Exploring functionally related enzymes using radially distributed properties of active sites around the reacting points of bound ligands
1 Division of Bioinformatics, Hokkaido University Research Center for Zoonosis Control, North 20 West 10, Sapporo, Hokkaido, 001-0020, Japan
2 Graduate School of Information Science and Technology, Hokkaido University, North 14 West 9, Sapporo, Hokkaido, 060-0814, Japan
BMC Structural Biology 2012, 12:5 doi:10.1186/1472-6807-12-5Published: 26 April 2012
Structural genomics approaches, particularly those solving the 3D structures of many proteins with unknown functions, have increased the desire for structure-based function predictions. However, prediction of enzyme function is difficult because one member of a superfamily may catalyze a different reaction than other members, whereas members of different superfamilies can catalyze the same reaction. In addition, conformational changes, mutations or the absence of a particular catalytic residue can prevent inference of the mechanism by which catalytic residues stabilize and promote the elementary reaction. A major hurdle for alignment-based methods for prediction of function is the absence (despite its importance) of a measure of similarity of the physicochemical properties of catalytic sites. To solve this problem, the physicochemical features radially distributed around catalytic sites should be considered in addition to structural and sequence similarities.
We showed that radial distribution functions (RDFs), which are associated with the local structural and physicochemical properties of catalytic active sites, are capable of clustering oxidoreductases and transferases by function. The catalytic sites of these enzymes were also characterized using the RDFs. The RDFs provided a measure of the similarity among the catalytic sites, detecting conformational changes caused by mutation of catalytic residues. Furthermore, the RDFs reinforced the classification of enzyme functions based on conventional sequence and structural alignments.
Our results demonstrate that the application of RDFs provides advantages in the functional classification of enzymes by providing information about catalytic sites.