Using Evolutionary Trees in Protein Secondary Structure Prediction and Other Comparative Sequence Analyses |
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| Institution: | 1. Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Klinik und Hochschulambulanz für Neurologie und Centrum für Schlaganfallforschung Berlin (CSB), 10117 Berlin, Germany;2. Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Institut für Pharmakologie, Center for Cardiovascular Research (CCR), 10115 Berlin, Germany;3. DZHK (German Center for Cardiovascular Research), partner site Berlin, 10115 Berlin, Germany;4. Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Center for Cardiovascular Research/CCR, Institut für Laboratoriumsmedizin, Klinische Chemie und Pathobiochemie, 10115 Berlin, Germany;5. Institut für Tierschutz, Tierverhalten und Versuchstierkunde, Freie Universität Berlin, 14163 Berlin, Germany;6. Institut für Pharmakologie, Universitätsklinikum Bonn, 53113 Bonn, Germany;7. Institut für Biologische Psychologie, Technische Universität Dresden, 01062 Dresden, Germany;8. Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Klinik für Psychiatrie und Psychotherapie, 10117 Berlin, Germany;9. Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 10117 Berlin, Germany;10. Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universitätsmedizin Rostock, 18147 Rostock, Germany |
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| Abstract: | Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments. A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method. |
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