The Community for Technology Leaders
2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) (2007)
Fremont, California
Nov. 2, 2007 to Nov. 4, 2007
ISBN: 0-7695-3031-1
pp: 349-354
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. Java source code available upon request. Supplementary Website: http://dis.unal.edu.co/~biocomp/metals/
INDEX TERMS
CITATION

F. Ni?, L. Bobadilla, E. Cepeda and M. A. Patarroyo, "A Novel Methodology for Characterizing and Predicting Protein Functional Sites," 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007)(BIBM), Fremont, California, 2007, pp. 349-354.
doi:10.1109/BIBM.2007.36
99 ms
(Ver 3.3 (11022016))