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Sampling Conformational Pathways
Jean-Claude
Latombe, Computer Science
Doug Brutlag, Biochemistry
Vijay Pande, Chemistry
Leonidas
Guibas , Computer Science
Michael Levitt, Structural Biology
We introduce Sochastic Roadmap Simulation (SRS),
a new approach for exploring the kinetics of molecular
motion by examining multiple pathways in a graph,
called a roadmap. A roadmap is generated by sampling
the molecule’s conformational space at random
and its computation does not suffer from the local-minimum
problem encountered with other methods.
Every path in the roadmap represents a potential
pathway and is associated with a probability indicating
the likelyhood that the molecule follow this pathway.
By viewing the roadmap as a Markov chain, we efficiently
compute kinetic properties of molecular motion
over the entire energy landscape of the molecule.
We also prove that in the limit, SRS converges
to the same distribution as Monte Carlo simulation.
To test the effectiveness of our approach, we
have applied it to the computation of the transmission
coefficient for protein folding, an important
parameter that measures the “kinetic distance”
of a protein’s conformation to the folded
state. Our computational studies show that SRS
achieves several orders-of-magnitude reduction
in running time compared with a Monte Carlo method. |
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