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Bioinformatics Advance Access originally published online on March 31, 2005
Bioinformatics 2005 21(12):2839-2843; doi:10.1093/bioinformatics/bti421
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Density guided importance sampling: application to a reduced model of protein folding

Geraint L. Thomas 1, Richard B. Sessions 2 and Martin J. Parker 1,*

1Astbury Centre for Structural Molecular Biology, Department of Biochemistry and Microbiology, University of Leeds Leeds LS2 9JT, UK
2Department of Biochemistry, University of Bristol, School of Medical Sciences, University Walk Bristol BS8 1TD, UK

*To whom correspondence should be addressed.

Motivation: Monte Carlo methods are the most effective means of exploring the energy landscapes of protein folding. The rugged topography of folding energy landscapes causes sampling inefficiencies however, particularly at low, physiological temperatures.

Results: A hybrid Monte Carlo method, termed density guided importance sampling (DGIS), is presented that overcomes these sampling inefficiencies. The method is shown to be highly accurate and efficient in determining Boltzmann weighted structural metrics of a discrete off-lattice protein model. In comparison to the Metropolis Monte Carlo method, and the hybrid Monte Carlo methods, jump-walking, smart-walking and replica-exchange, the DGIS method is shown to be more efficient, requiring no parameter optimization. The method guides the simulation towards under-sampled regions of the energy spectrum and recognizes when equilibrium has been reached, avoiding arbitrary and excessively long simulation times.

Availability: Fortran code available from authors upon request.

Contact: m.j.parker{at}leeds.ac.uk


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