De novo design of protein nanoparticles with integrated functional motifs

Computational design of self-assembling proteins has long relied on pre-existing structures and sequences, fundamentally limiting control over their structural and functional properties. Recent machine learning-based methods have transformed our ability to design functional small de novo proteins and oligomers, yet methods to design large de novo protein assemblies with structures tailored to specific applications are still underexplored. Here, we develop a generalizable method for designing de novo symmetric protein complexes that incorporate target functional motifs into their structures. We report 34 new protein nanoparticles that form on-target assemblies with cubic point group symmetries. The nanoparticles exhibit a wide variety of backbones that were designed with atom-level accuracy, as evidenced by several cryo-EM and crystal structures that reveal minimal deviations from the design models. We use the method to generate a de novo antigen-tailored nanoparticle vaccine that elicits robust immune responses in mice. These results establish a generalizable approach that can be used to design functional self-assembling protein complexes with structures tailored to specific applications.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2025-12-22
DOI
https://doi.org/10.64898/2025.12.19.695620
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
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article

De novo design of protein nanoparticles with integrated functional motifs

E Grey, Connor Weidle, Elizabeth M. Leaf, Neil P. King et al.
bioRxiv (Cold Spring Harbor Laboratory)
vaccines and immunoinformatics approaches
article

De novo design of protein nanoparticles with integrated functional motifs

E Grey, Connor Weidle, Elizabeth M. Leaf, Neil P. King, Asim K. Bera, Cyrus M. Haas, Cole A. DeForest, Rashmi Ravichandran, Andrew J. Borst, Adrian Creanga, Sanela Ristić Ranković, Masaru Kanekiyo, Annie Dosey, L. Nuss, Kenneth D. Carr, H. Tai Nguyen, N. Pavan Kumar, Yanqing Wang, Jackson McGowan, Banumathi Sankaran, Syed Moiz, Hanul K Lewis, Alex Kang, Sophie S Gerdes, Maggie Fiorelli, Felicitas Ruiz
article en

Abstract

Computational design of self-assembling proteins has long relied on pre-existing structures and sequences, fundamentally limiting control over their structural and functional properties. Recent machine learning-based methods have transformed our ability to design functional small de novo proteins and oligomers, yet methods to design large de novo protein assemblies with structures tailored to specific applications are still underexplored. Here, we develop a generalizable method for designing de novo symmetric protein complexes that incorporate target functional motifs into their structures. We report 34 new protein nanoparticles that form on-target assemblies with cubic point group symmetries. The nanoparticles exhibit a wide variety of backbones that were designed with atom-level accuracy, as evidenced by several cryo-EM and crystal structures that reveal minimal deviations from the design models. We use the method to generate a de novo antigen-tailored nanoparticle vaccine that elicits robust immune responses in mice. These results establish a generalizable approach that can be used to design functional self-assembling protein complexes with structures tailored to specific applications.

bioRxiv (Cold Spring Harbor Laboratory)
Lawrence Berkeley National Laboratory (US), University of Washington (US), National Institute of Allergy and Infectious Diseases (US)
Openalex Percentile: Top 37%
vaccines and immunoinformatics approaches
DOIOpen Access PDFOpenAlex
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