Ítem
Solo Metadatos
A novel methodology for characterizing and predicting protein functional sites
| dc.creator | Bobadilla, Leonardo | spa |
| dc.creator | Nino, Fernando | spa |
| dc.creator | Cepeda, Edilberto | spa |
| dc.creator | Patarroyo, Manuel A. | spa |
| dc.date.accessioned | 2020-08-28T15:49:58Z | |
| dc.date.available | 2020-08-28T15:49:58Z | |
| dc.date.created | 2008-01-02 | spa |
| dc.description.abstract | Since there is a strong need for computational methods to predict and characterize functional sites for initial anno- tations of protein structures, a new methodology that relies on descriptions of the functional sites based on local prop- erties is proposed in this paper. This new approach is in- dependent of conserved residues and conserved residue ge- ometry and takes advantage of the large number of protein structures available to construct models using a machine learning approach. Particularly, the proposed method per- formed feature extraction, clustering and classification on a protein structure data set, and it was validated on metal- binding sites (Ca2+, Zn2+, Na+,K+, Mg2+, Mn2+, Cu2+, Fe3+, Hg2+, Cl-) present in a non-redundant PDB (a total of 11,959 metal-binding sites in 3,609 proteins). Feature extraction provided a description of critical fea- tures for each metal-binding site, which were consistent with prior knowledge about them. Furthermore, new in- sights about metal-binding site microenvironments could be provided by the descriptors thus obtained. Results using k-fold cross-validation for classification showed accuracy above 90%. Complete proteins were scanned using these classifiers to locate metal-binding sites. Keywords: Functional Genomics, Protein functional sites, Feature Extraction, Clustering, Classification, Metal- binding sites. | eng |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.1109/BIBM.2007.36 | |
| dc.identifier.issn | ISBN: 978-0-7695-3031-4 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/28870 | |
| dc.language.iso | eng | spa |
| dc.publisher | IEEE | spa |
| dc.relation.citationEndPage | 354 | |
| dc.relation.citationStartPage | 349 | |
| dc.relation.citationTitle | 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) | |
| dc.relation.ispartof | IEEE International Conference on Bioinformatics and Biomedicine (BIBM), ISBN: 978-0-7695-3031-4 (2007); pp. 349-354 | spa |
| dc.relation.uri | https://www.computer.org/csdl/proceedings-article/bibm/2007/30310349/12OmNBNM8On | spa |
| dc.rights.accesRights | info:eu-repo/semantics/restrictedAccess | |
| dc.rights.acceso | Restringido (Acceso a grupos específicos) | spa |
| dc.source | 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) | spa |
| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject.keyword | Functional genomics | spa |
| dc.subject.keyword | Protein functional sites | spa |
| dc.subject.keyword | Feature extraction | spa |
| dc.subject.keyword | Clustering | spa |
| dc.subject.keyword | Classification | spa |
| dc.subject.keyword | Metalbinding sites | spa |
| dc.title | A novel methodology for characterizing and predicting protein functional sites | spa |
| dc.title.TranslatedTitle | Una metodología novedosa para caracterizar y predecir sitios funcionales de proteínas | spa |
| dc.type | bookPart | eng |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | |
| dc.type.spa | Parte de libro | spa |
