Record-breaking protein nanocages - Part I

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Today, I am trying something new: a series! 2 or 3 issues (I have not decided yet) around closely related papers. And the stars of this series are protein nanocages!

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Record-breaking protein nanocages

Welcome, welcome to the first-ever series in Plenty of Room history! I am stupidly excited about this: I think it’s very funny. But let’s get into it. We will be covering a couple of very cool papers about protein nanocages, and some big advancement in this field. But before we dive into the details, let’s back up a bit: what exactly are protein nanocages?

Imagine symmetrical, wireframe-like nanostructures, made entirely of proteins. These nanocages self-assemble from smaller subunits into highly ordered shapes and often resemble natural systems like viral capsids or ferritin ( an iron-transporting protein). If you have ever seen an image of a virus capsid, nanocages are similar: regular, symmetric and cool. But they are not only nice to look at: engineered nanocages have become a focus in bio-nanotech, thanks to their precision, biocompatibility and versatility. They could be used in lots of different fields, from drug delivery (remember how good viruses are at delivering nucleic acids?) to vaccine development, but also in material science and enzymatic catalysis.

The traditional design approaches have been focusing on symmetric assemblies (for example tetrahedral, octahedral, or icosahedral), which makes assembly easier, since there are few protein-protein interfaces to design. But symmetry also limits how complex or large these structures can be.

Here is where today’s paper comes in. Faced with this challenge, and wanting to create bigger and more complex nanocages, the authors decided to use pseudosymmetry as a solution. Pseudosymmetry relaxes the strict rules of symmetry, allowing for small variations. Instead of using identical subunits repeated in a symmetric pattern, the researchers used similar proteins, with slightly different sequences, creating slightly different interacting interfaces. The team developed a hierarchical computational framework to design protein nanocages, which can be broken into 3 main steps:

• Heterotrimeric building blocks: The basic subunits for the design of the nanocages are heterotrimers, complexes of 3 distinct (but similar) proteins that interact with high specificity. The researchers designed these heterotrimers to form stable interfaces that drive the pseudo symmetric self-assembly. The team used a combination of computational tools and wet-lab validation to obtain subunits that could work for their purpose. Funnily enough, they wanted to create 3 subunits that could interact (let’s call them A, B and C, interacting to create ABC heterotrimers), but the proteins did not collaborated: they preferred to create AAB or ABB trimers. These still work for their goal, but I found it very funny, and it was nice to see the researchers admit their “failure” in the paper.

• Assembly prediction: Using computational tools, the team predicted how the building blocks they developed would arrange into larger assemblies, while maintaining geometric constraints. The use of pseudosymmetry reduced the computational complexity, making it possible to model these massive assemblies.

• Optimization and validation: Of course, this was an iterative process: the designs were refined using energy minimization and structural simulation to ensure stability and accuracy.

So, what was the result of all of this? Well, they created 3 different nanocages, each with increasing complexity and size:

• 240-subunit nanocage: The “smallest” of the design, it was a sort of proof of concept for the pipeline. The cryo-EM data showed high geometric accuracy and the assembly was very stable. For reference, the biggest nanocage before this paper was 120 subunits, so this would be already a record! But they didn’t stop here.

• 540-subunit nanocage: To showcase the scalability of their approach, they created an even bigger structure. It also highlighted the ability to incorporate diverse subunit interactions while maintaining icosahedral geometry. This nanocage is actually also quasisymmetric, meaning that the same subunit is present in different conformations (something quite common in virus’s capsids).

• 960-subunit nanocage: This insanely big structure is the most complex, and a huge milestone in protein design. This nanocage consists of intricate subunit arrangements and achieves high stability despite its massive size.

So, yeah, they set a new record for protein nanocages’ size, and then they went and broke it twice! Apart from being very cool, these nanocages have interesting applications in biomedicine, like I said before. Another interesting application could be in synthetic biology, where these modular designs could be functionalized to create artificial biological systems.

This was a great read, and I strongly recommend it!

And stay tuned: next week the second issue in our series on protein nanocages is coming!

In other news:

• DNA origami templates for cryo-EM: If you have missed some DNA origami these days, this is a cool read. In this paper, the authors optimized DNA origami structures using cryo-EM single-particle analysis. They employed a refined DNA framework to resolve an 8 kDa thrombin-binding aptamer attached to the DNA structure. Pretty interesting and useful!

• Deep learning some RNA: I don’t give enough love to RNA here. To make amends (a little bit), here is a cool paper. In it, the team presents RhoFold+, a deep learning method for predicting RNA 3D structures from sequences, overcoming challenges of structural flexibility and data scarcity. Using an RNA language model pretrained on ~23.7 million sequences, it offers an automated pipeline that outperforms existing methods, and it can also predict RNA secondary structure. Very cool! I’ll cover more RNA, I promise.

• Stabilizing virus’ proteins: Sounds bad doesn’t it? But not always. Apparently, stabilizing the prefusion conformation of the respiratory syncytial virus (a key vaccine target) improves the effectiveness of this antigen as a vaccine, as show in this paper. The authors used computational approaches to identify localized flexible regions within the F protein that move during overall transformations in protein shape and they mutated it, fixing the protein shape. Very interesting.

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