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Related Concept Videos

The Two-State Receptor Model01:29

The Two-State Receptor Model

The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with one...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Drug-Receptor Bonds

Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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Updated: May 30, 2026

A BW Reporter System for Studying Receptor-Ligand Interactions
06:05

A BW Reporter System for Studying Receptor-Ligand Interactions

Published on: January 7, 2019

Predicting receptor-ligand pairs through kernel learning.

Ernesto Iacucci1, Fabian Ojeda, Bart De Moor

  • 1SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium.

BMC Bioinformatics
|August 13, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new kernel learning method to predict receptor-ligand pairs using multiple data sources. The approach significantly improves prediction accuracy for the tgfβ family compared to existing methods.

Related Experiment Videos

Last Updated: May 30, 2026

A BW Reporter System for Studying Receptor-Ligand Interactions
06:05

A BW Reporter System for Studying Receptor-Ligand Interactions

Published on: January 7, 2019

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Cellular event regulation often begins with extracellular signaling via ligand-receptor interactions.
  • Identifying these receptor-ligand pairs is crucial for understanding protein-protein interactions (PPI).

Purpose of the Study:

  • To develop and evaluate a novel computational method for predicting receptor-ligand pairs.
  • To assess the performance of this method against existing approaches using benchmark datasets.

Main Methods:

  • A combined kernel classifier was developed integrating disparate data sources: expression data, domain content, and phylogenetic profiles.
  • Performance was evaluated against the Database of Ligand-Receptor Partners (DLRP) and the Gertz et al. method.

Main Results:

  • The combined kernel classifier accurately reconstructed over 76% of supported edges for the tgfβ family receptor-ligand graph (0.76 recall, 0.67 precision).
  • For the tgfβ family, the new method achieved an F-measure of 0.71, a 1.5-fold improvement over the Gertz et al. method's F-measure of 0.48.

Conclusions:

  • Predicting receptor-ligand pairings is a complex challenge in bioinformatics.
  • Kernel learning on multiple data sources offers a robust and improved alternative for receptor-ligand prediction.