Protein cavity clustering based on community structure of pocket similarity network
International Journal of Bioinformatics Research and Applications
Computing the protein binding sites
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computation of more channels in protein molecules
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
Informatics-driven Protein-protein Docking
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Predicting Protein-Ligand Binding Site Using Support Vector Machine with Protein Properties
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. Here, we describe a new method of ligand binding site prediction called Q-SiteFinder. It uses the interaction energy between the protein and a simple van der Waals probe to locate energetically favourable binding sites. Energetically favourable probe sites are clustered according to their spatial proximity and clusters are then ranked according to the sum of interaction energies for sites within each cluster. Results: There is at least one successful prediction in the top three predicted sites in 90% of proteins tested when using Q-SiteFinder. This success rate is higher than that of a commonly used pocket detection algorithm (Pocket-Finder) which uses geometric criteria. Additionally, Q-SiteFinder is twice as effective as Pocket-Finder in generating predicted sites that map accurately onto ligand coordinates. It also generates predicted sites with the lowest average volumes of the methods examined in this study. Unlike pocket detection, the volumes of the predicted sites appear to show relatively low dependence on protein volume and are similar in volume to the ligands they contain. Restricting the size of the pocket is important for reducing the search space required for docking and de novo drug design or site comparison. The method can be applied in structural genomics studies where protein binding sites remain uncharacterized since the 86% success rate for unbound proteins appears to be only slightly lower than that of ligand-bound proteins. Availability: Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder Contact: r.m.jackson@leeds.ac.uk