Skip Navigation



Bioinformatics Advance Access published online on March 31, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti421
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/12/2839    most recent
bti421v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Thomas, G. L.
Right arrow Articles by Parker, M. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Thomas, G. L.
Right arrow Articles by Parker, M. J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received September 3, 2004
Revised March 21, 2005
Accepted March 30, 2005

Article

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*

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

* To whom correspondence should be addressed.
Martin J. Parker, E-mail: m.j.parker{at}leeds.ac.uk


  Abstract

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 optimisation. The method guides the simulation towards under sampled regions of the energy spectrum and recognises when equilibrium has been reached, avoiding arbitrary and excessively long simulation times.

Availability: Fortran code available from authors upon request.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.