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Related Concept Videos

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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
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...

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A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
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A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

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Evaluation of a Bayesian inference network for ligand-based virtual screening.

Beining Chen1, Christoph Mueller, Peter Willett

  • 1Krebs Institute for Biomolecular Research, Departments of Chemistry and of Information Studies, University of Sheffield, Sheffield, S10 2TN, UK. p.willett@sheffield.ac.uk.

Journal of Cheminformatics
|March 20, 2010
PubMed
Summary

Bayesian inference networks can predict bioactivity for chemical similarity searching. They are effective for structurally similar compounds but less so for diverse sets.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Bioinformatics

Background:

  • Bayesian inference networks calculate event probabilities and have ranked documents by relevance.
  • This study adapts Bayesian networks for chemical similarity searching to rank databases by bioactivity probability.

Purpose of the Study:

  • To evaluate Bayesian inference networks for ligand-based virtual screening.
  • To compare their performance against traditional Tanimoto-based similarity searching.

Main Methods:

  • Implemented Bayesian inference networks using two network types and four belief function types.
  • Tested performance on the MDDR and WOMBAT chemical databases.

Main Results:

  • Bayesian networks effectively screened for structurally homogeneous active molecules, outperforming Tanimoto searching.
  • Performance decreased significantly when searching for structurally heterogeneous active molecules.

Conclusions:

  • Bayesian inference networks offer a viable alternative for ligand-based virtual screening.
  • Their utility is highest for datasets with high structural homogeneity.