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A classical Master equation approach to modeling an artificial protein motor

Kuwada, Nathan J.; Blab, Gerhard A. and Linke, Heiner LU (2010) In Chemical Physics 375(2-3). p.479-485
Abstract
Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as... (More)
Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors. (C) 2010 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial molecular motors, Master equation, Computational simulation
in
Chemical Physics
volume
375
issue
2-3
pages
479 - 485
publisher
Elsevier
external identifiers
  • wos:000282594600046
  • scopus:77957750193
ISSN
0301-0104
DOI
10.1016/j.chemphys.2010.05.009
language
English
LU publication?
yes
id
1f5380e7-ac14-475d-8529-9ae05ed8a58d (old id 1727971)
date added to LUP
2010-11-22 16:42:33
date last changed
2018-06-17 04:15:23
@article{1f5380e7-ac14-475d-8529-9ae05ed8a58d,
  abstract     = {Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors. (C) 2010 Elsevier B.V. All rights reserved.},
  author       = {Kuwada, Nathan J. and Blab, Gerhard A. and Linke, Heiner},
  issn         = {0301-0104},
  keyword      = {Artificial molecular motors,Master equation,Computational simulation},
  language     = {eng},
  number       = {2-3},
  pages        = {479--485},
  publisher    = {Elsevier},
  series       = {Chemical Physics},
  title        = {A classical Master equation approach to modeling an artificial protein motor},
  url          = {http://dx.doi.org/10.1016/j.chemphys.2010.05.009},
  volume       = {375},
  year         = {2010},
}