A non-tight chemomechanical coupling model for force-dependence of movement dynamics of molecular motors
Literature Information
Hong Chen
Based on the available experimental evidence, we present a simple and general model to describe the movement dynamics of molecular motors that can move processively on their linear tracks by using the chemical energy derived from ATP hydrolysis. An important aspect of the model is the non-tight coupling between the ATP hydrolysis and mechanical stepping, in contrast to the prevailing models presented in the literature that assume the tight chemomechanical coupling. With kinesin as an example, based on the current model, we study in detail its movement dynamics under a backward load, reproducing well the diverse available single-molecule experimental data such as the forward to backward step ratio, velocity, dwell time, randomness, run length, etc., versus the load. Moreover, predicted results are provided on the force-dependence of the mean number of ATP molecules consumed per mechanical step. Additionally, the theoretical data for the dynamics of myosin-V obtained based on the model are also in good agreement with the available experimental data.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










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