This is the second review in a two-part series. In the first review (1) we described the computational complexity involved in the docking of a ligand onto a receptor surface. In particular, we focused on efficient algorithms designed to handle this computational task. Such a procedure results in a large number of potential, geometrically feasible solutions. The difficulty is to pinpoint which of these is the more likely candidate. While there exists a number of approaches to rank these solutions according to different criteria, such as the size of the interface or some approximation of their binding energetics, none of the existing methods has been shown to be consistently successful in this endeavor. If the binding site is unknown a priori, the magnitude of the task is awesome. Here we propose one way of addressing this problem, i.e., via derivation and utilization of binding epitopes. If a library of such epitopes is available, particularly for a large number of protein families, it may be used to predict more likely binding sites for a given ligand. We describe an efficient, computer-vision based method to construct binding epitopes focusing on two ways through which such a library can be generated, (i) molecular surface-based, or (ii) residue-based. Alternatively, the two can be combined. We further describe how such a library may be used efficiently in the matching/docking procedure.
|Number of pages||9|
|Journal||Combinatorial Chemistry and High Throughput Screening|
|State||Published - 1999|