Classification of self-assembling protein nanoparticle architectures for applications in vaccine design

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Self-assembling protein nanoparticles (SAPNs) are versatile platforms for antigen display.
• Their unique geometries arise from peptide building blocks forming trimeric and pentameric clusters.
• Understanding SAPN structure is crucial for optimizing their use in vaccine development.

Purpose of the Study:

• To introduce a mathematical procedure for the structural classification of specific SAPNs.
• To provide a comprehensive atlas of SAPN morphologies based on their assembly principles.
• To enable prediction of B-cell epitope density for improved vaccine design.

Main Methods:

• Describing SAPN architectures using bipartite (3,5)-regular graphs.
• Leveraging the mathematical relationship between SAPN graphs and fullerene graphs.
• Developing a classification system for diverse SAPN structures.

Main Results:

• A complete atlas of self-assembling protein nanoparticle morphologies was generated.
• The classification accurately describes the spectrum of possible SAPN geometries.
• Mathematical models predict the density of B-cell epitopes on SAPN surfaces.

Conclusions:

• The developed mathematical classification provides a systematic approach to understanding SAPN structures.
• This toolkit facilitates bioengineering applications, particularly in rational vaccine design.
• Predicting epitope density is key to eliciting a potent humoral immune response.