A mathematical framework for the classification of
Giuliana Indelicato, University of Torino
In the last decade scientists have started engineering many
self-assembling protein nanoparticles. The possible applications are
manifold and innovative: from vectors for the delivery of genes or
synthetic vaccines. The fast development of this research field
challenges for the structural analysis as, in general, synthetic
nanoparticles do not fit into the Caspar and Klug’s framework: an
approach is needed to provide blueprints for the experimental
of their structure.
We focus on a *de novo *class of proteins, referred to as
protein nanoparticles (SAPNs). This is a family of nanoparticles
provide a versatile platform for synthetic vaccines against a broad
of diseases, including malaria, SARS, influenza and HIV.
These nanoparticles self assemble from multiple copies of a
synthesized to have specific connectivity properties. The
to each other following precise local assembly rules, forming groups
five or three polypeptides. To elucidate the resulting assembled
structure, we use tools from graph theory. This approach unveils a
relation with fullerene geometries and enables a full classification
high and low symmetry particles observed in experiments.