Observation of Aerosolization-induced Morphological Changes in Viral Capsids

TL;DR

This study uses advanced imaging and machine learning to observe how viral capsids change shape during aerosolization, revealing a buckling transition that helps viruses survive dehydration.

Contribution

It provides the first experimental evidence of aerosolization-induced morphological changes in viral capsids and proposes a molecular mechanism for buckling.

Findings

Identification of a new compact capsid conformation

Observation of intermediate structures during buckling

Proposal of a destabilization mechanism involving a 19-residue loop

Abstract

Single-stranded RNA viruses co-assemble their capsid with the genome and variations in capsid structures can have significant functional relevance. In particular, viruses need to respond to a dehydrating environment to prevent genomic degradation and remain active upon rehydration. Theoretical work has predicted low-energy buckling transitions in icosahedral capsids which could protect the virus from further dehydration. However, there has been no direct experimental evidence, nor molecular mechanism, for such behaviour. Here we observe this transition using X-ray single particle imaging of MS2 bacteriophages after aerosolization. Using a combination of machine learning tools, we classify hundreds of thousands of single particle diffraction patterns to learn the structural landscape of the capsid morphology as a function of time spent in the aerosol phase. We found a previously unreported compact conformation as well as intermediate structures which suggest an incoherent buckling transition which does not preserve icosahedral symmetry. Finally, we propose a mechanism of this buckling, where a single 19-residue loop is destabilised, leading to the large observed morphology change. Our results provide experimental evidence for a mechanism by which viral capsids protect themselves from dehydration. In the process, these findings also demonstrate the power of single particle X-ray imaging and machine learning methods in studying biomolecular structural dynamics.