Paper Info
Title
Modelling interaction sites in protein domains with interaction profile hidden Markov models.
Abstract
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.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.The MATLAB-files are available on request from the first author.
Year
DOI
Venue
2006
10.1093/bioinformatics/btl486
Bioinformatics
Keywords
Field
DocType
ligand interaction,interacting sequence position,developed method,interaction site prediction,contact: tobias.mueller@biozentrum.uni-wuerzburg.de supplementary information: http://domains.bioapps.biozentrum.uni- wuerzburg.de/,protein domain,posterior decoding algorithm,markov model,ligand interaction site,interaction profile,de supplementary information,hidden markov model,protein domains
Data mining,Annotation,Protein domain,Computer science,Data sequences,Bioinformatics,Decoding methods,Hidden Markov model
Journal
Volume
Issue
ISSN
22
23
1367-4811
Citations 
PageRank 
References 
15
0.85
14
Authors
5
Name
Order
Citations
PageRank
Torben Friedrich1150.85
Birgit Pils212010.77
Thomas Dandekar341123.17
Jörg Schultz4347174.67
Tobias Müller521415.95