Authors: O. Burchan Bayazit, Guang Song, Nancy M. Amato

Venue: ACM Digital Library
DOI:
Link to Publication

Abstract:
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 especially 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 improvements obtained when supplementary user input is collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (obprm) to some known protein-ligand pairs are very encouraging. In particular, we were able to automatically generate congurations close to, and correctly identify, the true binding site in the three protein-ligand complexes we tested. We nd that user input helps the planner, and a haptic device helps the user to understand the protein structure by enabling them to feel the forces which are hard to visualize. 

@misc{Bayazit-lbwoah-2000, 
 author = {O. Burchan Bayazit and Guang Song and Nancy M. Amato}, 
 title = {Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch}, 
 year = {2000} 
}