Virus-like particles (VLPs) are self-assembling nanoparticles derived from viruses with potential as scaffolds for myriad applications. They are also excellent testbeds for engineering protein superstructures. Engineers often employ techniques such as amino acid substitutions and insertions/deletions. Yet evolution also utilizes circular permutation, a powerful natural strategy that has not been fully explored in engineering self-assembling protein nanoparticles. Here, we demonstrate this technique using the MS2 VLP as a model self-assembling, proteinaceous nanoparticle. We constructed a comprehensive circular permutation library of the fused MS2 coat protein dimer construct. The strategy revealed terminal locations, validated via cryo-electron microscopy, that enabled C-terminal peptide tagging and led to a protein encapsulation strategy via covalent bonding – a feature the native coat protein does not permit. Our systematic study demonstrates the power of circular permutation for engineering features as well as quantitatively and systematically exploring VLP structural determinants.
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Systematic engineering of virus-like particles to identify self-assembly rules for shifting particle size
Virus-like particles (VLPs) are promising scaffolds for biomaterials as well as diagnostic and therapeutic applications. However, there are some key challenges to be solved, such as the ability to engineer alternate sizes for varied use cases. To this end, we created a library of MS2 VLP variants at two key residues in the coat protein which have been implicated as important to controlling VLP size and geometry. By adapting a method for systematic mutagenesis coupled with size-based selections and high-throughput sequencing as a readout, we developed a quantitative assessment of two residues in MS2 coat protein that govern the size shift in MS2 VLPs. We then applied the strategy to the equivalent residues in Qβ VLPs, an MS2 homolog, and demonstrate that the analogous pair of residues are also able to impact Qβ VLP size and shape. These results underscore the power of fitness landscapes in identifying critical features for assembly.
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- Award ID(s):
- 2043973
- PAR ID:
- 10468724
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Virology
- Volume:
- 579
- Issue:
- C
- ISSN:
- 0042-6822
- Page Range / eLocation ID:
- 137 to 147
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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