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Nucleic Acids Research 2005 33(6):1874-1891; doi:10.1093/nar/gki327
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Published online 1 April 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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{at}oupjournals.org

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Practical lessons from protein structure prediction

Krzysztof Ginalski1,2,3, Nick V. Grishin3,4, Adam Godzik5 and Leszek Rychlewski1,*

1BioInfoBank Institute ul. Limanowskiego 24A, 60-744 Poznan, Poland 2Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University Pawinskiego 5a, 02-106 Warsaw, Poland 3Department of Biochemistry, University of Texas, Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9038, USA 4Howard Hughes Medical Institute, University of Texas, Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA 5The Burnham Institute 10901 N. Torrey Pines Road, La Jolla, CA 92037, USA

*To whom correspondence should be addressed. Tel: +48 604 628805; Fax: +48 61 8643350; Email: leszek{at}bioinfo.pl

Received December 20, 2004. Revised March 8, 2005. Accepted March 8, 2005.

Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed.


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