Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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...
Protein-protein Interfaces02:04

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 polypeptide...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

You might also read

Related Articles

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

Sort by
Same author

Computational Resources for Molecular Biology 2026.

Journal of molecular biology·2026
Same author

3DSeqCheck: A Web-based Tool for Verifying Sequence Consistency Between a 3D Structure File and the Corresponding UniProt Entry.

Journal of molecular biology·2026
Same author

The aging of the AlphaFold database.

Nature structural & molecular biology·2025
Same author

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.

Machine learning·2025
Same author

Computational Resources for Molecular Biology 2025.

Journal of molecular biology·2025
Same author

Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.

Journal of molecular biology·2025
Same journal

Combining bacterial display and protein language models to engineer a CD69-binding affibody for molecular imaging of immune activation.

Protein engineering, design & selection : PEDS·2026
Same journal

Examining selection dynamics and limitations in multi-round protein selection of high diversity libraries.

Protein engineering, design & selection : PEDS·2026
Same journal

A photo-enhanced oxidative coupling for site-specific protein Labeling via noncanonical amino acid incorporation.

Protein engineering, design & selection : PEDS·2026
Same journal

Engineering affibody domains as anti-idiotypic masks for nivolumab-based prodrugs.

Protein engineering, design & selection : PEDS·2026
Same journal

Integrating machine learning tools in protein design: a case of MHETase engineering for PET biodeconstruction.

Protein engineering, design & selection : PEDS·2026
Same journal

Computational redesign of a thermostable T7 RNA polymerase.

Protein engineering, design & selection : PEDS·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Discovering rules for protein-ligand specificity using support vector inductive logic programming.

Lawrence A Kelley1, Paul J Shrimpton, Stephen H Muggleton

  • 1Structural Bioinformatics Group, Division of Molecular Biosciences, Imperial College London, London, UK. l.a.kelley@imperial.ac.uk

Protein Engineering, Design & Selection : PEDS
|July 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning method to identify protein binding pocket features crucial for ligand specificity. The approach accurately distinguishes between pockets binding FAD and NAD, aiding in protein function prediction and design.

More Related Videos

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Jun 21, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Structural genomics and comparative modeling generate numerous protein structures, but function determination remains a challenge.
  • Understanding the relationship between protein structure (specifically binding pockets) and biological function is critical for prediction and design.
  • High-throughput methods are needed to infer protein function directly from structural data.

Purpose of the Study:

  • To develop a general method for discovering structural features that confer ligand binding specificity.
  • To predict protein function based on binding pocket characteristics.
  • To enable protein functional design by learning specificity rules.

Main Methods:

  • Utilized a machine learning technique combining inductive logic programming and support vector machines.
  • Analyzed protein binding pocket geometry and composition without employing molecular docking.
  • Developed a general representation applicable to various discriminatory binding problems.

Main Results:

  • Achieved high precision (90%) and recall (86%) in discriminating between FAD and NAD binding pockets.
  • Identified specific rules governing binding pocket specificity.
  • Discovered potential links between binding pocket features and ligand conformational freedom.

Conclusions:

  • The developed method effectively predicts ligand specificity from protein binding pocket structure.
  • The learned rules provide insights for protein functional design.
  • The approach is generalizable to other ligand-binding specificity problems.