Ligand binding with OBPRM and user input

Abstract

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 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 building sites for all complexes studied. We find that user input helps the planner, and haptic device helps the user to understand the protein structure by enabling them to feel the difficult-to-visualize forces.