Campus Event Calendar
Event Entry
What and Who
Stochastic Roadmap Simulation: An efficient representation and algorithm for analyzing molecular motion
Serkan Apaydin
Stanford University
Talk
AG 1, AG 2, AG 3, AG 4, AG 5
MPI Audience
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Date, Time and Location
Thursday, 22 July 2004
10:00
-- Not specified --
46.1 - MPII
021
Saarbrücken
Abstract
Classic molecular simulation techniques, such as molecular dynamics
(MD) or Monte Carlo (MC) simulation, generate individual motion
pathways and spend most of their time escaping the local minima of
the energy landscape defined over a molecular conformation space.
Their high computational cost prevents them from being used to
compute ensemble properties, i.e., properties requiring the analysis
of many motion pathways. In this talk, we introduce Stochastic
Roadmap Simulation (SRS) as a new computational framework for
exploring the kinetics of molecular motion by simultaneously
examining large sets of pathways. These pathways are compactly
encoded in a graph, which is constructed by sampling a molecular
conformation space at random. Each arc in the graph represents a
potential transition of the molecule and is associated with a probability
indicating the likelihood of this transition. By viewing the graph as a
Markov chain, ensemble properties can be efficiently computed. This
computation, which does not trace any particular pathway explicitly,
circumvents the local minima problem. Furthermore, SRS converges
to the same stationary distribution as MC simulation.
SRS is applied to two biological problems: computing the probability
of folding (pfold), an important parameter that measures the
``kinetic distance'' of a protein's conformation from its native
state with respect to its unfolded state; and estimating the
expected time to escape of a ligand from a protein binding site.
Comparison with MC simulations on protein folding shows that SRS
produces accurate results, while reducing computation time by
several orders of magnitude. This enables the use of pfold, instead
of a previous technique, to predict folding rates and phi values
for five proteins, improving the correlation with experimental data.
Similar to pfold, the escape time from a binding site would
probably be impractical to compute with MD or MC simulations. Here,
SRS makes it possible to qualitatively analyze the role of amino
acids in the catalytic site of an enzyme by computational
mutagenesis and to distinguish the catalytic site from other
potential binding sites for several ligand-protein complexes. These
applications establish SRS as a new approach to efficiently and
accurately compute ensemble properties of molecular motion.
(MD) or Monte Carlo (MC) simulation, generate individual motion
pathways and spend most of their time escaping the local minima of
the energy landscape defined over a molecular conformation space.
Their high computational cost prevents them from being used to
compute ensemble properties, i.e., properties requiring the analysis
of many motion pathways. In this talk, we introduce Stochastic
Roadmap Simulation (SRS) as a new computational framework for
exploring the kinetics of molecular motion by simultaneously
examining large sets of pathways. These pathways are compactly
encoded in a graph, which is constructed by sampling a molecular
conformation space at random. Each arc in the graph represents a
potential transition of the molecule and is associated with a probability
indicating the likelihood of this transition. By viewing the graph as a
Markov chain, ensemble properties can be efficiently computed. This
computation, which does not trace any particular pathway explicitly,
circumvents the local minima problem. Furthermore, SRS converges
to the same stationary distribution as MC simulation.
SRS is applied to two biological problems: computing the probability
of folding (pfold), an important parameter that measures the
``kinetic distance'' of a protein's conformation from its native
state with respect to its unfolded state; and estimating the
expected time to escape of a ligand from a protein binding site.
Comparison with MC simulations on protein folding shows that SRS
produces accurate results, while reducing computation time by
several orders of magnitude. This enables the use of pfold, instead
of a previous technique, to predict folding rates and phi values
for five proteins, improving the correlation with experimental data.
Similar to pfold, the escape time from a binding site would
probably be impractical to compute with MD or MC simulations. Here,
SRS makes it possible to qualitatively analyze the role of amino
acids in the catalytic site of an enzyme by computational
mutagenesis and to distinguish the catalytic site from other
potential binding sites for several ligand-protein complexes. These
applications establish SRS as a new approach to efficiently and
accurately compute ensemble properties of molecular motion.
Contact
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Ruth Schneppen-Christmann, 07/21/2004 14:08 -- Created document.