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Using N-terminal targeting sequences, amino acid composition, and sequence motifs for predicting protein subcellular localization

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dc.contributor.authorHöglund, Annette
dc.contributor.authorDönnes, Pierre
dc.contributor.authorBlum, Torsten
dc.contributor.authorAdolph, Hans-Werner
dc.contributor.authorKohlbacher, Oliver
dc.contributor.editorTorda, Andrew
dc.contributor.editorKurtz, Stefan
dc.contributor.editorRarey, Matthias
dc.date.accessioned2019-08-27T08:22:38Z
dc.date.available2019-08-27T08:22:38Z
dc.date.issued2005
dc.description.abstractFunctional annotation of unknown proteins is a major goal in proteomics. A key step in this annotation process is the definition of a protein's subcellular localization. As a consequence, numerous prediction techniques for localization have been developed over the years. These methods typically focus on a single underlying biological aspect or predict a subset of all possible subcellular localizations. There is a clear need for new methods that utilize and represent available protein specific biological knowledge from several sources, in order to improve accuracy and localization coverage for a wide range of organisms. Here we present a novel Support Vector Machine (SVM)-based approach for predicting protein subcellular localization, which integrates information about N-terminal targeting sequences, amino acid composition, and protein sequence motifs. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information. Our novel approach has been used to develop two new prediction methods, TargetLoc and MultiLoc. TargetLoc is restricted to analysis of proteins containing N-terminal targeting sequences, whereas MultiLoc covers all major eukaryotic subcellular localizations for animal, plant, and fungal proteins. Compared to similar methods, TargetLoc performs better than these. MultiLoc performs considerably better than comparable prediction methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism.en
dc.identifier.isbn3-88579-400-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/24939
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman Conference on Bioinformatics 2005 (GCB 2005)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-71
dc.titleUsing N-terminal targeting sequences, amino acid composition, and sequence motifs for predicting protein subcellular localizationen
dc.typeText/Conference Paper
gi.citation.endPage59
gi.citation.publisherPlaceBonn
gi.citation.startPage45
gi.conference.date5.-7. Oktober 2005
gi.conference.locationHamburg
gi.conference.sessiontitleRegular Research Papers

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P071 - GCB 2005 - German Conference on Bioinformatics