Computational Modeling and Simulation in Biomedical Research

Dynamics of Biomolecular Ligand Recognition

Author(s): Ilija Cvijetić, Dušan Petrović and Mire Zloh * .

Pp: 103-139 (37)

DOI: 10.2174/9789815165463124010008

* (Excluding Mailing and Handling)

Abstract

Molecular recognition is one of the key principles in the development of active pharmaceutical compounds. Active molecules that can be delivered in vivo to a biological target, responsible for pathological states associated with a disease, can be developed into therapeutic agents. Such molecules must overcome relevant biological barriers and establish intermolecular interactions with the target in order to modulate its activity. The drug discovery process entails the identification of potential therapeutic agents and the design of optimal formulations for targeted or prolonged drug release in vivo. This requires a balanced and dynamic interplay of interactions between the therapeutic agent and different molecular systems through diverse environments. Computational methods, including molecular dynamics simulation, complement experiments in the evaluation of relevant biochemical processes at different stages of drug development, e.g., the elucidation of the ligand mode of action. In this chapter, we will explore the applications of various molecular modeling approaches to evaluate the key interactions small molecules form with different targets. Molecular docking is the most common tool used to evaluate the ligand complementarity to the target binding site. Although the flexible receptor and induced fit approaches provide some additional insights into how target flexibility affects ligand binding, biomolecules have a large number of degrees of freedom, often demanding the use of more exhaustive sampling methods to explore the ligand-binding associated conformational dynamics. This can be achieved with molecular dynamics and enhanced sampling approaches to model large conformational changes. In particular, molecular dynamics of protein-ligand complexes can describe the plasticity of the protein binding sites by identifying dynamic pharmacophores―dynophores. These pharmacophore models incorporate information on target flexibility and describe the dynamics of intermolecular interactions. We will provide a relevant introduction to the above-mentioned techniques and explore key successful applications in hit discovery and lead optimization efforts of drug development campaigns.

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