Modelling interaction sites in protein domains with interaction profile hidden Markov models
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, th...
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| Veröffentlicht in: | Bioinformatics 2006-12, Vol.22 (23), p.2851-2857 |
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| creator | Friedrich, Torben Pils, Birgit Dandekar, Thomas Schultz, Jörg Müller, Tobias |
| description | 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: |
| doi_str_mv | 10.1093/bioinformatics/btl486 |
| format | Article |
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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:</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btl486</identifier><identifier>PMID: 17000753</identifier><identifier>CODEN: BOINFP</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Amino Acid Sequence ; Artificial Intelligence ; Binding Sites ; Biological and medical sciences ; Fundamental and applied biological sciences. Psychology ; General aspects ; Markov Chains ; Mathematics in biology. Statistical analysis. Models. Metrology. 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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. 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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. 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| subjects | Algorithms Amino Acid Sequence Artificial Intelligence Binding Sites Biological and medical sciences Fundamental and applied biological sciences. Psychology General aspects Markov Chains Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Molecular Sequence Data Pattern Recognition, Automated - methods Protein Binding Protein Interaction Mapping - methods Protein Structure, Tertiary Sequence Alignment - methods Sequence Analysis, Protein - methods |
| title | Modelling interaction sites in protein domains with interaction profile hidden Markov models |
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