Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods

By Xing Du, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-lai Ji, Shu-Qun Liu. 2016

1,417 citations recorded by the source corpus.

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “This is a difficult problem with different facets and constraints: binding kinetics, conformational changes (internal molecular flexibility), and chemical and geometrical atomic interaction types are part of the domain knowledge describing ligand-protein binding mechanisms (Du et al., 2016).”
Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management (2023, background): “Furthermore, amidohydrolase superfamily hydrolysis processes the protein to maintain its stiffness, therefore docking molecules that match transition states should yield a stronger signal than docking substrates 38 .”
Using PyMOL as a platform for computational drug design (2017, background): “12 Ligand-mediated signal transmission via molecular complementarity is essential to all life processes; these chemical interactions comprise biological recognition in atomic level.”