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Shape distributions and protein similarity

dc.contributor.authorCanzar, Stefan
dc.contributor.authorRemy, Jan
dc.contributor.editorHuson, Daniel
dc.contributor.editorKohlbacher, Oliver
dc.contributor.editorLupas, Andrei
dc.contributor.editorNieselt, Kay
dc.contributor.editorZell, Andreas
dc.date.accessioned2019-08-12T13:06:13Z
dc.date.available2019-08-12T13:06:13Z
dc.date.issued2006
dc.description.abstractIn this paper we describe a similarity model that provides the objective basis for clustering proteins of similar structure. More specifically, we consider the following variant of the protein-protein similarity problem: We want to find proteins in a large database D that are very similar to a given query protein in terms of geometric shape. We give experimental evidence, that the shape similarity model of Osada, Funkhouser, Chazelle and Dobkin [OFCD02] can be transferred to the context of protein structure comparison. This model is very simple and leads to algorithms that have attractive space requirements and running times. For example, it took 0.39 seconds to retrieve the eight members of the seryl family out of 26, 600 domains. Furthermore, a very high agreement with one of the most popular classification schemes proved the significance of our simplified representation of complex proteins structure by a distribution of C��-C��distances.en
dc.identifier.isbn978-3-88579-177-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/24200
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman Conference on Bioinformatics
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-83
dc.titleShape distributions and protein similarityen
dc.typeText/Conference Paper
gi.citation.endPage10
gi.citation.publisherPlaceBonn
gi.citation.startPage1
gi.conference.dateSeptember 19-22, 2006
gi.conference.locationTübingen
gi.conference.sessiontitleRegular Research Papers

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