<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><submitter>Dowling QM</submitter><funding>NIAID NIH HHS</funding><funding>NIGMS NIH HHS</funding><pubmed_abstract>Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions &lt;sup>1-3&lt;/sup>. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry &lt;sup>4,5&lt;/sup>. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.</pubmed_abstract><journal>bioRxiv : the preprint server for biology</journal><pagination>2023.06.16.545393</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10312784</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Hierarchical design of pseudosymmetric protein nanoparticles.</pubmed_title><pmcid>PMC10312784</pmcid><funding_grant_id>DP1 AI158186</funding_grant_id><funding_grant_id>P41 GM128577</funding_grant_id><funding_grant_id>P01 AI167966</funding_grant_id><funding_grant_id>75N93022C00036</funding_grant_id><funding_grant_id>U54 AI170856</funding_grant_id><pubmed_authors>Walkey C</pubmed_authors><pubmed_authors>Veesler D</pubmed_authors><pubmed_authors>Fries CN</pubmed_authors><pubmed_authors>Baker D</pubmed_authors><pubmed_authors>Ravichandran R</pubmed_authors><pubmed_authors>Gerstenmaier N</pubmed_authors><pubmed_authors>Burrell A</pubmed_authors><pubmed_authors>Yang EC</pubmed_authors><pubmed_authors>Wargacki A</pubmed_authors><pubmed_authors>King NP</pubmed_authors><pubmed_authors>Park YJ</pubmed_authors><pubmed_authors>Hsia Y</pubmed_authors><pubmed_authors>Dowling QM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Hierarchical design of pseudosymmetric protein nanoparticles.</name><description>Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions &lt;sup>1-3&lt;/sup>. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry &lt;sup>4,5&lt;/sup>. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jun</publication><modification>2026-04-07T15:47:13.848Z</modification><creation>2026-04-07T14:52:03.794Z</creation></dates><accession>S-EPMC10312784</accession><cross_references><pubmed>37398374</pubmed><doi>10.1101/2023.06.16.545393</doi></cross_references></HashMap>