, by the viral codon bias, the transition-transversion bias, and the pressure to lower CpG content.

Understanding DNA interactions in crowded environments with a coarse-grained model.

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.

Design, optimization and analysis of large DNA and RNA nanostructures through interactive visualization, editing and molecular simulation.

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 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 to design and analysis of a range of DNA/RNA nanostructures.

A Self-Regulating DNA Rotaxane Linear Actuator Driven by Chemical Energy.

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.

Coarse-grained nucleic acid-protein model for hybrid nanotechnology.

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.

Unified Nanotechnology Format: One Way to Store Them All.

The domains of DNA and RNA nanotechnology are steadily gaining in popularity while proving their value with various successful results, including biosensing robots and drug delivery cages. Nowadays, the nanotechnology design pipeline usually relies on computer-based design (CAD) approaches to design and simulate the desired structure before the wet lab assembly. To aid with these tasks, various software tools exist and are often used in conjunction. However, their interoperability is hindered by a lack of a common file format that is fully descriptive of the many design paradigms. Therefore, in this paper, we propose a Unified Nanotechnology Format (UNF) designed specifically for the biomimetic nanotechnology field. UNF allows storage of both design and simulation data in a single file, including free-form and lattice-based DNA structures. By defining a logical and versatile format, we hope it will become a widely accepted and used file format for the nucleic acid nanotechnology community, facilitating the future work of researchers and software developers. Together with the format description and publicly available documentation, we provide a set of converters from existing file formats to simplify the transition. Finally, we present several use cases visualizing example structures stored in UNF, showcasing the various types of data UNF can handle.