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Chemoselective Modification of Viral Surfaces via Bioorthogonal Click Chemistry
Published on: August 19, 2012
Mechanical tuning of virus-like particles
Milad Radiom1, Tim Keys2, Yagmur Turgay2
1Laboratory of Food Immunology, Institute of Food, Nutrition and Health, ETH Zürich, Zürich, Switzerland; Laboratory of Food and Soft Materials, Institute of Food, Nutrition and Health, ETH Zürich, Zürich, Switzerland.
Virus-like particles (VLPs) are promising vaccine scaffolds. Modifying their surface with polyethylene glycol or SARS-CoV-2 peptides alters VLP mechanical properties, impacting vaccine design.
Area of Science:
- Biophysics
- Vaccine Development
- Nanotechnology
Background:
- Virus-like particles (VLPs) are emerging as potent scaffolds for mucosal vaccine development.
- Optimal VLP performance may depend on biophysical properties beyond immunological design parameters.
Purpose of the Study:
- To investigate the mechanical properties of Acinetobacter phage AP205 virus-like particles (VLPs).
- To determine how surface modifications, such as polyethylene glycol attachment or antigenic peptide fusion, affect VLP biophysical characteristics.
Main Methods:
- Atomic force microscopy (AFM) was employed to assess VLP mechanical properties.
- Small-angle X-ray scattering (SAXS) provided insights into VLP structure.
- Mechanical properties including stiffness and elastic modulus were quantified.
Main Results:
- AP205 VLPs exhibit significant stiffness (93 ± 23 pN/nm) and an elastic modulus of 0.11 GPa.
- Polyethylene glycol conjugation reduced stiffness to 46 ± 10 pN/nm and elastic modulus to 0.05 GPa.
- Fusion of SARS-CoV-2 spike protein peptides increased stiffness to 146 ± 54 pN/nm, without altering elastic modulus.
Conclusions:
- Surface conjugation strategies can significantly modulate the biophysical properties of VLP scaffolds.
- Changes in VLP mechanical properties are linked to variations in shell thickness and coat protein net charge.
- Understanding and controlling these biophysical properties is crucial for optimizing VLP-based vaccine efficacy.
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