MOLECULAR-SURFACE RECOGNITION BY A COMPUTER VISION-BASED TECHNIQUE

Correct docking of a ligand onto a receptor surface is a complex probl em, involving geometry and chemistry. Geometrically acceptable solutio ns require close contact between corresponding patches of surfaces of the receptor and of the ligand and no overlap between the van der Waal s spheres of the remainder of the receptor and ligand atoms. In the qu est for favorable chemical interactions, the next step involves minimi zation 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 opti mally matching surfaces are sought, the entire conformational space ne eds to be considered. As the number of atoms residing on molecular sur faces can be several hundred, sampling of all rotations and translatio ns of every patch of a surface of one molecule with respect to the oth er can reach immense proportions. The problem we are faced with here i s reminiscent of object recognition problems in computer vision. Here we borrow and adapt the geometric hashing paradigm developed in comput er vision to a central problem in molecular biology. Using an indexing approach based on a transformation invariant representation, the algo rithm 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 s uccessfully 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 l igands have been crystallized separately. In two of these three latter tests, the correct transformations have been obtained.