Molecular surface recognition by a computer vision-based technique.
Correct docking of a ligand onto a receptor surface is a complex problem, involving geometry and chemistry. Geometrically acceptable solutions require close contact between corresponding patches of surfaces of the receptor and of the ligand and no overlap between the van der Waals spheres of the remainder of the receptor and ligand atoms. In the quest for favorable chemical interactions, the next step involves minimization of the energy between the docked molecules. This work addresses the geometrical aspect of the problem. It is assumed that we have the atomic coordinates of each of the molecules. In principle, since optimally matching surfaces are sought, the entire conformational space needs to be considered. As the number of atoms residing on molecular surfaces can be several hundred, sampling of all rotations and translations of every patch of a surface of one molecule with respect to the other can reach immense proportions. The problem we are faced with here is reminiscent of object recognition problems in computer vision. Here we borrow and adapt the geometric hashing paradigm developed in computer vision to a central problem in molecular biology. Using an indexing approach based on a transformation invariant representation, the algorithm efficiently scans groups of surface dots (or atoms) and detects optimally matched surfaces. Potential solutions displaying receptor--ligand atomic overlaps are discarded. Our technique has been applied successfully to seven cases involving docking of small molecules, where the structures of the receptor--ligand complexes are available in the crystallographic database and to three cases where the receptors and ligands have been crystallized separately. In two of these three latter tests, the correct transformations have been obtained.