A Novel Methodology for Characterizing and Predicting Protein Functional Sites

Since there is a strong need for computational methods to predict and characterize functional sites for initial annotations of protein structures, a new methodology that relies on descriptions of the functional sites based on local properties is proposed in this paper. This new approach is independent of conserved residues and conserved residue geometry and takes advantage of the large number of protein structures available to construct models using a machine learning approach. Particularly, the proposed method performed 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 features for each metal-binding site, which were consistent with prior knowledge about them. Furthermore, new insights 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. © 2007 IEEE.