Search for: All records

Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

 

Checkerboard lattices—where the resulting structure is open, porous, and highly symmetric—are difficult to create by self-assembly. Synthetic systems that adopt such structures typically rely on shape complementarity and site-specific chemical interactions that are only available to biomolecular systems (e.g., protein, DNA). Here we show the assembly of checkerboard lattices from colloidal nanocrystals that harness the effects of multiple, coupled physical forces at disparate length scales (interfacial, interparticle, and intermolecular) and that do not rely on chemical binding. Colloidal Ag nanocubes were bi-functionalized with mixtures of hydrophilic and hydrophobic surface ligands and subsequently assembled at an air–water interface. Using feedback between molecular dynamics simulations and interfacial assembly experiments, we achieve a periodic checkerboard mesostructure that represents a tiny fraction of the phase space associated with the polymer-grafted nanocrystals used in these experiments. In a broader context, this work expands our knowledge of non-specific nanocrystal interactions and presents a computation-guided strategy for designing self-assembling materials.

 

Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

 

Recent advances in structural DNA nanotechnology have been facilitated by design tools that continue to push the limits of structural complexity while simplifying an often-tedious design process. We recently introduced the software MagicDNA, which enables design of complex 3D DNA assemblies with many components; however, the design of structures with free-form features like vertices or curvature still required iterative design guided by simulation feedback and user intuition. Here, we present an updated design tool, MagicDNA 2.0, that automates the design of free-form 3D geometries, leveraging design models informed by coarse-grained molecular dynamics simulations. Our GUI-based, stepwise design approach integrates a high level of automation with versatile control over assembly and subcomponent design parameters. We experimentally validated this approach by fabricating a range of DNA origami assemblies with complex free-form geometries, including a 3D Nozzle, G-clef, and Hilbert and Trifolium curves, confirming excellent agreement between design input, simulation, and structure formation.

 

Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization. However, AD systems have been restricted to the subset of programs that have a continuous dependence on parameters. Programs that have discrete stochastic behaviors governed by distribution parameters, such as flipping a coin with probability p of being heads, pose a challenge to these systems because the connection between the result (heads vs tails) and the parameters ( p ) is fundamentally discrete. In this paper we develop a new reparameterization-based methodology that allows for generating programs whose expectation is the derivative of the expectation of the original program. We showcase how this method gives an unbiased and low-variance estimator which is as automated as traditional AD mechanisms. We demonstrate unbiased forward-mode AD of discrete-time Markov chains, agent-based models such as Conway's Game of Life, and unbiased reverse-mode AD of a particle filter. Our code package is available at https://github.com/gaurav-arya/StochasticAD.jl. 

In this work, we describe the development of a computational model for arrays of rotary DNA origami elements which can self-organize on a large scale and explore the interesting morphologies and order–disorder transition behavior of these systems.

 

Binary nanoparticle (NP) superlattices exhibit distinct collective plasmonic, magnetic, optical, and electronic properties. Here, we computationally demonstrate how fluid-fluid interfaces could be used to self-assemble binary systems of NPs into 2D superlattices when the NP species exhibit different miscibility with the fluids forming the interface. We develop a basin-hopping Monte Carlo (BHMC) algorithm tailored for interface-trapped structures to rapidly determine the ground-state configuration of NPs, allowing us to explore the repertoire of binary NP architectures formed at the interface. By varying the NP size ratio, interparticle interaction strength, and difference in NP miscibility with the two fluids, we demonstrate the assembly of an array of exquisite 2D periodic architectures, including AB-, AB₂-, and AB₃-type monolayer superlattices as well as AB-, AB₂-, A₃B₅-, and A₄B₆-type bilayer superlattices. Our results suggest that the interfacial assembly approach could be a versatile platform for fabricating 2D colloidal superlattices with tunable structure and properties.

 

The bacterial FtsK motor harvests energy from ATP to translocate double-stranded DNA during cell division. Here, we probe the molecular mechanisms underlying coordinated DNA translocation in FtsK by performing long timescale simulations of its hexameric assembly and individual subunits. From these simulations we predict signaling pathways that connect the ATPase active site to DNA-gripping residues, which allows the motor to coordinate its translocation activity with its ATPase activity. Additionally, we utilize well-tempered metadynamics simulations to compute free-energy landscapes that elucidate the extended-to-compact transition involved in force generation. We show that nucleotide binding promotes a compact conformation of a motor subunit, whereas the apo subunit is flexible. Together, our results support a mechanism whereby each ATP-bound subunit of the motor conforms to the helical pitch of DNA, and ATP hydrolysis/product release causes a subunit to lose grip of DNA. By ordinally engaging and disengaging with DNA, the FtsK motor unidirectionally translocates DNA.

 

Faceted nanoparticles can be used as building blocks to assemble nanomaterials with exceptional optical and catalytic properties. Recent studies have shown that surface functionalization of such nanoparticles with organic molecules, polymer chains, or DNA can be used to control the separation distance and orientation of particles within their assemblies. In this study, we computationally investigate the mechanism of assembly of nanocubes grafted with short-chain molecules. Our approach involves computing the interaction free energy landscape of a pair of such nanocubes via Monte Carlo simulations and using the Dijkstra algorithm to determine the minimum free energy pathway connecting key states in the landscape. We find that the assembly pathway of nanocubes is very rugged involving multiple energy barriers and metastable states. Analysis of nanocube configurations along the pathway reveals that the assembly mechanism is dominated by sliding motion of nanocubes relative to each other punctuated by their local dissociation at grafting points involving lineal separation and rolling motions. The height of energy barriers between metastable states depends on factors such as the interaction strength and surface roughness of the nanocubes and the steric repulsion from the grafts. These results imply that the observed assembly configuration of nanocubes depends not only on their globally stable minimum free energy state but also on the assembly pathway leading to this state. The free energy landscapes and assembly pathways presented in this study along with the proposed guidelines for engineering such pathways should be useful to researchers aiming to achieve uniform nanostructures from self-assembly of faceted nanoparticles.