Predicting protein ligand binding sites
Related: Computational Biology   Ligand Binding   Binding Site Accessibility   Protein Folding   RNA Folding  

Current Contributors: Diane Uwacu, Shawna Thomas, Nancy Amato
Project Alumni: Kasra Manavi, Shuvra Nath, Guang Song, Xinyu Tang, Lydia Tapia
Interns and undergrad students: Katarzyna Leyk, Bonnie Kirkpatrick, Manasi Vartak

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 configurations close to, and correctly identify, the true binding site in the three protein-ligand complexes we tested. 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 forces, which are hard to visualize.


Using OBPRM and Haptic User Input to Search for Binding Sites

One of the challenges in haptic research is the very fast (i.e., ~1kHz) update requirements for force feedback. This limits the possible applications to very simple environments. However, we used a grid based force calculation algorithm to approximate the force feedback, hence, achieved a realistic feedback even in the complex proteins.

Our approach to ligand binding problem is as the following:

  • Generate binding site candidates
  • Create a roadmap using these candidates
  • Recognize binding sites
In generation, we used both our automated planner (OBPRM) created or used collected configurations. Since these configurations may have higher potentials then the desired, (a binding sites should have lower potential), we pushed these configuration to local minima close them.

Later we used these pushed configurations to create a roadmap. In the roadmap, we chose the largest connected component. The accessibility is an important issue in ligand binding, and the larger a connected component, the more likely its nodes are accessible to outside world. In the largest connected component we used the low energy configurations as our candidate sites. Later we used our scoring function to evaluate each candidate.

Our scoring function is based on the average potential energy of a local roadmap around any given configuration.

Related Publications

Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch, O. Burchan Bayazit , Guang Song , Nancy M. Amato , ACM Digital Library, College Station, Texas, USA, Oct 2000.
Keywords: Ligand Binding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@MISC{Bayazit00ligandbinding,
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}
}


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.