Simulation Studies of a TRI-PEDAL, Protein-Based Artificial Molecular Motor

Abstract

Though the biological function of many natural molecular motors is fairly well established, many structure-function details responsible for motor performance remain vague or unknown completely. Recently, we have undertaken a new bottom-up approach to understanding biological molecular motors by designing and building an artificial, protein-based molecular motor dubbed the Tumbleweed (TW). The TW is a purely diffusive motor construct consisting of three DNA-binding proteins attached to a designed, protein-based central hub, where directional stepping along a DNA track is maintained by a temporally periodic external chemical supply. To better understand important design and performance characteristics of the TW, coarse-grained Langevin Dynamics (LD) simulations and numerical solutions to the Master Equation (ME) were carried out. The LD approach, which is a single motor simulation, is particularly suitable for exploring the diffusional behavior of the system, where the ME approach, which models an ensemble of motor states, is best suited for statistically exploring the parameter space of the system and the interaction of processes at different time scales. We present results from these two theoretical approaches that illuminate not only important design and experimental considerations, such as motor geometry and track spacing, but also produce unexpected diffusional behavior. Of particular interest is that the addition of certain internal symmetric potentials can increase motor performance. For example, the addition of a non-specific binding potential, symmetric about the DNA track, can double motor speed by replacing some of the 3D diffusional search by a relatively fast 1D diffusional slide along the DNA. This, and other symmetric potential inputs that increase motor performance by subtly amplifying asymmetries in the system, are not only fundamentally interesting but also may be applicable to any molecular motor that incorporates a diffusional search in its stepping cycle.