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| Autor | Moraes, Joao P. A. | |
| Autor | Pappa, Gisele Lobo | |
| Autor | Pires, Douglas Eduardo Valente | |
| Autor | Izidoro, Sandro Carvalho | |
| Fecha de acceso | 2018-05-18T17:12:38Z | |
| Fecha de disponibilización | 2018-05-18T17:12:38Z | |
| Fecha de publicación | 2017 | |
| Referencia | MORAES, Joao P. A. et al. GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms. Nucleic Acids Res. , v. 45, n. W1. Art; W315-W319, 2017. | pt_BR |
| ISSN | 0305-1048 | pt_BR |
| URI | https://www.arca.fiocruz.br/handle/icict/26485 | |
| Idioma | eng | pt_BR |
| Editor | Oxford Journals | pt_BR |
| Derechos de autor | restricted access | pt_BR |
| Palabras clave en Portugués | algoritimos geneticos | pt_BR |
| Palabras clave en Portugués | funções da proteina | pt_BR |
| Título | GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms | pt_BR |
| Tipo del documento | Article | |
| DOI | 10.1093/nar/gkx337 | |
| Resumen en Inglés | Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/. | pt_BR |
| Afiliación | Universidade Federal de Itajuba. Departamento de Engenharia Computacional. Itajubá, MG, Brazil | pt_BR |
| Afiliación | Universidade Federal de Minas Gerais. Departamento de Ciência da Computação. Belo Horizonte, MG, Brazil | pt_BR |
| Afiliación | Fundação Oswaldo Cruz. Instituto Rene Rachou. Belo Horizonte, MG, Brazil | pt_BR |
| Afiliación | Universidade Federal de Itajuba. Departamento de Engenharia Computacional. Itajubá, MG, Brazil | pt_BR |
| Palavras clave en Inglês | genetic algorithms | pt_BR |
| Palavras clave en Inglês | protein function | pt_BR |
| Fecha de embargo | 2022-01-01 |
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