Paper
8 March 1996 Visualization tool for simulating ligand-receptor binding process
Karl Geppert, Binh Pham, R. Quinn
Author Affiliations +
Proceedings Volume 2656, Visual Data Exploration and Analysis III; (1996) https://doi.org/10.1117/12.234676
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
Ligand-receptor protein binding is an important process in drug design. This paper discusses the development of a visual tool for studying the binding of ligand-receptor pairs, to help in identifying active sites of the molecules. The tool can be used to explore many possible variations in binding pairs without doing expensive laboratory experiments. The traditional view of the binding process has been limited to a static lock and key model. It is now regarded that the ligand and receptor change dynamically during the interaction. Traditional experimental methods only determine the shape of static chemical structures. Our tool improves on the previous methods by dynamically simulating the entire binding process of isolated ligand-receptor pairs. The open design of our dynamic interaction model allows its extension with further constraints and heuristic rules. This is needed when the existing forces do not provide a sufficiently complete system description. For example, more detailed simulation constraints can increase the probability of convergence of a ligand-receptor pair. New constraints can limit the degree of freedom of rotation about bonds, to take into account molecular affinity. The intermolecular rules may be changed to include the effects of hydrogen bonding, and other forces.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Karl Geppert, Binh Pham, and R. Quinn "Visualization tool for simulating ligand-receptor binding process", Proc. SPIE 2656, Visual Data Exploration and Analysis III, (8 March 1996); https://doi.org/10.1117/12.234676
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KEYWORDS
Molecules

Visualization

Chemical species

Receptors

Visual process modeling

Modeling

Data modeling

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