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

TitleA threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation
Publication TypeJournal Article
Year of Publication2008
AuthorsBrylinski M, Skolnick J
JournalProc Natl Acad Sci USA
Volume105
Issue1
Pagination129-34
Date Published2008 Jan 8
ISSN1091-6490
KeywordsAlgorithms, Binding Sites, Biophysics, Computational Biology, Crystallography, X-Ray, Ligands, Models, Molecular, Models, Statistical, Molecular Conformation, Protein Binding, Protein Conformation, Protein Interaction Mapping, Proteins, Reproducibility of Results, Software
Abstract

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 A 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 A. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site <2 A. 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.

Alternate JournalProceedings of the National Academy of Sciences of the United States of America
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