AN AUTOMATED COMPUTER VISION AND ROBOTICS-BASED TECHNIQUE FOR 3-D FLEXIBLE BIOMOLECULAR DOCKING AND MATCHING

The generation of binding modes between two molecules, also known as m olecular docking, is a key problem in rational drug design and biomole cular recognition. Docking a ligand e.g., a drug molecule or a protein molecule, to a protein receptor, involves recognition of molecular su rfaces as molecules interact at their surface. Recent studies report t hat the activity of many molecules induces conformational transitions by 'hinge-bending', which involves movements of relatively rigid parts with respect to each other. In ligand-receptor binding, relative rota tional movements of molecular substructures about their common hinges have been observed. For automatically predicting flexible molecular in teractions, we adapt a new technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulat ed objects. These type of objects consist of ligid parts which are con nected by rotary joints (hinges). Our approach is based on an extensio n and generalization of the Geometric Hashing and Generalized Hough Tr ansform paradigm for rigid object recognition. Unlike other techniques which match each part individually, our approach exploits forcefully and efficiently enough the fact that the different rigid parts do belo ng to the same flexible molecule. We show experimental results obtaine d by an implementation of the algorithm for rigid and flexible docking . While the 'correct', crystal-bound complex is obtained with a small RMSD, additional, predictive 'high scoring' binding modes are generate d as well, The diverse applications and implications of this general, powerful tool are discussed.