Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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

The Equilibrium Binding Constant and Binding Strength

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 Linkage

Ligand Binding and Linkage

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 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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The Distribution of Average Pairwise Distances Among Human Pre-miRNAs for Disease Association Analysis.

International journal of molecular sciences·2026

Same author

Three-Dimensional Bioprinting and Rose-Inspired Medical Applications.

Biomimetics (Basel, Switzerland)·2026

Same author

Editorial: Neuroimaging innovations for encephalitis, neuroinfectious diseases, and neuroinflammation.

Frontiers in neurology·2025

Same author

Electroencephalography, microRNA, and anti-NMDA receptor encephalitis.

Clinica chimica acta; international journal of clinical chemistry·2025

Same author

Medical Benefits and Polymer Applications of Grapes.

Polymers·2025

Same author

Distribution of microRNA Counts Across Human Chromosomes.

Current medicinal chemistry·2024

Same journal

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 19, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Logistic Regression Method for Ligand Discovery.

Chian Chen¹, Hsiuying Wang²

¹Institutes of Population Health Sciences, National Health Research, Zhunan, Taiwan.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|September 24, 2019

Summary

This summary is machine-generated.

This study introduces a novel protein-specific scoring approach for drug discovery. The logistic regression method enhances ligand selection accuracy in protein-based virtual screening, improving enrichment factors for most targets.

Keywords:

docking logistic regression protein-specific scoring method

More Related Videos

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Published on: September 13, 2014

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Related Experiment Videos

Last Updated: Jan 19, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Published on: September 13, 2014

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Protein-based virtual screening is crucial for identifying drug candidates.
Docking programs, reliant on scoring functions, are key tools in this process.
Current scoring functions often lack protein-specific characteristics, limiting accuracy.

Purpose of the Study:

To develop an improved protein-specific scoring approach for virtual screening.
To enhance the selection of potential drug ligands.
To increase the accuracy and efficiency of the drug discovery pipeline.

Main Methods:

Utilized logistic regression analysis for a protein-specific scoring model.
Evaluated the method using the Directory of Useful Decoys (DUD) dataset.
Tested performance across 40 diverse protein targets.

Main Results:

The proposed logistic regression-based scoring approach demonstrated improved performance.
Increased enrichment factors were observed for the majority of the 40 protein targets.
The method shows potential for more effective virtual screening.

Conclusions:

Protein-specific scoring functions significantly enhance virtual screening efficacy.
Logistic regression offers a robust framework for developing these specialized scoring functions.
This approach represents a valuable advancement in computational drug discovery.