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Nucleic Acids Research Advance Access published online on October 30, 2009

Nucleic Acids Research, doi:10.1093/nar/gkp900
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Database Issue

fPOP: footprinting functional pockets of proteins by comparative spatial patterns

Yan Yuan Tseng1,*, Z. Jeffrey Chen2 and Wen-Hsiung Li1

1Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637 and 2Center for Computational Biology and Bioinformatics, University of Texas at Austin, One University Station, C4500, Austin, TX 78712, USA

*To whom correspondence should be addressed. Tel: +1 773 834 3965; Fax: +1 773 702 9740; Email: ytseng3{at}uchicago.edu

Received August 11, 2009. Revised September 21, 2009. Accepted October 6, 2009.

fPOP (footprinting Pockets Of Proteins, http://pocket.uchicago.edu/fpop/) is a relational database of the protein functional surfaces identified by analyzing the shapes of binding sites in ~42 700 structures, including both holo and apo forms. We previously used a purely geometric method to extract the spatial patterns of functional surfaces (split pockets) in ~19 000 bound structures and constructed a database, SplitPocket (http://pocket.uchicago.edu/). These functional surfaces are now used as spatial templates to predict the binding surfaces of unbound structures. To conduct a shape comparison, we use the Smith–Waterman algorithm to footprint an unbound pocket fragment with those of the functional surfaces in SplitPocket. The pairwise alignment of the unbound and bound pocket fragments is used to evaluate the local structural similarity via geometric matching. The final results of our large-scale computation, including ~90 000 identified or predicted functional surfaces, are stored in fPOP. This database provides an easily accessible resource for studying functional surfaces, assessing conformational changes between bound and unbound forms and analyzing functional divergence. Moreover, it may facilitate the exploration of the physicochemical textures of molecules and the inference of protein function. Finally, our approach provides a framework for classification of proteins into families on the basis of their functional surfaces.


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