A global optimization algorithm for protein surface alignment

In this paper we propose a new method for local structural alignment of protein surfaces based on global optimization techniques. The method can be applied to the comparison and recognition of protein binding sites, a relevant problem in drug design. Given the three-dimensional structures of two proteins, we are interested in finding the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known iterative closest point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach.