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The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation

Structural genomics efforts have led to increasing numbers of novel, uncharacterized protein structures with low sequence identity to known proteins, resulting in a growing need for structure-based function recognition tools. Our method, SeqFEATURE, robustly models protein functions described by seq...

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Detalles Bibliográficos
Autores principales: Wu, Shirley, Liang, Mike P, Altman, Russ B
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2395245/
https://www.ncbi.nlm.nih.gov/pubmed/18197987
http://dx.doi.org/10.1186/gb-2008-9-1-r8
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author Wu, Shirley
Liang, Mike P
Altman, Russ B
author_facet Wu, Shirley
Liang, Mike P
Altman, Russ B
author_sort Wu, Shirley
collection PubMed
description Structural genomics efforts have led to increasing numbers of novel, uncharacterized protein structures with low sequence identity to known proteins, resulting in a growing need for structure-based function recognition tools. Our method, SeqFEATURE, robustly models protein functions described by sequence motifs using a structural representation. We built a library of models that shows good performance compared to other methods. In particular, SeqFEATURE demonstrates significant improvement over other methods when sequence and structural similarity are low.
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spelling pubmed-23952452008-05-29 The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation Wu, Shirley Liang, Mike P Altman, Russ B Genome Biol Method Structural genomics efforts have led to increasing numbers of novel, uncharacterized protein structures with low sequence identity to known proteins, resulting in a growing need for structure-based function recognition tools. Our method, SeqFEATURE, robustly models protein functions described by sequence motifs using a structural representation. We built a library of models that shows good performance compared to other methods. In particular, SeqFEATURE demonstrates significant improvement over other methods when sequence and structural similarity are low. BioMed Central 2008-01-16 /pmc/articles/PMC2395245/ /pubmed/18197987 http://dx.doi.org/10.1186/gb-2008-9-1-r8 Text en Copyright © 2008 Wu et al.; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Wu, Shirley
Liang, Mike P
Altman, Russ B
The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title_full The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title_fullStr The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title_full_unstemmed The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title_short The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation
title_sort seqfeature library of 3d functional site models: comparison to existing methods and applications to protein function annotation
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2395245/
https://www.ncbi.nlm.nih.gov/pubmed/18197987
http://dx.doi.org/10.1186/gb-2008-9-1-r8
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