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Functional annotation strategy for protein structures

Whole-genome sequencing projects are a major source of unknown function proteins. However, as predicting protein function from sequence remains a difficult task, research groups recently started to use 3D protein structures and structural models to bypass it. MED-SuMo compares protein surfaces analy...

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Detalles Bibliográficos
Autores principales: Doppelt, Olivia, Moriaud, Fabrice, Bornot, Aurélie, de Brevern, Alexandre G
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891723/
https://www.ncbi.nlm.nih.gov/pubmed/17597920
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author Doppelt, Olivia
Moriaud, Fabrice
Bornot, Aurélie
de Brevern, Alexandre G
author_facet Doppelt, Olivia
Moriaud, Fabrice
Bornot, Aurélie
de Brevern, Alexandre G
author_sort Doppelt, Olivia
collection PubMed
description Whole-genome sequencing projects are a major source of unknown function proteins. However, as predicting protein function from sequence remains a difficult task, research groups recently started to use 3D protein structures and structural models to bypass it. MED-SuMo compares protein surfaces analyzing the composition and spatial distribution of specific chemical groups (hydrogen bond donor, acceptor, positive, negative, aromatic, hydrophobic, guanidinium, hydroxyl, acyl and glycine). It is able to recognize proteins that have similar binding sites and thus, may perform similar functions. We present here a fine example which points out the interest of MED-SuMo approach for functional structural annotation.
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spelling pubmed-18917232007-06-27 Functional annotation strategy for protein structures Doppelt, Olivia Moriaud, Fabrice Bornot, Aurélie de Brevern, Alexandre G Bioinformation Views & Challenges Whole-genome sequencing projects are a major source of unknown function proteins. However, as predicting protein function from sequence remains a difficult task, research groups recently started to use 3D protein structures and structural models to bypass it. MED-SuMo compares protein surfaces analyzing the composition and spatial distribution of specific chemical groups (hydrogen bond donor, acceptor, positive, negative, aromatic, hydrophobic, guanidinium, hydroxyl, acyl and glycine). It is able to recognize proteins that have similar binding sites and thus, may perform similar functions. We present here a fine example which points out the interest of MED-SuMo approach for functional structural annotation. Biomedical Informatics Publishing Group 2007-03-19 /pmc/articles/PMC1891723/ /pubmed/17597920 Text en © 2006 Biomedical Informatics Publishing Group This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Views & Challenges
Doppelt, Olivia
Moriaud, Fabrice
Bornot, Aurélie
de Brevern, Alexandre G
Functional annotation strategy for protein structures
title Functional annotation strategy for protein structures
title_full Functional annotation strategy for protein structures
title_fullStr Functional annotation strategy for protein structures
title_full_unstemmed Functional annotation strategy for protein structures
title_short Functional annotation strategy for protein structures
title_sort functional annotation strategy for protein structures
topic Views & Challenges
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891723/
https://www.ncbi.nlm.nih.gov/pubmed/17597920
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