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Register a new userModelling Molecular Motors as Folding-Unfolding Cycles
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We propose a model for motor proteins based on a hierarchical Hamiltonian that we have previously introduced to describe protein folding. The proposed motor model has high efficiency and is consistent with a linear load-velocity response. The main improvement with respect to previous models is that this description suggests a connection between folding and function of allosteric proteins.
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Molecular Docking (MD) is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound to each other. During such analysis, 3D representations of molecules are manipulated according to their degree of freedoms: rigid roto-translation and fragment rotations along the rotatable bonds. In our work, we focused on one specific phase of the molecular docking procedure i.e. Molecular Unfolding (MU), which is used to remove the initial bias of a molecule by expanding it to an unfolded shape. The objective of the MU problem is to find the configuration that maximizes the molecular area, or equivalently, that maximizes the internal distances between atoms inside the molecule. We propose a quantum annealing approach to MU by formulating it as a High-order Unconstrained Binary Optimization (HUBO) which was possible to solve on the latest D-Wave annealing hardware (2000Q and Advantage). Results and performances obtained with quantum annealers are compared with state of art classical solvers.
Molecular motors convert chemical energy into mechanical work while operating in an environment dominated by Brownian motion. The aim of this paper is to explore the flow of energy between the molecular motors and its surroundings, in particular, its efficiency. Based on the Fokker-Planck equation with either $N$ or infinite chemical states, we find that the energy efficiency of the molecular motors, whether the Stokes efficiency or the usual thermodynamic efficiency, is strictly bounded by 1, because of the dissipation of the energy in both the overdamped surroundings and in the process of the chemical reaction.
Molecular motors transduce chemical energy obtained from hydrolizing ATP into mechanical work exerted against an external force. We calculate their efficiency at maximum power output for two simple generic models and show that the qualitative behaviour depends crucially on the position of the transition state. Specifically, we find a transition state near the initial state (sometimes characterized as a power stroke) to be most favorable with respect to both high power output and high efficiency at maximum power. In this regime, driving the motor further out of equilibrium by applying higher chemical potential differences can even, counter-intuitively, increase the efficiency.
We study the dynamic properties of a thermal autonomous machine made up of two quantum Brownian particles, each of which is in contact with an environment at different temperature and moves on a periodic sinusoidal track. When such tracks are shifted, the center of mass of the system exhibits a non-vanishing velocity, for which we provide an exact expression in the limit of small track undulations. We discuss the role of the broken spatial symmetry in the emergence of directed motion in thermal machines. We then consider the case in which external deterministic forces are applied to the system, and characterize its steady state velocity. If the applied external force opposes the system motion, work can be extracted from such a steady state thermal machine, without any external cyclic protocol. When the two particles are not interacting, our results reduce to those of refs. [1,2] for a single particle moving in a periodic tilted potential. We finally use our results for the motor velocity to check the validity of the quantum molecular dynamics algorithm in the non--linear, non--equilibrium regime.
Experimental approaches have been applied to address questions in understanding three-dimensional chromatin organisation and function. As datasets increase in size and complexity, it becomes a challenge to reach a mechanistic interpretation of experimental results. Polymer simulations and mechanistic modelling have been applied to explain experimental observations, and the links to different aspects of genome function. Here, we provide a guide for biologists, explaining different simulation approaches and the contexts in which they have been used.
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