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Related Concept Videos

Protein Networks02:26

Protein Networks

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,...
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...

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Related Experiment Video

Updated: May 28, 2026

Pulldown Assay Coupled with Co-Expression in Bacteria Cells as a Time-Efficient Tool for Testing Challenging Protein-Protein Interactions
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Pulldown Assay Coupled with Co-Expression in Bacteria Cells as a Time-Efficient Tool for Testing Challenging Protein-Protein Interactions

Published on: December 23, 2022

Optimized Quantitative Bacterial Two-Hybrid (qB2H) for Protein-Protein Interaction Assessment.

Antoine Guyot1,2,3, Emma Maillard4, Kelly Ferreira-Pinto2

  • 1Sanofi Large Molecule Research, Vitry-sur-Seine, France.

Computational and Structural Biotechnology Journal
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed quantitative bacterial two-hybrid (qB2H) systems to accurately measure protein-protein interactions (PPIs). This improved method enables precise interface mapping and protein engineering for scientific discovery.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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

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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

Area of Science:

  • Biochemistry and Molecular Biology
  • Protein Engineering
  • Computational Biology

Background:

  • Characterizing mutation effects on protein-protein interactions (PPIs) is vital for understanding protein structure and function.
  • Massively parallel analyses, like deep mutational scanning, generate data for machine learning but rely on reliable quantitative methods.
  • Existing bacterial two-hybrid (B2H) systems have limitations affecting accurate dataset generation for PPI variant analysis.

Purpose of the Study:

  • To engineer and benchmark optimized quantitative B2H (qB2H) systems for reliable and accurate PPI variant analysis.
  • To demonstrate the utility of qB2H in interface mapping and protein binder optimization.
  • To provide R&D scientists with a robust platform for quantitative PPI analysis and data-driven discovery.

Main Methods:

  • Engineered and benchmarked optimized quantitative B2H (qB2H) alternatives.
  • Developed strain-independent assays with improved metrics for high-quality dataset generation.
  • Applied qB2H for interface mapping, perturbation analysis, and integration with AI-based protein design.

Main Results:

  • Engineered qB2H systems overcome limitations of existing B2H methods, enabling accurate PPI variant dataset generation.
  • Perturbation analysis of single-site variants accurately identified known antisilencing function 1 (ASF1) complex contact positions, consistent with crystallographic data.
  • Integration with generative AI yielded an ASF1-binding peptide with a 70-fold affinity increase, showcasing qB2H's application in protein engineering.

Conclusions:

  • The developed qB2H platform offers a robust and reusable solution for quantitative PPI analysis.
  • qB2H facilitates both rational protein engineering and data-driven discovery by providing high-quality PPI data.
  • The study makes code, data, and materials available, promoting community access and further research in PPI analysis.