In this project, 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 ligand binding.
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 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.
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:
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.
We have implemented three experiments to answer the following questions:
In order to answer these questions we have three experiments. In the first experiments we treat the ligand as rigid body, we use OBPRM to generate initial configurations and decide the binding site.
In the second experiment, user collects the initial configurations and our automated planner decides the binding sites based on them.
In the third experiment, we let OBPRM to generate the initial configurations as in the first experiment but this time the ligand is an articulated representation.
Our results show that our approach to molecular docking is promising. In the examples we studied, we were able to find and recognize configurations in the true binding site. However, further processing, perhaps with a more accurate potential function, will be needed to find an exact binding configuration. For example, candidates identified by our our method might be used as input for other docking programs that perform detailed simulations, such as molecular dynamics methods. We also saw that better results were obtained using an articulated representation for the ligand, as opposed to the commonly used rigid body simplification. User input was seen to improve efficiency, and moreover, haptic feedback was observed to help the user better understand the problem.
Our future work includes finding a better representation for potential energy formulations, concentrating on the binding sites and reaching the binding configurations, and improving the haptic interface by letting the user collect configurations for a flexible ligand.
Uniform Sampling Framework for Sampling Based Motion Planning and Its Applications to Robotics and Protein Ligand Binding, Hsin-Yi (Cindy) Yeh, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, May 2016.
Ph.D. Thesis(pdf, abstract)
Solving Motion Planning Problems by Iterative Relaxation of Constraints, Osman BurÁhan Bayazit, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, U.S.A., May 2003.
Ph.D. Thesis(ps, pdf, abstract)
Ligand Binding with OBPRM and Haptic User Input, O. Burchan Bayazit, Guang Song, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 954-959, May 2001.
Proceedings(ps, pdf, abstract)
Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch, O. Burchan Bayazit, Guang Song, Nancy M. Amato, Technical Report, TR00-025, Department of Computer Science and Engineering, Texas A&M University, Oct 2000.
Technical Report(ps, pdf, abstract)
Supported by NSF
Project Alumni:O. Burchan Bayazit,Guang Song
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