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TitleDesign and structure of protein cages based on helical fusion and machine learning
Journal, issue, pagesTo Be Published
Publish dateAug 4, 2026 (structure data deposition date)
AuthorsSan Segundo-Acosta P / LeCoq J / Boskovic J / Aglietti RA / Bowers P / Yeates TO / Castells-Graells R
External linksSearch PubMed
MethodsEM (single particle)
Resolution3.0 - 5.11 Å
Structure data

59307

33ar

EMDB-59307, PDB-33ar:
T33-Fus-1B cage - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 4.2 Å

59308

33as

EMDB-59308, PDB-33as:
T33-Fus-1A cage - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 3.61 Å

59315

33at

EMDB-59315, PDB-33at:
T33-Fus-2 cage - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 5.11 Å

59316

33au

EMDB-59316, PDB-33au:
T33-Fus-1A asymmetric unit - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 3.0 Å

59317

33av

EMDB-59317, PDB-33av:
T33-Fus-1B asymmetric unit - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 3.9 Å

59318

33aw

EMDB-59318, PDB-33aw:
T33-Fus-2 asymmetric unit - Designed tetrahedral protein cage based on helical fusion and machine learning
Method: EM (single particle) / Resolution: 3.84 Å

Source
  • synthetic construct (others)
KeywordsDE NOVO PROTEIN / protein cage / tetrahedral / protein design / nanohedra / nanoparticle

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