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AnEnPi: identification and annotation of analogous enzymes.

Otto TD, GuimarĂ£es AC, Degrave WM, de Miranda AB - BMC Bioinformatics (2008)

Bottom Line: They are thought to have arisen as the result of independent evolutionary events.We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes).AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br

ABSTRACT

Background: Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development.

Results: In this work, we have constructed and implemented a new approach, AnEnPi (the Analogous Enzyme Pipeline), using a combination of bioinformatics tools like BLAST, HMMer, and in-house scripts, to assist in the identification, annotation, comparison and study of analogous and homologous enzymes. The algorithm for the detection of analogy is based i) on the construction of groups of homologous enzymes and ii) on the identification of cases where a given enzymatic activity is performed by two or more proteins without significant similarity between their primary structures. We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes). AnEnPi is of considerable value in the construction of initial datasets that can be further curated, particularly in gene and genome annotation, in studies involving molecular evolution and metabolism and in the identification of new potential drug targets.

Conclusion: AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes. It is available for academic use at: http://bioinfo.pdtis.fiocruz.br/AnEnPi/

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Work flow of AnEnPi. Databases are represented as rectangles. Darker gray rectangles represent the five datasets of clusters. Light gray rectangles are the modular functions of AnEnPi, described in the text.
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Figure 1: Work flow of AnEnPi. Databases are represented as rectangles. Darker gray rectangles represent the five datasets of clusters. Light gray rectangles are the modular functions of AnEnPi, described in the text.

Mentions: An overview of AnEnPi is shown in Figure 1. For clustering we used the similarity score with a cut-off Value 120 of BLASTp pair wise comparisons between all proteins included in a specified dataset, based on the experimental work of Galperin [7]. In the work described here, groups are composed of proteins sharing the same enzymatic activity (EC classes). Within a group, protein sequences are clustered. Enzymes within a given cluster are considered homologous, while enzymes in different clusters (of the same group/function) are considered analogous. These clusters are stored in a flat file database, which can be used to annotate or re-annotate a set of proteins. To improve visualization, metabolic maps can be generated automatically.


AnEnPi: identification and annotation of analogous enzymes.

Otto TD, GuimarĂ£es AC, Degrave WM, de Miranda AB - BMC Bioinformatics (2008)

Work flow of AnEnPi. Databases are represented as rectangles. Darker gray rectangles represent the five datasets of clusters. Light gray rectangles are the modular functions of AnEnPi, described in the text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2628392&req=5

Figure 1: Work flow of AnEnPi. Databases are represented as rectangles. Darker gray rectangles represent the five datasets of clusters. Light gray rectangles are the modular functions of AnEnPi, described in the text.
Mentions: An overview of AnEnPi is shown in Figure 1. For clustering we used the similarity score with a cut-off Value 120 of BLASTp pair wise comparisons between all proteins included in a specified dataset, based on the experimental work of Galperin [7]. In the work described here, groups are composed of proteins sharing the same enzymatic activity (EC classes). Within a group, protein sequences are clustered. Enzymes within a given cluster are considered homologous, while enzymes in different clusters (of the same group/function) are considered analogous. These clusters are stored in a flat file database, which can be used to annotate or re-annotate a set of proteins. To improve visualization, metabolic maps can be generated automatically.

Bottom Line: They are thought to have arisen as the result of independent evolutionary events.We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes).AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br

ABSTRACT

Background: Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development.

Results: In this work, we have constructed and implemented a new approach, AnEnPi (the Analogous Enzyme Pipeline), using a combination of bioinformatics tools like BLAST, HMMer, and in-house scripts, to assist in the identification, annotation, comparison and study of analogous and homologous enzymes. The algorithm for the detection of analogy is based i) on the construction of groups of homologous enzymes and ii) on the identification of cases where a given enzymatic activity is performed by two or more proteins without significant similarity between their primary structures. We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes). AnEnPi is of considerable value in the construction of initial datasets that can be further curated, particularly in gene and genome annotation, in studies involving molecular evolution and metabolism and in the identification of new potential drug targets.

Conclusion: AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes. It is available for academic use at: http://bioinfo.pdtis.fiocruz.br/AnEnPi/

Show MeSH