Dynamic properties of molecular motors in the divided-pathway model

Literature Information

Publication Date 2009-04-09
DOI 10.1039/B901214A
Impact Factor 3.676
Authors

Rahul Kumar Das, Anatoly B. Kolomeisky


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Abstract

The mechanisms of molecular motors transport are important for understanding multiple biological processes. Recent single-molecule experiments indicate that motor proteins myosin V moves along protein filaments via a complex biochemical pathway that consists of sequentially coupled linear and parallel two-chain segments. We investigate analytically the corresponding discrete-state stochastic divided-pathway model for molecular motors transport. Explicit expressions are obtained for velocities and dispersions. The dynamic properties of motor proteins in the divided-pathway model are compared with those in single-chain linear and parallel-pathway stochastic models. It is argued that modifying biochemical pathways has a strong effect on the dynamic properties, and it allows motor proteins to be more flexible in performing their biological functions.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

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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