Back

Characterization of Single Ribosomes and Virus Particles with Large-Diameter Perfringolysin O Nanopores

Vracar, A.; Salyahetdinova, V.; Balog, S.; Ianiro, A.; Mukhopadhyay, A.; Mayer, M.

2026-09-17 biophysics
10.64898/2026.09.16.752082 bioRxiv
Show abstract

Existing biological nanopores are often too narrow to accommodate large proteins and biomolecular complexes in their native folded states, precluding the analysis of megadalton assemblies. Here, we report the self-assembly of the cholesterol-dependent cytolysin Perfringolysin O (PFO) into stable transmembrane nanopores composed of 46 {+/-} 9 monomers, with an inner pore diameter of 28.5 {+/-} 5.6 nm and a length of 9.8 nm. We demonstrate that despite the large size of the pore assembly, PFO pores exhibit a stable open-pore current with a high signal-to-noise ratio, making them suitable for resistive-pulse recordings. Moreover, PFO nanopores enable accurate, calibration-free, and label-free sizing of individual proteins, multi-protein complexes, and viral particles across a large molecular-weight range spanning 50 kDa to 3.4 MDa. The exceptionally large diameter of these pores enables, for the first time, resistive-pulse-based characterization of intact virus particles and ribosomes with a biological nanopore. Specifically, we determined the volume, shape, and diameter of complete capsids of recombinant adeno-associated virus serotype 2 (rAAV2) as well as capsid fragments. Finally, simultaneous analysis of molecular volume and shape resolved intact 70S ribosomes from their dissociated 30S and 50S subunits in a mixture. By extending biological nanopore sensing to single-particle characterization of large protein complexes well beyond the reach of existing pores, this approach introduces PFO nanopores as a versatile platform for label-free, single-particle analysis in solution.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.