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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/08/2013 |
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Data da última atualização: |
22/05/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
BOARETO, M.; YAMAGISHI, M. E. B.; CATICHA, N.; LEITE, V. B. P. |
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Afiliação: |
MARCELO BOARETO, USP; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; NESTOR CATICHA, USP; VITOR B. P. LEITE, Unesp. |
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Título: |
Relationship between global structural parameters and Enzyme Commission hierarchy: implications for function prediction. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Computational Biology and Chemistry, Oxford, v. 40, p. 15-19, 2012. |
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DOI: |
https://doi.org/10.1016/j.compbiolchem.2012.06.003 |
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Idioma: |
Inglês |
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Conteúdo: |
In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. MenosIn protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies su... Mostrar Tudo |
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Palavras-Chave: |
Bioinformática; Enzyme structure; Estrutura de enzimas; Function prediction; Parâmetros físico-químicos; Physicochemical parameters; Previsão de função. |
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Thesaurus NAL: |
Bioinformatics. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02766naa a2200265 a 4500
001 1964271
005 2024-05-22
008 2012 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compbiolchem.2012.06.003$2DOI
100 1 $aBOARETO, M.
245 $aRelationship between global structural parameters and Enzyme Commission hierarchy$bimplications for function prediction.$h[electronic resource]
260 $c2012
520 $aIn protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them.
650 $aBioinformatics
653 $aBioinformática
653 $aEnzyme structure
653 $aEstrutura de enzimas
653 $aFunction prediction
653 $aParâmetros físico-químicos
653 $aPhysicochemical parameters
653 $aPrevisão de função
700 1 $aYAMAGISHI, M. E. B.
700 1 $aCATICHA, N.
700 1 $aLEITE, V. B. P.
773 $tComputational Biology and Chemistry, Oxford$gv. 40, p. 15-19, 2012.
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