Linked Life Data

14507681

Source:http://linkedlifedata.com/resource/pubmed/id/14507681

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2003-9-25
pubmed:abstractText
A reaction probability is required to calculate the rate constant of a diffusion-dominated reaction. Due to the complicated geometry and potentially high dimension of the reaction probability problem, it is usually solved by a Brownian dynamics simulation, also known as a random walk or path integral method, instead of solving the equivalent partial differential equation by a discretization method. Building on earlier work, this article completes the development of a robust importance sampling algorithm for Brownian dynamics-i.e., biased Brownian dynamics with weight control-to overcome the high energy and entropy barriers in biomolecular association reactions. The biased Brownian dynamics steers sampling by a bias force, and the weight control algorithm controls sampling by a target weight. This algorithm is optimal if the bias force and the target weight are constructed from the solution of the reaction probability problem. In reality, an approximate reaction probability has to be used to construct the bias force and the target weight. Thus, the performance of the algorithm depends on the quality of the approximation. Given here is a method to calculate a good approximation, which is based on the selection of a reaction coordinate and the variational formulation of the reaction probability problem. The numerically approximated reaction probability is shown by computer experiments to give a factor-of-two speedup over the use of a purely heuristic approximation. Also, the fully developed method is compared to unbiased Brownian dynamics. The tests for human superoxide dismutase, Escherichia coli superoxide dismutase, and antisweetener antibody NC6.8, show speedups of 17, 35, and 39, respectively. The test for reactions between two model proteins with orientations shows speedups of 2578 for one set of configurations and 3341 for another set of configurations.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-10919998, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-10920003, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-1353610, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-2248953, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-7711269, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-7756548, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-7893280, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-8031131, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-8159682, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-8392365, http://linkedlifedata.com/resource/pubmed/commentcorrection/14507681-8770190
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0006-3495
pubmed:author
pubmed:issnType
Print
pubmed:volume
85
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2147-57
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Robust biased Brownian dynamics for rate constant calculation.
pubmed:affiliation
Renaissance Technologies, East Setauket, New York, USA.
pubmed:publicationType
Journal Article, Evaluation Studies, Validation Studies