Using Motion Planning to Study Ligand Binding
نویسندگان
چکیده
In the drug discovery process, pharmaceutical companies screen many candidates for the most promising. Drug screening is costly, but by carrying out a part of it computationally or virtually, the cost can be reduced. An effective drug molecule acts as a ligand that binds to the active site of a protein to form a protein-ligand complex. The binding configurations of the protein and ligand may be predicted by molecular docking. The predicted configurations may be used to determine the binding affinity of the complex. Ligand binding and molecular docking are computationally intensive problems in computational biology. We present an approach that applies a motion planning technique to these problems. We approximate ligands to robots and proteins to obstacles using Uniform Obstacle-Based Probabilistic Roadmap (UOBPRM), a novel planning algorithm that uniformly distributes robot configurations around obstacle surfaces. We develop an algorithm to test and rank protein-ligand complexes based on an approximation of binding affinity. We present five complexes with experimentally determined binding affinities published in the literature. We find that our simulated ranking of these complexes matches the ranking from the published binding affinities. Thus, UOBPRM shows promise as a potential technique with which to rank protein-ligand complexes based on their binding affinity properties. This information may be useful as a cost-saving measure to pharmaceutical companies in the area of computational or virtual drug screening.
منابع مشابه
A Motion Planning Approach to Flexible Ligand Binding
Most computational models of protein-ligand interactions consider only the energetics of the final bound state of the complex and do not examine the dynamics of the ligand as it enters the binding site. We have developed a novel technique for studying the dynamics of protein-ligand interactions based on motion planning algorithms from the field of robotics. Our algorithm uses electrostatic and ...
متن کاملLigand Binding with OBPRM and User Input
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 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 as the effects of supplementary user input collected using a haptic device....
متن کاملStatement of Research Interests
Figure 1: Protein 3W6H in wireframe (a) and in spheres (b) viewed by PyMOL. The protein is modeled as a rigid obstacle (c) and we can use a recently developed method which guarantees the sample distribution around the obstacle to generate possible binding ligand configurations with respect to the target protein (d). My graduate research focus is motion planning [4, 6] and its application in com...
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تاریخ انتشار 2014