Project Alumni: Kasra Manavi, Shuvra Nath, Guang Song, Xinyu Tang, Lydia Tapia, Shawna Thomas
Interns and undergrad students: Katarzyna Leyk, Bonnie Kirkpatrick, Manasi Vartak
Supported By: NSF
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To view this as a motion planning problem, we consider the protein to be the environment and treat the ligand as the moving object (robot) whose goal is to reach the binding site. This approach allows us to consider the flexibility of the ligand and also the protein, if desired.
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:
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.
Using Guided Motion Planning to Study Binding Site Accessibility, Diane Uwacu, Abigail Ren, Shawna Thomas, Nancy M. Amato, Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Issue: 109, (Virtual) New York, USA, Sep 2020. DOI: 10.1145/3388440.3414707 @inbook{10.1145/3388440.3414707, Computational methods are commonly used to predict protein-ligand interactions. These methods typically search for regions with favorable energy that geometrically fit the ligand, and then rank them as potential binding sites. While this general strategy can provide good predictions in some cases, it does not do well when the binding site is not accessible to the ligand. In addition, recent research has shown that in some cases protein access tunnels play a major role in the activity and stability of the protein's binding interactions. Hence, to fully understand the binding behavior of such proteins, it is imperative to identify and study their access tunnels. In this work, we present a motion planning algorithm that scores protein binding site accessibility for a particular ligand. This method can be used to screen ligand candidates for a protein by eliminating those that cannot access the binding site. This method was tested on two case studies to analyze effects of modifying a protein's access tunnels to increase activity and/or stability as well as study how a ligand inhibitor blocks access to the protein binding site.
Keywords: Computational Biology, Ligand Binding, Motion Planning
Links : [Published] [Manuscript] BibTex
author = {Uwacu, Diane and Ren, Abigail and Thomas, Shawna and Amato, Nancy M.},
title = {Using Guided Motion Planning to Study Binding Site Accessibility},
year = {2020},
isbn = {9781450379649},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3388440.3414707},
abstract = {Computational methods are commonly used to predict protein-ligand interactions. These methods typically search for regions with favorable energy that geometrically fit the ligand, and then rank them as potential binding sites. While this general strategy can provide good predictions in some cases, it does not do well when the binding site is not accessible to the ligand. In addition, recent research has shown that in some cases protein access tunnels play a major role in the activity and stability of the protein's binding interactions. Hence, to fully understand the binding behavior of such proteins, it is imperative to identify and study their access tunnels. In this work, we present a motion planning algorithm that scores protein binding site accessibility for a particular ligand. This method can be used to screen ligand candidates for a protein by eliminating those that cannot access the binding site. This method was tested on two case studies to analyze effects of modifying a protein's access tunnels to increase activity and/or stability as well as study how a ligand inhibitor blocks access to the protein binding site.},
booktitle = {Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
articleno = {109},
numpages = {10}
}
Abstract
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. @MISC{Bayazit00ligandbinding, 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.
Keywords: Ligand Binding, Sampling-Based Motion Planning
Links : [Published] BibTex
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