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Characterizing and predicting catalytic residues in enzyme active sites based on local properties: a machine learning approach
| dc.creator | Bobadilla, Leonardo | spa |
| dc.creator | Nino, Fernando | spa |
| dc.creator | Cepeda, Edilberto | spa |
| dc.creator | Patarroyo, Manuel A. | spa |
| dc.date.accessioned | 2020-08-28T15:49:56Z | |
| dc.date.available | 2020-08-28T15:49:56Z | |
| dc.date.created | 2007-11-05 | spa |
| dc.description.abstract | Developing computational methods for assigning protein function from tertiary structure is a very important problem, predicting a catalytic mechanism based only on structural information being a particularly challenging task. This work focuses on helping to understand the molecular basis of catalysis by exploring the nature of catalytic residues, their environment and characteristic properties in a large data set of enzyme structures and using this information to predict enzyme structures' active sites. A machine learning approach that performs feature extraction, clustering and classification on a protein structure data set is proposed. The 6,376 residues directly involved in enzyme catalysis, present in more than 800 proteins structures in the PDB were analyzed. Feature extraction provided a description of critical features for each catalytic residue, which were consistent with prior knowledge about them. Results from k-fold-cross-validation for classification showed more than 80% accuracy. Complete enzymes were scanned using these classifiers to locate catalytic residues. | eng |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.1109/BIBE.2007.4375671 | |
| dc.identifier.issn | ISBN: 1-4244-1509-8 | |
| dc.identifier.issn | EISBN: 978-1-4244-1509-0 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/28861 | |
| dc.language.iso | eng | spa |
| dc.publisher | IEEE | spa |
| dc.relation.citationEndPage | 945 | |
| dc.relation.citationStartPage | 938 | |
| dc.relation.citationTitle | 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering | |
| dc.relation.ispartof | IEEE 7th International Symposium on BioInformatics and BioEngineering, ISBN: 1-4244-1509-8;EISBN: 978-1-4244-1509-0 (2007); pp. 938-945 | spa |
| dc.relation.uri | https://ieeexplore.ieee.org/document/4375671 | spa |
| dc.rights.accesRights | info:eu-repo/semantics/restrictedAccess | |
| dc.rights.acceso | Restringido (Acceso a grupos específicos) | spa |
| dc.source | 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering | spa |
| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject.keyword | Biochemistry | spa |
| dc.subject.keyword | Machine learning | spa |
| dc.subject.keyword | Sequences | spa |
| dc.subject.keyword | Genomics | spa |
| dc.subject.keyword | Bioinformatics | spa |
| dc.subject.keyword | Predictive models | spa |
| dc.subject.keyword | Protein engineering | spa |
| dc.subject.keyword | Nuclear magnetic resonance | spa |
| dc.subject.keyword | Data mining | spa |
| dc.subject.keyword | Crystallography | spa |
| dc.title | Characterizing and predicting catalytic residues in enzyme active sites based on local properties: a machine learning approach | spa |
| dc.title.TranslatedTitle | Caracterización y predicción de residuos catalíticos en sitios activos de enzimas según propiedades locales: un enfoque de aprendizaje automático | spa |
| dc.type | bookPart | eng |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | |
| dc.type.spa | Parte de libro | spa |
