De novo design of protein nanoparticles with integrated functional motifs.

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

SUBMITTER: Haas CM 

PROVIDER: S-EPMC12776285 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature