Ligand binding with OBPRM and user input

In this paper, we present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are interested in locating binding sites on the protein for a ligand molecule. Our work investigates the performance of a fully automated motion planner, as well as the effects of supplementary user input collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (OBPRM) to some protein-ligand complexes are encouraging. The framework successfully identified potential binding sites for all complexes studied. We find that user input helps the planner, and a haptic device helps the user to understand the protein structure by enabling them to feel the difficult-to-visualize forces.