threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocket-detection methods are limited to experimentally solved structures. Here, FINDSITE, a method for lig...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 105; no. 1; pp. 129 - 134
Main Authors Brylinski, Michal, Skolnick, Jeffrey
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 08.01.2008
National Acad Sciences
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Summary:The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocket-detection methods are limited to experimentally solved structures. Here, FINDSITE, a method for ligand-binding site prediction and functional annotation based on binding-site similarity across groups of weakly homologous template structures identified from threading, is described. For crystal structures, considering a cutoff distance of 4 Å as the hit criterion, the success rate is 70.9% for identifying the best of top five predicted ligand-binding sites with a ranking accuracy of 76.0%. Both high prediction accuracy and ability to correctly rank identified binding sites are sustained when approximate protein models (<35% sequence identity to the closest template structure) are used, showing a 67.3% success rate with 75.5% ranking accuracy. In practice, FINDSITE tolerates structural inaccuracies in protein models up to a rmsd from the crystal structure of 8-10 Å. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site <2 Å. Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model.
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Edited by Harold A. Scheraga, Cornell University, Ithaca, NY, and approved November 19, 2007
Author contributions: J.S. designed research; M.B. performed research; M.B. analyzed data; and M.B. and J.S. wrote the paper.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0707684105