Structural and functional characterization of binding sites in metallocarboxypeptidases based on Optimal Docking Area analysis

Abstract

The metallocarboxypeptidases (MCPs) belonging to the clan MC were studied by the Optimal Docking Area (ODA) method to evaluate protein-protein binding sites and to provide a basis for the identification of binding partners for this class of enzymes. The ODA method identifies surface patches with optimal desolvation energy based on the selection of low-energy docking regions, generated from a set of surface points around the protein. With few exceptions, the ODA method identified surface patches with a significant low-energy docking surface for all the MCPs with known three-dimensional structure. Overall, in 14 out of 24 cases, the detected ODA patches were correctly located (i.e. more than 50% of the predicted residues were in known protein-protein binding sites), yielding a global success rate of 58%. More specifically, the success rate increased up to 80% on the ODA patches detected for the catalytic domains of the M14A subfamily, independently on the partner. Interestingly, the ODA residues on the catalytic domain were correctly located in the interface with the N-terminal pro domain in all MCPs. The spatial distribution of the ODA patches for the different members of the family is in relation to the origin and function of the particular MCP, which allowed distinguishing between them. In good agreement with the experimentally characterized protein interfaces, the total average surface area of the theoretically derived ODA patches for the catalytic domain of MCPs is around 1700 A2 and their content in hydrophobic residues is about 40%. As a particular case, the average surface area of the ODA patches in MCPs of crop insect pests is about twice that of the MCPs of vertebrates, which might be related to their particular function. We recognized two binding regions for the catalytic domain of the MCPs, one of them accounting for nearly all the known intermolecular interactions made up by the enzymes. Protein inhibitors seem to have evolved to dock on this subset of ODA patches, evoking the binding mode of the N-terminal pro domains. The second binding region detected, for which no ligands have been identified so far, seems to be related to the acquisition/maintenance of the native structure of the peptidase. Overall, the ODA method has been successful in identifying low-energy docking areas in a set of structurally and functionally related proteins, suggesting that it can be easily extended to other families in the search for protein-protein binding sites and for their functional significance.

Direct interaction between a human digestive protease and the mucoadhesive poly(acrylic acid)

“…dimensional structures of the M14 family of CPs that were the subject of a recent structural and functional characterization of binding sites using this method (Fernandez et al, 2007). In the mentioned study it was found that although the set of CPenzyme domains were structurally very similar, the protein-protein docking surfaces of different subclasses differed substantially and could be distinguished based on analysis of ODA residues over the surface of the protein.…”

“…To identify continuous surface patches with optimal desolvation energy, we followed the procedure established in a previous publication (Fernandez et al, 2007). Briefly, a number of starting points were defined around the protein using the centre of coordinates of each residue side chain.…”

“…compounds that do not coordinate to the metal ion. The recent successful application of the ODA method to the CP M14 family based on complexes of known three-dimensional structure (Fernandez et al, 2007) permits its extrapolation to newly released structures.…”

Computer applications for prediction of protein–protein interactions and rational drug design

“…70 The strategy has been applied to numerous cases of biological and therapeutic interest, with excellent predictive results. [71][72][73][74]…”

Prediction of protein binding sites and hot spots

“…In green is shown the contour of the real interface (if known) for comparison purposes. Positive predictive values of the predictions are 60, 80, 87, 90, and 90%, from left to right and top to bottom, respectively 94…”