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- PDB-9clz: Novel designed icosahedral nanoparticle I3-A6 -

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Entry
Database: PDB / ID: 9clz
TitleNovel designed icosahedral nanoparticle I3-A6
ComponentsI3-A6
KeywordsVIRUS LIKE PARTICLE / Thermophile / nanoparticle / icosahedron / dehydratase
Biological speciesEscherichia coli BL21 (bacteria)
MethodELECTRON MICROSCOPY / single particle reconstruction / cryo EM / Resolution: 2.5 Å
AuthorsHaas, C.M. / Jasti, N. / Dosey, A.M. / Gillespie, R. / Allen, J.D. / Leaf, E.M. / Crispin, M. / DeForest, C. / Kanekiyo, M. / King, N.P.
Funding support United States, 1items
OrganizationGrant numberCountry
National Institutes of Health/National Institute Of Allergy and Infectious Diseases (NIH/NIAID) United States
Citation
Journal: Proc Natl Acad Sci U S A / Year: 2025
Title: From sequence to scaffold: Computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks.
Authors: Cyrus M Haas / Naveen Jasti / Annie Dosey / Joel D Allen / Rebecca Gillespie / Jackson McGowan / Elizabeth M Leaf / Max Crispin / Cole A DeForest / Masaru Kanekiyo / Neil P King /
Abstract: Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and ...Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and durability. Computational protein design offers a route to nanoparticle scaffolds with structural and biochemical features tailored to specific vaccine applications. Although strategies for designing self-assembling proteins have been established, the recent development of powerful machine learning (ML)-based tools for protein structure prediction and design provides an opportunity to overcome several of their limitations. Here, we leveraged these tools to develop a generalizable method for designing self-assembling proteins starting from AlphaFold2 predictions of oligomeric protein building blocks. We used the method to generate six 60-subunit protein nanoparticles with icosahedral symmetry, and single-particle cryoelectron microscopy reconstructions of three of them revealed that they were designed with atomic-level accuracy. To transform one of these nanoparticles into a functional immunogen, we reoriented its termini through circular permutation, added a genetically encoded oligomannose-type glycan, and displayed a stabilized trimeric variant of the influenza hemagglutinin receptor-binding domain through a rigid de novo linker. The resultant immunogen elicited potent receptor-blocking and neutralizing antibody responses in mice. Our results demonstrate the practical utility of ML-based protein modeling tools in the design of nanoparticle vaccines. More broadly, by eliminating the requirement for experimentally determined structures of protein building blocks, our method dramatically expands the number of starting points available for designing self-assembling proteins.
#1: Journal: bioRxiv / Year: 2025
Title: From sequence to scaffold: computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks.
Authors: Cyrus M Haas / Naveen Jasti / Annie Dosey / Joel D Allen / Rebecca Gillespie / Jackson McGowan / Elizabeth M Leaf / Max Crispin / Cole A DeForest / Masaru Kanekiyo / Neil P King /
Abstract: Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and ...Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and durability. Computational protein design offers a route to novel nanoparticle scaffolds with structural and biochemical features tailored to specific vaccine applications. Although strategies for designing new self-assembling proteins have been established, the recent development of powerful machine learning-based tools for protein structure prediction and design provides an opportunity to overcome several of their limitations. Here, we leveraged these tools to develop a generalizable method for designing novel self-assembling proteins starting from AlphaFold2 predictions of oligomeric protein building blocks. We used the method to generate six new 60-subunit protein nanoparticles with icosahedral symmetry, and single-particle cryo-electron microscopy reconstructions of three of them revealed that they were designed with atomic-level accuracy. To transform one of these nanoparticles into a functional immunogen, we reoriented its termini through circular permutation, added a genetically encoded oligomannose-type glycan, and displayed a stabilized trimeric variant of the influenza hemagglutinin receptor binding domain through a rigid linker. The resultant immunogen elicited potent receptor-blocking and neutralizing antibody responses in mice. Our results demonstrate the practical utility of machine learning-based protein modeling tools in the design of nanoparticle vaccines. More broadly, by eliminating the requirement for experimentally determined structures of protein building blocks, our method dramatically expands the number of starting points available for designing new self-assembling proteins.
History
DepositionJul 12, 2024Deposition site: RCSB / Processing site: RCSB
Revision 1.0Jul 16, 2025Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: EM metadata / Data content type: EM metadata / Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: FSC / Data content type: FSC / Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: Half map / Part number: 1 / Data content type: Half map / Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: Half map / Part number: 2 / Data content type: Half map / Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: Image / Data content type: Image / Provider: repository / Type: Initial release
Revision 1.0Jul 16, 2025Data content type: Primary map / Data content type: Primary map / Provider: repository / Type: Initial release
Revision 1.1Sep 17, 2025Group: Data collection / Database references / Category: citation / citation_author / em_admin
Item: _citation.country / _citation.journal_abbrev ..._citation.country / _citation.journal_abbrev / _citation.journal_id_CSD / _citation.journal_id_ISSN / _citation.pdbx_database_id_DOI / _citation.pdbx_database_id_PubMed / _citation.title / _citation.year / _citation_author.identifier_ORCID / _citation_author.name / _em_admin.last_update
Revision 1.2Jan 28, 2026Group: Data collection / Database references / Category: citation / citation_author / em_admin / Item: _em_admin.last_update
Revision 1.1Jan 28, 2026Data content type: EM metadata / Data content type: EM metadata / EM metadata / Group: Database references / Experimental summary / Data content type: EM metadata / EM metadata / EM metadata / Category: citation / citation_author / em_admin / Data content type: EM metadata / Item: _em_admin.last_update

