Self-assembling protein cages: from coiled-coil module to machine learning-driven de novo design of next-generation biomaterials

Abstract

The rational design of self-assembling protein nanocages holds great promise for synthetic biology, biotechnology and biomedical applications. Protein nanocages are well-defined nanoparticles with an inner cavity formed by self-assembly of repetitive protein building blocks. These cavities can be tailored to encapsulate and protect cargo molecules such as drugs, enzymes, or imaging agents. The ability to design de novo protein cages has recently been revolutionized by new concepts of modular protein design, computational design of interacting surfaces and machine learning-based generative protein design. Protein cages can be designed in diverse architectures and sizes, and their assembly and disassembly can be regulated by chemical, biological, and physical signals. Here, we focus on the review of engineering strategies for the designed protein cages based on coiled coils or other modular protein domains, their functionalization and opportunities of customized engineered protein cages.

Graphical abstract: Self-assembling protein cages: from coiled-coil module to machine learning-driven de novo design of next-generation biomaterials

Article information

Article type
Review Article
Submitted
22 Jul 2025
Accepted
14 Dec 2025
First published
23 Dec 2025
This article is Open Access
Creative Commons BY-NC license

Mater. Adv., 2026, Advance Article

Self-assembling protein cages: from coiled-coil module to machine learning-driven de novo design of next-generation biomaterials

A. K. Gupta, H. Esih, H. Gradišar and R. Jerala, Mater. Adv., 2026, Advance Article , DOI: 10.1039/D5MA00792E

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements