Dilatable DNA origami nanopores as nuclear pore mimics
Bertosin, E.; Van Herck, B.; Barth, A.; Wang, S.; Dekker, C.
Show abstract
The nuclear pore complex (NPC) is a massive protein system that controls all nucleocytoplasmic transport via a dynamic network of intrinsically disordered proteins called FG-Nups. We developed an NPC-mimicking DNA origami nanostructure that self-assembles into a tetrameric, octagonal nanopore with an inner diameter that, through addition of DNA oligonucleotides, can be tuned in a user-defined fashion from 57 to 66 nm - mimicking the contracted and dilated states of NPCs. We demonstrated that these origami nanopores can dilate within minutes and recontract at slower timescales. Both contracted and dilated pores can be inserted into lipid bilayers, where they maintain their conformation and allow transmembrane transport of fluorescent proteins. With outer diameters up to 87 nm, these are, to our knowledge, the largest DNA origami nanopores inserted into lipid membranes. Functionalization of the pores with the FG-Nup Nsp1 reduces non-specific cargo diffusion across the lipid membrane while facilitating translocation of the transport receptor Kap95, demonstrating transport selectivity much like biological NPCs. The work establishes a size-tunable platform to dissect nuclear transport mechanisms, with potential applications for engineering programmable artificial channels.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Reconstitution of ultrawide DNA origami pores in liposomes for transmembrane transport of macromolecules 96%
- Graphene-enabled, spatially controlled electroporation of adherent cells for live-cell super-resolution microscopy 95%
- Rapid exchange of stably bound protein and DNA cargo on a DNA origami receptor 95%
Similar papers in this journal
Similar papers in this journal
"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.