Proteins are components of vast interaction networks called interactomes. These complex maps involve the complete set of macromolecular interactions occurring within a cell (1). The study of interactomes and the binding affinity between molecules can provide very useful insight that enables us to understand cellular pathways. 

New and more sensitive detection techniques are allowing scientists to approach their research differently, obtain better and more accurate results, and consequently develop more effective therapies for the treatment of human diseases (1).

Understanding Binding Affinity

A ligand is a molecule that reversibly binds to a site on a target protein. This triggers responsive signals. Ligands can function as inhibitors or activators, behave as substrates or neurotransmitters, and operate in response to different stimuli and environmental conditions (1).

Binding affinity is defined as the interaction of ligands with their binding sites. Intermolecular forces such as Van der Waals forces and ionic and hydrogen bonds are responsible for these interactions. The instance of binding is transient and occurs in an infinitesimal range of time and space (2). The only possible way to understand the dynamic of the interactomes is by analyzing the rate at which the binding occurs.

The Equilibrium Dissociation Constant

Being able to predict protein-protein interactions is a key goal of computational structural biology, and science is constantly trying to improve the accuracy of protein-ligand affinity prediction (2).

The equilibrium dissociation constant (Kd) represents the binding affinity between a ligand, called an agonist, and its respective receptor. Microscale thermophoresis technology allows scientists to quantify this measurable value and determine the strength of the interactions between a fluorescently labeled or intrinsically fluorescent sample and a binding partner (the ligand). The lower the Kd, the higher the strength of the binding interaction between two molecules. In other words, high-affinity ligand binding implies that a relatively low concentration of a ligand is adequate for triggering a physiological response. High-affinity ligand binding means stronger intermolecular forces between the ligand and its receptor.

Importance of Binding Affinity in Pharmacology

Binding affinity data is crucially relevant for pharmacology in the development and betterment of therapeutic drugs. Ligand efficacy refers to the ability of the ligand to produce a biological response upon binding to the target receptor (3). Drugs used in therapies behave as ligands, and the measurement of their affinity indicates the strength of the effect in the patient. Selective drugs target specific receptors that will be more specific than non-selective drugs, reducing the chances of adverse effects.

Another goal in the pharmacology sector is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands (2). Quantification of these interactions allows scientists to draw interactomes. If an interactome is partially known, new interactions may be predicted from known studied network structure, as interacting proteins tend to share interaction partners (4). These insights can then be translated into better treatment drugs.

Scientists have seen in the last decade that a human disease is rarely the consequence of an isolated abnormality in a particular gene, but rather, the outcome of complex perturbations of the underlying cellular network (5). In fact, interactomes become dynamic due to their environment and external or internal changes, which means that diseases can also affect these pathways. This makes correct mapping a very difficult; a job that makes precision technology the best option for handling these obstacles.

  1. Snider J., Kotlyar M., Saraon P., Yao Z., Jurisica I., Stagljar I (2015). Fundamentals of protein interaction network mapping. Mol Syst Biol. 2015 Dec; 11(12): 848. Published online 2015 Dec 17. doi:  10.15252/msb.20156351
  2. Dias R., Kolaczwosky B. (2017). Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data. BMC Bioinformatics. 2017; 18(Suppl 5): 102. Published online 2017 Mar 23. doi:  10.1186/s12859-017-1533-z
  3. Kenakin, Terrance P. (November 2006). A pharmacology primer: theory, applications and methods. Academic Press. p. 79. ISBN 978-0-12-370599-0.
  4. Saito R, Suzuki H, Hayashizaki Y (2002) Interaction generality, a measurement to assess the reliability of a protein‐protein interaction. Nucleic Acids Res 30: 1163–1168
  5. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68. doi: 10.1038/nrg2918.

About The Author

Jeffrey Elder