Skip to content

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

Sep 2026 · bioRxiv (Cold Spring Harbor Laboratory)
Nanopore and Nanochannel Transport Studies

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.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.