%0 Journal Article %1 pmid17000753 %A Friedrich, T. %A Pils, B. %A Dandekar, T. %A Schultz, J. %A Muller, T. %D 2006 %J Bioinformatics %K imported myown protein_evolution %P 2851--2857 %T Modelling interaction sites in protein domains with interaction profile hidden Markov models %V 22 %X 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.

@article{pmid17000753, 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.}, added-at = {2011-07-21T16:10:59.000+0200}, author = {Friedrich, T. and Pils, B. and Dandekar, T. and Schultz, J. and Muller, T.}, biburl = {https://www.bibsonomy.org/bibtex/23e91b074f2905043a661f5af7d27677b/jschultz}, interhash = {4b44d956c90b532b358e2676ef4f009b}, intrahash = {3e91b074f2905043a661f5af7d27677b}, journal = {Bioinformatics}, keywords = {imported myown protein_evolution}, month = dec, pages = {2851--2857}, timestamp = {2017-06-09T10:28:58.000+0200}, title = {{{M}odelling interaction sites in protein domains with interaction profile hidden {M}arkov models}}, volume = 22, year = 2006 }