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Related Concept Videos

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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Ymir: A 3D structural affinity model for multi-epitope vaccine simulations.

Philippe A Robert1, Theinmozhi Arulraj1, Michael Meyer-Hermann1,2,3,4

  • 1Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany.

Iscience
|September 6, 2021
PubMed
Summary

A new computational framework, Ymir, models antibody-antigen interactions to predict vaccine breadth. This approach accounts for 3D antigen structure, improving vaccine design by identifying structurally related epitopes.

Keywords:
Biocomputational methodComputer simulationImmunologyMolecular Structure

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Area of Science:

  • Immunology
  • Computational Biology
  • Vaccinology

Background:

  • Vaccine development faces challenges from epitope immunodominance and pathogen mutations leading to antigenic variants.
  • Current computational methods for assessing antibody responses to multivalent antigens lack 3D structural considerations and accurate prediction of antibody breadth.
  • Germinal center dynamics, crucial for antibody response strength and breadth, depend on antigen structure and B cell selection.

Purpose of the Study:

  • To introduce Ymir, a novel 3D-lattice-based computational framework for calculating in silico antibody-antigen affinities.
  • To address the limitations of existing methods in reflecting antigen 3D structure and predicting antibody breadth.
  • To provide a new tool for understanding vaccine potency based on structural relationships between vaccine antigens.

Main Methods:

  • Development of Ymir, a 3D-lattice-based computational framework.
  • Calculation of in silico antibody-antigen affinities using the Ymir framework.
  • Validation of Ymir by replicating known germinal center dynamics and predicting antibody breadth.

Main Results:

  • Ymir naturally simulates key physiological properties including affinity jumps, cross-reactivity, and differential epitope accessibility.
  • Validation confirmed Ymir's ability to replicate known germinal center dynamics.
  • The study demonstrated that combining antigens with mutated yet structurally related epitopes enhances vaccine breadth.

Conclusions:

  • Ymir offers a new computational approach to assess antibody-antigen affinities and predict vaccine breadth.
  • The framework's ability to incorporate 3D antigen structure improves the understanding of germinal center responses.
  • Strategically combining structurally related epitopes can enhance vaccine potency and breadth, paving the way for improved vaccine design.