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

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 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:

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

You might also read

Related Articles

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

Sort by

Same author

AbCVista: a deep learning framework for predicting antibody conformational ensembles.

Protein & cell·2026

Same author

Mechanical behavior of cemented paste backfill under the coupled effect of loading rate and moisture content.

PloS one·2026

Same author

CB-Dock3: an enhanced web server for protein-ligand blind docking.

Nucleic acids research·2026

Same author

Targeted inhibition of FBXL2 confers susceptibility of HER2-negative breast cancer to trastuzumab deruxtecan.

Nature cancer·2026

Same author

Energy stress-induced PKCζ S-glutathionylation is essential for LKB1 cytoplasmic translocation and AMPK activation.

Life metabolism·2025

Same author

Natural product chelerythrine induces plant resistance through activating defensive reactions.

Pest management science·2025

Search research articles

Related Experiment Video

Updated: Nov 30, 2025

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

LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening.

Maximilian Grimm¹, Yang Liu¹, Xiaocong Yang¹

¹Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China.

Journal of Chemical Information and Modeling

|November 16, 2020

Summary

LigMate integrates multiple molecular descriptors for improved ligand-similarity virtual screening. This novel approach enhances drug design by combining features like maximum common substructures and stereoelectronic properties for more precise screening.

More Related Videos

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

Related Experiment Videos

Last Updated: Nov 30, 2025

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

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Ligand-similarity-based virtual screening is crucial in computer-aided drug design.
Current methods often use molecular descriptors independently, limiting screening precision.
There is a need for integrated approaches to enhance virtual screening effectiveness.

Purpose of the Study:

To develop and validate LigMate, a multifeature integration algorithm for virtual screening.
To improve the accuracy and efficiency of ligand-similarity-based virtual screening.
To introduce novel descriptors for enhanced molecular feature representation.

Main Methods:

Developed LigMate, a multifeature integration algorithm.
Employed Hungarian algorithm-based matching and machine learning for descriptor combination.
Integrated traditional descriptors with new features: maximum common substructure score (MCSS), intraligand distance score (ILDS), and ring score (RS).

Main Results:

LigMate achieved high performance on benchmark datasets (DUD-E and MUV).
Demonstrated superior enrichment factor (EF1) and area under the curve (AUC) compared to single-descriptor methods.
EF1 of 36.14 and AUC of 0.81 on DUD-E; EF1 of 15.44 and AUC of 0.69 on MUV.

Conclusions:

The study presents a novel framework for integrating multiple molecular descriptors.
LigMate offers a significant advancement for ligand-similarity-based virtual screening.
The developed algorithm provides a more effective and precise approach to computer-aided drug design.