2.50
Hdl Handle:
http://hdl.handle.net/10547/227182
Title:
Classification of proteins based on similarity of two-dimensional protein maps.
Authors:
Albrecht, Birgit; Grant, Guy H.; Sisu, Cristina; Richards, W. Graham
Abstract:
Data reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.
Affiliation:
Department of Chemistry, University of Oxford, Central Chemistry Laboratory, South Parks Road, Oxford OX13QH, UK.
Citation:
Classification of proteins based on similarity of two-dimensional protein maps. 2008, 138 (1-2):11-22 Biophys. Chem.
Publisher:
Elsevier
Journal:
Biophysical chemistry
Issue Date:
Nov-2008
URI:
http://hdl.handle.net/10547/227182
DOI:
10.1016/j.bpc.2008.08.004
PubMed ID:
18814947
Type:
Article
Language:
en
ISSN:
1873-4200
Appears in Collections:
Cell and Cryobiology Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorAlbrecht, Birgiten_GB
dc.contributor.authorGrant, Guy H.en_GB
dc.contributor.authorSisu, Cristinaen_GB
dc.contributor.authorRichards, W. Grahamen_GB
dc.date.accessioned2012-06-01T13:44:53Z-
dc.date.available2012-06-01T13:44:53Z-
dc.date.issued2008-11-
dc.identifier.citationClassification of proteins based on similarity of two-dimensional protein maps. 2008, 138 (1-2):11-22 Biophys. Chem.en_GB
dc.identifier.issn1873-4200-
dc.identifier.pmid18814947-
dc.identifier.doi10.1016/j.bpc.2008.08.004-
dc.identifier.urihttp://hdl.handle.net/10547/227182-
dc.description.abstractData reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.en_GB
dc.language.isoenen
dc.publisherElsevieren_GB
dc.rightsArchived with thanks to Biophysical chemistryen_GB
dc.subject.meshComputational Biology-
dc.subject.meshDatabases, Protein-
dc.subject.meshModels, Biological-
dc.subject.meshPhosphotransferases-
dc.subject.meshProtein Conformation-
dc.subject.meshProtein Folding-
dc.subject.meshProtein Structure, Secondary-
dc.subject.meshProtein Structure, Tertiary-
dc.subject.meshProteins-
dc.subject.meshStructural Homology, Protein-
dc.titleClassification of proteins based on similarity of two-dimensional protein maps.en
dc.typeArticleen
dc.contributor.departmentDepartment of Chemistry, University of Oxford, Central Chemistry Laboratory, South Parks Road, Oxford OX13QH, UK.en_GB
dc.identifier.journalBiophysical chemistryen_GB
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