Treecode-based generalized Born method

dc.contributor.author

Xu, Zhenli

dc.contributor.author

Cheng, Xiaolin

dc.contributor.author

Yang, Haizhao

dc.date.accessioned

2016-02-28T03:46:31Z

dc.date.issued

2011

dc.description.abstract

We have developed a treecode-based O(Nlog N) algorithm for the generalized Born (GB) implicit solvation model. Our treecode-based GB (tGB) is based on the GBr6 [J. Phys. Chem. B 111, 3055 (2007)], an analytical GB method with a pairwise descreening approximation for the R6 volume integral expression. The algorithm is composed of a cutoff scheme for the effective Born radii calculation, and a treecode implementation of the GB charge–charge pair interactions. Test results demonstrate that the tGB algorithm can reproduce the vdW surface based Poisson solvation energy with an average relative error less than 0.6% while providing an almost linear-scaling calculation for a representative set of 25 proteins with different sizes (from 2815 atoms to 65456 atoms). For a typical system of 10k atoms, the tGB calculation is three times faster than the direct summation as implemented in the original GBr6 model. Thus, our tGB method provides an efficient way for performing implicit solvent GB simulations of larger biomolecular systems at longer time scales.

dc.identifier.uri

https://hdl.handle.net/10161/11657

dc.publisher

AIP Publishing

dc.relation.ispartof

The Journal of Chemical Physics

dc.relation.isversionof

10.1063/1.3552945

dc.title

Treecode-based generalized Born method

dc.type

Journal article

pubs.begin-page

064107

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citeulike-article-id: 8794651 citeulike-linkout-0: http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JCPSA6000134000006064107000001&idtype=cvips&gifs=yes citeulike-linkout-1: http://link.aip.org/link/?JCP/134/064107 citeulike-linkout-2: http://dx.doi.org/10.1063/1.3552945 keywords: gb posted-at: 2011-02-11 14:42:11 priority: 2

pubs.organisational-group

Duke

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Mathematics

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Trinity College of Arts & Sciences

pubs.publisher-url

http://dx.doi.org/10.1063/1.3552945

pubs.volume

134

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