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Related Concept Videos

Hybridoma Technology01:31

Hybridoma Technology

17.2K
Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...
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Antibody Structure and Classes01:25

Antibody Structure and Classes

8.1K
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
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Antibody Structure01:10

Antibody Structure

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Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
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B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...
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Related Experiment Video

Updated: Jan 10, 2026

Generation of Murine Monoclonal Antibodies by Hybridoma Technology
09:42

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

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Energy-based generative models for monoclonal antibodies.

Paul Pereira1,2, Hervé Minoux2, Aleksandra M Walczak1

  • 1Laboratoire de Physique de l'École Normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France.

Mabs
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Energy-based generative models optimize monoclonal antibodies for drug development. This approach balances antibody affinity, solubility, and humanness, addressing key challenges in antibody engineering.

Keywords:
AI modelsComputational designmulti-objective optimization

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Last Updated: Jan 10, 2026

Generation of Murine Monoclonal Antibodies by Hybridoma Technology
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Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • Antibody therapeutics are crucial in treating various diseases, with 162 approved since 1986.
  • Antibody drug discovery is complex, costly, and time-consuming, requiring extensive optimization.
  • Key optimization goals include enhancing target affinity and crucial biophysical properties like solubility and stability.

Purpose of the Study:

  • To explore energy-based generative models for optimizing monoclonal antibodies.
  • To address the multi-optimization challenge in antibody development, focusing on affinity, solubility, and humanness.
  • To identify and navigate trade-offs between these critical antibody properties.

Main Methods:

  • Development and application of an energy-based generative model.
  • Optimization of a candidate monoclonal antibody for multiple properties simultaneously.
  • Analysis of Pareto fronts to identify optimal trade-offs between affinity, solubility, and humanness.

Main Results:

  • The generative model successfully identified trade-offs in optimizing antibody properties.
  • Candidate antibodies were generated that represent optimal solutions on the Pareto front.
  • Demonstrated the utility of generative models in antibody engineering for drug development.

Conclusions:

  • Energy-based generative models offer a powerful approach to streamline antibody optimization.
  • Balancing multiple properties like affinity, solubility, and humanness is achievable with advanced computational methods.
  • This methodology has the potential to accelerate the development of safer and more effective antibody therapeutics.