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Bioinformatics 2006 22(14):e174-e183; doi:10.1093/bioinformatics/btl220
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design

Ivelin Georgiev 1, Ryan H. Lilien 1,2,3 and Bruce R. Donald 1,3,4,5,*

1 Dartmouth Computer Science Department Hanover, NH 03755, USA
2 Dartmouth Medical School Hanover, NH 03755, USA
3 Dartmouth Center for Structural Biology and Computational Chemistry Hanover, NH 03755, USA
4 Dartmouth Department of Chemistry Hanover, NH 03755, USA
5 Dartmouth Department of Biological Sciences Hanover, NH 03755, USA

*To whom correspondence should be addressed.

Motivation: Structure-based protein redesign can help engineer proteins with desired novel function. Improving computational efficiency while still maintaining the accuracy of the design predictions has been a major goal for protein design algorithms. The combinatorial nature of protein design results both from allowing residue mutations and from the incorporation of protein side-chain flexibility. Under the assumption that a single conformation can model protein folding and binding, the goal of many algorithms is the identification of the Global Minimum Energy Conformation (GMEC). A dominant theorem for the identification of the GMEC is Dead-End Elimination (DEE). DEE-based algorithms have proven capable of eliminating the majority of candidate conformations, while guaranteeing that only rotamers not belonging to the GMEC are pruned. However, when the protein design process incorporates rotameric energy minimization, DEE is no longer provably-accurate. Hence, with energy minimization, the minimized-DEE (MinDEE) criterion must be used instead.

Results: In this paper, we present provably-accurate improvements to both the DEE and MinDEE criteria. We show that our novel enhancements result in a speedup of up to a factor of more than 1000 when applied in redesign for three different proteins: Gramicidin Synthetase A, plastocyanin, and protein G.

Availability: Contact authors for source code.

Contact: Bruce.R.Donald{at}dartmouth.edu


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