Effect of polyvalency on tethered molecular walkers on independent one-dimensional tracks

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Adhesive Dynamics Simulations of Highly Polyvalent DNA Motors

Molecular motors, such as myosin and kinesin, perform diverse tasks ranging from vesical transport to bulk muscle contraction. Synthetic molecular motors may eventually be harnessed to perform similar tasks in versatile synthetic systems. The most promising type of synthetic molecular motor, the DNA walker, can undergo processive motion but generally exhibits low speeds and virtually no capacity for force generation. However, we recently showed that highly polyvalent DNA motors (HPDMs) can rival biological motors by translocating at micrometer per minute speeds and generating 100+ pN of force. Accordingly, DNA nanotechnology-based designs may hold promise for the creation of synthetic, force-generating nanomotors. However, the dependencies of HPDM speed and force on tunable design parameters are poorly understood and difficult to characterize experimentally. To overcome this challenge, we present RoloSim, an adhesive dynamics software package for fine-grained simulations of HPDM translocation. RoloSim uses biophysical models for DNA duplex formation and dissociation kinetics to explicitly model tens of thousands of molecular scale interactions. These molecular interactions are then used to calculate the nano- and microscale motions of the motor. We use RoloSim to uncover how motor force and speed scale with several tunable motor properties such as motor size and DNA duplex length. Our results support our previously defined hypothesis that force scales linearly with polyvalency. We also demonstrate that HPDMs can be steered with external force, and we provide design parameters for novel HPDM-based molecular sensor and nanomachine designs.

Burnt bridge ratchet motor force scales linearly with polyvalency: a computational study

Nano- and micro-scale burnt bridge ratchet motors, which translocate via "guide" molecules that bind to and degrade a field of "fuel" molecules, have recently emerged in several biological and engineering contexts. The capacity of these motors to generate mechanical forces remains an open question. Here, chemomechanical modeling suggests that BBR force scales linearly with the steady-state number of guide-fuel bonds.

Substrate stiffness tunes the dynamics of polyvalent rolling motors

Nature has evolved many mechanisms for achieving directed motion on the subcellular level. The burnt-bridges ratchet (BBR) is one mechanism used to achieve superdiffusive molecular motion over long distances through the successive cleavage of surface-bound energy-rich substrate sites. This mechanism has been associated with both nanoscale and microscale movement, with the latter accomplished through polyvalent interactions between a large hub (e.g. influenza virus) and substrate (e.g. cell surface receptors). Experimental successes in achieving superdiffusive motion by synthetic polyvalent BBRs have raised questions about the dynamics of their motility, including whether rolling or translation is better able to direct motion of microscale spherical hubs. Here we simulate the three-dimensional dynamics of a polyvalent sphere moving on and cleaving an elastic substrate. We find that substrate stiffness plays an important role in controlling both the motor's mode of motility and its directional persistence. As we tune lateral substrate stiffness from soft to stiff we find there exists an intermediate value that optimizes rolling behaviour. We also find that there is an optimal substrate stiffness for maximizing persistence length, while stiffness does not influence as strongly the superdiffusive dynamics of the particle. Lastly, we examine the effect of substrate density, and show that softer landscapes are better able to buffer against decreases in substrate occupancy, with the spherical motor maintaining superdiffusive motion more on softer landscapes than on stiff landscapes as occupancy drops. Our results highlight the importance of surface in controlling the motion of polyvalent BBRs.