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

Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
12.6K
Ladder Diagrams: Complexation Equilibria01:07

Ladder Diagrams: Complexation Equilibria

399
Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...
399
Ligand Binding Sites02:40

Ligand Binding Sites

13.0K
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...
13.0K
Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
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...
4.3K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.1K
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:
13.1K

You might also read

Related Articles

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

Sort by
Same author

The Fossilized Birth-Death Model Is Identifiable.

Systematic biology·2024
Same author

Estimating the mean in the space of ranked phylogenetic trees.

Bioinformatics (Oxford, England)·2024
Same author

Ranked Subtree Prune and Regraft.

Bulletin of mathematical biology·2024
Same author

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.

Journal of computational biology : a journal of computational molecular cell biology·2022
Same author

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution.

Molecular biology and evolution·2022
Same author

Tuberous sclerosis complex: a complex case.

Cold Spring Harbor molecular case studies·2022
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Aug 15, 2025

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects
13:57

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Published on: February 18, 2014

29.5K

A fast lasso-based method for inferring higher-order interactions.

Kieran Elmes1,2, Astra Heywood3, Zhiyi Huang1

  • 1Department of Computer Science, University of Otago, Dunedin, New Zealand.

Plos Computational Biology
|December 29, 2022
PubMed
Summary
This summary is machine-generated.

We developed Pint, a new method for analyzing large-scale genetic data. Pint can identify three-way gene interactions, improving our understanding of complex traits like antibiotic resistance and cell proliferation.

More Related Videos

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.3K
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

7.4K

Related Experiment Videos

Last Updated: Aug 15, 2025

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects
13:57

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Published on: February 18, 2014

29.5K
Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.3K
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

7.4K

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale genotype-phenotype screens are crucial for identifying molecular alterations linked to phenotypes.
  • Epistatic effects, particularly gene-gene interactions, are vital in association studies and have implications in areas like antimicrobial resistance.
  • Current tools for exome-wide screens primarily focus on pairwise interactions, lacking the capacity to analyze three-way interactions.

Purpose of the Study:

  • To develop and validate a novel computational method, Pint, capable of analyzing three-way gene interactions in large-scale genetic datasets.
  • To improve the performance and scalability of methods for detecting complex genetic interactions.
  • To apply the developed method to real-world biological data, including antibiotic resistance and siRNA perturbation screens.

Main Methods:

  • Development of the Pint algorithm, an extension of state-of-the-art methods to incorporate three-way interaction analysis.
  • Application of Pint to simulated datasets to assess its performance against existing methods.
  • Validation of Pint on real-world datasets from antibiotic resistance testing and siRNA perturbation screens.

Main Results:

  • Pint demonstrated superior performance compared to known methods on simulated data.
  • The method successfully identified biologically plausible gene effects in both antibiotic resistance and siRNA perturbation models.
  • A specific combination of known tumor suppressor genes was identified by Pint as potentially increasing cell proliferation.

Conclusions:

  • Pint offers a significant advancement in the analysis of large-scale genetic data by enabling the detection of three-way interactions.
  • The method has broad applicability in fields ranging from microbial genetics to human disease research.
  • Pint provides a powerful tool for uncovering complex genetic architectures underlying various biological phenotypes.