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Structure visualization

Structure viewerMolecule:
MolmilJmol/JSmol

Downloads & links

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Assembly

Deposited unit
0: I3-A6
1: I3-A6
2: I3-A6
3: I3-A6
4: I3-A6
5: I3-A6
6: I3-A6
7: I3-A6
8: I3-A6
9: I3-A6
A: I3-A6
B: I3-A6
C: I3-A6
D: I3-A6
E: I3-A6
F: I3-A6
G: I3-A6
H: I3-A6
I: I3-A6
J: I3-A6
K: I3-A6
L: I3-A6
M: I3-A6
N: I3-A6
O: I3-A6
P: I3-A6
Q: I3-A6
R: I3-A6
S: I3-A6
T: I3-A6
U: I3-A6
V: I3-A6
W: I3-A6
X: I3-A6
Y: I3-A6
Z: I3-A6
c: I3-A6
d: I3-A6
e: I3-A6
f: I3-A6
g: I3-A6
h: I3-A6
i: I3-A6
j: I3-A6
k: I3-A6
l: I3-A6
m: I3-A6
n: I3-A6
o: I3-A6
p: I3-A6
q: I3-A6
r: I3-A6
s: I3-A6
t: I3-A6
u: I3-A6
v: I3-A6
w: I3-A6
x: I3-A6
y: I3-A6
z: I3-A6


Theoretical massNumber of molelcules
Total (without water)1,257,82560
Polymers1,257,82560
Non-polymers00
Water00
1


  • Idetical with deposited unit
  • defined by author&software
  • Evidence: electron microscopy, not applicable
TypeNameSymmetry operationNumber
identity operation1_555x,y,z1

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Components

#1: Protein ...
I3-A6


Mass: 20963.756 Da / Num. of mol.: 60
Source method: isolated from a genetically manipulated source
Source: (gene. exp.) Escherichia coli BL21 (bacteria) / Plasmid: pET29b+ / Production host: Escherichia coli BL21 (bacteria)
Has protein modificationN

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Experimental details

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Experiment

ExperimentMethod: ELECTRON MICROSCOPY
EM experimentAggregation state: PARTICLE / 3D reconstruction method: single particle reconstruction

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Sample preparation

ComponentName: Novel designed icosahedral nanoparticle I3-A6 / Type: COMPLEX / Entity ID: all / Source: RECOMBINANT
Source (natural)Organism: Escherichia coli (E. coli)
Source (recombinant)Organism: Escherichia coli (E. coli)
Buffer solutionpH: 8
SpecimenEmbedding applied: NO / Shadowing applied: NO / Staining applied: NO / Vitrification applied: YES
VitrificationCryogen name: ETHANE

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Electron microscopy imaging

Experimental equipment
Model: Titan Krios / Image courtesy: FEI Company
MicroscopyModel: FEI TITAN KRIOS
Electron gunElectron source: FIELD EMISSION GUN / Accelerating voltage: 300 kV / Illumination mode: FLOOD BEAM
Electron lensMode: BRIGHT FIELD / Nominal defocus max: 2000 nm / Nominal defocus min: 500 nm
Image recordingElectron dose: 52 e/Å2 / Film or detector model: GATAN K3 (6k x 4k)

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Processing

CTF correctionType: PHASE FLIPPING AND AMPLITUDE CORRECTION
3D reconstructionResolution: 2.5 Å / Resolution method: FSC 0.143 CUT-OFF / Num. of particles: 290122 / Symmetry type: POINT

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