Method for recognizing local descriptors of protein structures using Hidden Markov Models
Independent thesis Basic level (professional degree), 20 points / 30 hpStudent thesis
Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models.
Place, publisher, year, edition, pages
Institutionen för fysik, kemi och biologi , 2008. , 38 p.
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:liu:diva-11408ISRN: LiTH-IFM-EX-08/1922-SEOAI: oai:DiVA.org:liu-11408DiVA: diva2:17817
2008-02-22, Jordan-Fermi, Fysikhuset, Linköping, 13:15