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Ligand Binding Sites

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 Sites

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 and Linkage

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 Linkage

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 Interaction

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...

The Two-State Receptor Model

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...

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Related Experiment Video

Updated: May 30, 2026

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

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

Published on: April 3, 2026

Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network.

Ammar Abdo¹, Naomie Salim, Ali Ahmed

¹Information System Department, Universiti Teknologi Malaysia, Skudai, Malaysia. ammar_utm@yahoo.com

Journal of Biomolecular Screening

|August 25, 2011

Summary

Researchers enhanced the Bayesian inference network (BIN) for similarity-based virtual screening using relevance feedback. This improves retrieval effectiveness and cost-efficiency, particularly for diverse datasets in drug discovery.

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Area of Science:

Chemoinformatics
Computational Chemistry
Drug Discovery

Background:

Similarity-based virtual screening is crucial for identifying drug candidates.
Existing methods can be limited in retrieval effectiveness and cost-efficiency.
Bayesian networks offer a promising alternative for virtual screening.

Purpose of the Study:

To enhance the Bayesian inference network (BIN) for improved virtual screening.
To integrate relevance feedback into BIN for better performance.
To increase the cost-effectiveness of ligand-based virtual screening.

Main Methods:

Enhanced the Bayesian inference network (BIN) by incorporating relevance feedback.
Filtered high-ranking structures to create a set of active reference structures.
Applied two techniques: reweighting fragments and group fusion for BIN searching.

Main Results:

The enhanced BIN demonstrated improved retrieval effectiveness.
Proposed techniques enhance the cost-effectiveness of virtual screening searches.
Effectiveness was particularly notable for higher diversity datasets.

Conclusions:

Relevance feedback integration significantly enhances BIN performance.
The developed methods offer a simple and effective way to improve virtual screening.
This approach holds potential for more efficient drug discovery pipelines.