Ligand Binding
Related Projects:  Computational Biology    Sampling-based Planning    Protein Folding    RNA Folding    NeuronPRM  
Computational methods are commonly used to predict protein-drug (or ligand) interactions and generally are crucial tools in the design and discovery of new drugs. These methods typically search for regions with favorable energy that geometrically fit the ligand, and then rank them as potential binding sites. In this work, we have been developing methods based on sampling-based planning methods to aid in identifying protein-ligand binding sites, to compare ligands based on their suitability to bind in a particular site, and to assess how accessible a particular binding site on a protein's surface is for a given ligand.


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.



Accessibility of Protein Binding Sites

Binding site accessibility is an important feature often ignored by methods that classify binding based solely on the energetic or geometric properties of the bound protein-ligand complex. To evaluate this necessity, we transform the ligand accessibility problem into a robot motion planning problem where the ligand is modeled as a flexible agent whose task is to travel from outside the protein to its binding site. Ligands are small molecules that interact with (bind to) a protein to trigger or inhibit the protein’s activity. This mechanism works like a lock and key model, and similar ligands can bind to the same receptor. For example, caffeine binds to brain cell receptors to block Adenosine's role of regulating sleep and local neural excitability. Caffeine is able to 'fool' adenosine receptors and speed up neural activity.

Prediction of protein-ligand interaction, or binding, is important for drug discovery research. Specifially, the research aims to answer two questions: (1) Can the ligand access the binding site of the target protein? (2) Is the protein-ligand interaction stable?
We use motion planningn algorithms to study binding site accessibility. The protein is modeled as an environment and the ligand as the moving object or robot with a goal to reach the binding site.

Using skeleton-guided path planning algorithms to analyze the accessibility of buried binding sites:

We use Rapidly-exploring Random Graphs coupled with Mean Curve workspace skeletons to quickly and thoroughly explore a protein environment and find valid paths for ligand motion. We annotated the mean curvature skeleton of the protein with energy information, and then bias planning to explore regions with favorabke energy values first. We used our algorithm to analyze accessibility of the protein Haloalkane dehalogenase (dhaA) from the bacterium Rhodococcus rhodochrous, used in soil inoculation. The protein has multiple mutants whose binding activity is regulated by the accessibility of the buried binding site.

Predicting protein ligand binding sites

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.


Using OBPRM and Haptic User Input to Search for Binding Sites
Our scoring function is based on the average potential energy of a local roadmap around any given configuration.

Related Publications

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
Keywords: Computational Biology, Ligand Binding, Motion Planning
Links : [Published] [Manuscript]

BibTex

@inbook{10.1145/3388440.3414707,
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

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.


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.