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

4.2K
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,...
4.2K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.0K
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...
14.0K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

14.3K
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:
14.3K

You might also read

Related Articles

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

Sort by
Same author

Spotlight on sensors as tools for biointelligence.

Analytical and bioanalytical chemistry·2026
Same author

Optical imaging approaches for biosensing applications.

Analytical and bioanalytical chemistry·2025
Same author

Reflectometric-based sensor arrays for the screening of kinase-inhibitor interactions and kinetic determination.

Analytical and bioanalytical chemistry·2025
Same author

Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers.

Biochimica et biophysica acta. Biomembranes·2023
Same author

(R)evolution of the Standard Addition Procedure for Immunoassays.

Biosensors·2023
Same author

Tools to compare antibody gold nanoparticle conjugates for a small molecule immunoassay.

Mikrochimica acta·2023

Related Experiment Video

Updated: Oct 20, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K

Comparison of methods for quantitative biomolecular interaction analysis.

Monika Conrad1, Peter Fechner2, Günther Proll2

  • 1Institute of Physical and Theoretical Chemistry (IPTC), Eberhard Karls Universität Tübingen, Auf der Morgenstelle 18, 72076, Tübingen, Germany. monika.conrad@uni-tuebingen.de.

Analytical and Bioanalytical Chemistry
|September 10, 2021
PubMed
Summary
This summary is machine-generated.

This study compares mathematical approaches for analyzing biomolecular interaction analysis (BIA) data to determine kinetic rate constants. It highlights the importance of robust evaluation methods for accurate kinetic experiments.

Keywords:
Association rate constantBinding kineticsBiomolecular interaction analysisPseudo-first-order kineticsReflectometric interference spectroscopy

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

16.5K
A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation
14:44

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

20.7K

Related Experiment Videos

Last Updated: Oct 20, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K
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

16.5K
A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation
14:44

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

20.7K

Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Biophysics

Background:

  • Accurate kinetic experiments require optimized experimental conditions and evaluation procedures.
  • Biomolecular interaction analysis (BIA) is crucial for understanding molecular interactions.
  • Existing evaluation methods may lack transparency, relying on 'black box' software.

Purpose of the Study:

  • To compare different mathematical approaches and algorithms for determining kinetic rate constants in BIA.
  • To evaluate the performance of these methods using both simulated and real-world data.
  • To provide a detailed analysis of the advantages and disadvantages of each approach.

Main Methods:

  • Comparison of five mathematical approaches for evaluating binding curves under pseudo-first-order kinetics.
  • Application of algorithms to simulated data with varying noise levels.
  • Evaluation of reflectometric interference spectroscopy (RIfS) measurements for antibody-antigen binding kinetics.

Main Results:

  • Performance of different kinetic evaluation algorithms varied depending on data quality and noise levels.
  • Specific advantages and disadvantages were identified for each mathematical approach.
  • Real-world RIfS data demonstrated the practical applicability of the evaluated methods.

Conclusions:

  • Awareness raised on the critical need for careful evaluation of BIA data.
  • Demonstrated that multiple approaches can yield reliable kinetic rate constants.
  • Encourages researchers to utilize diverse evaluation strategies beyond commercial software for BIA data analysis.