PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Research Article: Exploiting three kinds of interface propensities to identify protein binding sites
Computational Biology and Chemistry
Hi-index | 3.84 |
Motivation: Due to the growing number of completely sequenced genomes, functional annotation of proteins becomes a more and more important issue. Here, we describe a method for the prediction of sites within protein domains, which are part of protein--ligand interactions. As recently demonstrated, these sites are not trivial to detect because of a varying degree of conservation of their location and type within a domain family. Results: The developed method for the prediction of protein--ligand interaction sites is based on a newly defined interaction profile hidden Markov model (ipHMM) topology that takes structural and sequence data into account. It is based on a homology search via a posterior decoding algorithm that yields probabilities for interacting sequence positions and inherits the efficiency and the power of the profile hidden Markov model (pHMM) methodology. The algorithm enhances the quality of interaction site predictions and is a suitable tool for large scale studies, which was already demonstrated for pHMMs. Availability: The MATLAB-files are available on request from the first author. Contact: tobias.mueller@biozentrum.uni-wuerzburg.de Supplementary information: http://domains.bioapps.biozentrum.uni-wuerzburg.de/