An interaction network approach predicts protein cage architectures in bionanotechnology

TL;DR

This paper introduces an interaction network method to predict and rationalize the structures of protein nanocages, including novel architectures, enhancing control over nanoparticle design in bionanotechnology.

Contribution

The study presents a new approach leveraging local interaction data to infer geometric principles of protein cage assembly, especially for non-quasi-equivalent structures.

Findings

Successfully predicts known protein cage structures.

Identifies new viable cage geometries.

Provides a framework for programmable nanoparticle design.

Abstract

Protein nanoparticles play pivotal roles in many areas of bionanotechnology, including drug delivery, vaccination and diagnostics. These technologies require control over the distinct particle morphologies that protein nanocontainers can adopt during self-assembly from their constituent protein components. The geometric construction principle of virus-derived protein cages is by now fairly well understood by analogy to viral protein shells in terms of Caspar and Klug's quasi-equivalence principle. However, many artificial, or genetically modified, protein containers violate this principle, because identical protein subunits do not interact in quasi-equivalent ways, leading to gaps in the particle surface. We introduce a method that exploits information on the local interactions between the assembly units, called capsomers, to infer the geometric construction principle of these nanoparticle architectures. The predictive power of this approach is demonstrated here for a prominent system in nanotechnology, the AaLS pentamer. Our method not only rationalises hitherto discovered cage structures, but also predicts geometrically viable options that have not yet been observed. The classification of nanoparticle architecture based on the geometric properties of the interaction network closes a gap in our current understanding of protein container structure and can be widely applied in protein nanotechnology, paving the way to programmable control over particle polymorphism.