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.

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.

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.

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.

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.

A rhythmically pulsing leaf-spring DNA-origami nanoengine that drives a passive follower.

Molecular engineering seeks to create functional entities for 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 artificial molecular motors are driven by Brownian motion, in which, with few exceptions, the generated forces are non-directed and insufficient for efficient transfer to passive second-level components. Consequently, efficient chemical-fuel-driven nanoscale driver-follower systems have not yet been realized. Here we present a DNA nanomachine (70 nm × 70 nm × 12 nm) driven by the chemical energy of DNA-templated RNA-transcription-consuming nucleoside triphosphates as fuel to generate a rhythmic pulsating motion of two rigid DNA-origami arms. Furthermore, we demonstrate actuation control and the simple coupling of the active nanomachine with a passive follower, to which it then transmits its motion, forming a true driver-follower pair.

Engineering Azeotropy to Optimize the Self-Assembly of Colloidal Mixtures.

The goal of inverse self-assembly is to design interparticle interactions capable of assembling the units into a desired target structure. The effective assembly of complex structures often requires the use of multiple components, each new component increasing the thermodynamic degrees of freedom and, hence, the complexity of the self-assembly pathway. In this work we explore the possibility to use azeotropy, i.e., a special thermodynamic condition where the system behaves effectively as a one-component system, as a way to control the self-assembly of an arbitrary number of components. Exploiting the mass-balance equations, we show how to select patchy particle systems that exhibit azeotropic points along the desired self-assembly pathway. As an example we map the phase diagram of a binary mixture that, by design, fully assembles into cubic (and only cubic) diamond crystal via an azeotropic point. The ability to explicitly include azeotropic points in artificial designs reveals effective pathways for the self-assembly of complex structures.

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.

High-affinity binding to the SARS-CoV-2 spike trimer by a nanostructured, trivalent protein-DNA synthetic antibody.

Multivalency enables nanostructures to bind molecular targets with high affinity. Although antibodies can be generated against a wide range of antigens, their shape and size cannot be tuned to match a given target. DNA nanotechnology provides an attractive approach for designing customized multivalent scaffolds due to the addressability and programmability of the nanostructure shape and size. Here, we design a nanoscale synthetic antibody ("nano-synbody") based on a three-helix bundle DNA nanostructure with one, two, or three identical arms terminating in a mini-binder protein that targets the SARS-CoV-2 spike protein. The nano-synbody was designed to match the valence and distance between the three receptor binding domains (RBDs) in the spike trimer, in order to enhance affinity. The protein-DNA nano-synbody shows tight binding to the wild-type, Delta, and several Omicron variants of the SARS-CoV-2 spike trimer, with affinity increasing as the number of arms increases from one to three. The effectiveness of the nano-synbody was also verified using a pseudovirus neutralization assay, with the three-arm nanostructure inhibiting two Omicron variants against which the structures with only one or two arms are ineffective. The structure of the three-arm nano-synbody bound to the Omicron variant spike trimer was solved by negative-stain transmission electron microscopy reconstruction, and shows the protein-DNA nanostructure with all three arms attached to the RBD domains, confirming the intended trivalent attachment. The ability to tune the size and shape of the nano-synbody, as well as its potential ability to attach two or more different binding ligands, will enable the high-affinity targeting of a range of proteins not possible with traditional antibodies.

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.

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.

The oxDNA Coarse-Grained Model as a Tool to Simulate DNA Origami.

This chapter introduces how to run molecular dynamics simulations for DNA origami using the oxDNA coarse-grained model.

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.

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.

Designing the Self-Assembly of Arbitrary Shapes Using Minimal Complexity Building Blocks.

The design space for self-assembled multicomponent objects ranges from a solution in which every building block is unique to one with the minimum number of distinct building blocks that unambiguously define the target structure. We develop a pipeline to explore the design spaces for a set of structures of various sizes and complexities. To understand the implications of the different solutions, we analyze their assembly dynamics using patchy particle simulations and study the influence of the number of distinct building blocks, and the angular and spatial tolerances on their interactions, on the kinetics and yield of the target assembly. We show that the resource-saving solution with a minimum number of distinct blocks can often assemble just as well (or faster) than designs where each building block is unique. We further use our methods to design multifarious structures, where building blocks are shared between different target structures. Finally, we use coarse-grained DNA simulations to investigate the realization of multicomponent shapes using DNA nanostructures as building blocks.

Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions.

The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.

A simple solution to the problem of self-assembling cubic diamond crystals.

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 Boolean satisfiability problem (SAT). 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.

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.

Coarse-Grained Simulations for the Characterization and Optimization of Hybrid Protein-DNA Nanostructures.

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.