Dataset Information
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation.
Ontology highlight
ABSTRACT: Background
Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed.Results
We present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcsc performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively.Conclusion
The use of the Connolly surface leads to slight improvements, the prediction re-ranking by conservation to significant improvements of the binding site predictions. A web server for LIGSITEcsc and its source code is available at scoppi.biotec.tu-dresden.de/pocket
SUBMITTER: Huang B
PROVIDER: S-EPMC1601958 | biostudies-literature | 2006 Sep
REPOSITORIES: biostudies-literature
Publications
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation.
BMC structural biology 20060924
<h4>Background</h4>Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed.<h4>Results</h4>We present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface events and the degree of conservation of the involved surfac ...[more]