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- EMDB-42286: T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learni... -

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Basic information

Entry
Database: EMDB / ID: EMD-42286
TitleT33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms
Map data
Sample
  • Complex: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
    • Protein or peptide: T33-ml28-redesigned-tandem-BMC-T-fold
    • Protein or peptide: T33-ml28-redesigned-CutA-fold
KeywordsNanohedra / protein cage / tetrahedral / de novo protein interface / machine learning / two components / ProteinMPNN / nanoparticle / DE NOVO PROTEIN
Biological speciessynthetic construct (others)
Methodsingle particle reconstruction / cryo EM / Resolution: 2.73 Å
AuthorsCastells-Graells R / Meador K / Sawaya MR / Yeates TO
Funding support United States, 2 items
OrganizationGrant numberCountry
National Institutes of Health/National Institute of General Medical Sciences (NIH/NIGMS)GM129854 United States
Department of Energy (DOE, United States)DE-FC02-02ER63421 United States
CitationJournal: Structure / Year: 2024
Title: A suite of designed protein cages using machine learning and protein fragment-based protocols.
Authors: Kyle Meador / Roger Castells-Graells / Roman Aguirre / Michael R Sawaya / Mark A Arbing / Trent Sherman / Chethaka Senarathne / Todd O Yeates /
Abstract: Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational ...Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.
History
DepositionOct 9, 2023-
Header (metadata) releaseMar 6, 2024-
Map releaseMar 6, 2024-
UpdateJun 19, 2024-
Current statusJun 19, 2024Processing site: RCSB / Status: Released

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Structure visualization

Supplemental images

emd_42286.png

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Map

FileDownload / File: emd_42286.map.gz / Format: CCP4 / Size: 28.7 MB / Type: IMAGE STORED AS FLOATING POINT NUMBER (4 BYTES)
Projections & slices

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AxesZ (Sec.)Y (Row.)X (Col.)
1.1 Å/pix.
x 196 pix.
= 215.6 Å		1.1 Å/pix.
x 196 pix.
= 215.6 Å		1.1 Å/pix.
x 196 pix.
= 215.6 Å

Surface

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Images are generated by Spider.

Voxel sizeX=Y=Z: 1.1 Å
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Histogram

Histogram (log scale)
Contour LevelBy AUTHOR: 0.55
Minimum - Maximum-2.2432404 - 3.028654
Average (Standard dev.)0.007838745 (±0.1471246)
SymmetrySpace group: 1
Details

EMDB XML:

Map geometry
Axis orderXYZ
Origin000
Dimensions196196196
Spacing196196196
CellA=B=C: 215.6 Å
α=β=γ: 90.0 °

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Supplemental data

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Half map: #2

Fileemd_42286_half_map_1.map
Projections & Slices
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Half map: #1

Fileemd_42286_half_map_2.map
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Sample components

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Entire : T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning

EntireName: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
Components
  • Complex: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
    • Protein or peptide: T33-ml28-redesigned-tandem-BMC-T-fold
    • Protein or peptide: T33-ml28-redesigned-CutA-fold

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Supramolecule #1: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning

SupramoleculeName: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
type: complex / ID: 1 / Parent: 0 / Macromolecule list: all
Source (natural)Organism: synthetic construct (others)
Molecular weightTheoretical: 400 KDa

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Macromolecule #1: T33-ml28-redesigned-tandem-BMC-T-fold

MacromoleculeName: T33-ml28-redesigned-tandem-BMC-T-fold / type: protein_or_peptide / ID: 1 / Number of copies: 1 / Enantiomer: LEVO
Source (natural)Organism: synthetic construct (others)
Molecular weightTheoretical: 21.832328 KDa
Recombinant expressionOrganism: Escherichia coli BL21(DE3) (bacteria)
SequenceString: MHHHHHHGGS HHWGGDPARP ALGVLELKSY ALGVAVADAA LRAAPVELLK CEPVEPGKAL IMIRGEPEAV ARAMAAALET AKAGSGNLI DHAFIGRIHP ALLPFLLEET AAPPIEDPDE AVLVVETKTV AAAIEAADAA LDVAPVRLLR MRLSEHIGGK A YFVLAGDE ...String:
MHHHHHHGGS HHWGGDPARP ALGVLELKSY ALGVAVADAA LRAAPVELLK CEPVEPGKAL IMIRGEPEAV ARAMAAALET AKAGSGNLI DHAFIGRIHP ALLPFLLEET AAPPIEDPDE AVLVVETKTV AAAIEAADAA LDVAPVRLLR MRLSEHIGGK A YFVLAGDE EAVRKAARAV RAVAGEKLID LRIIPRPHEA LRGRLFF

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Macromolecule #2: T33-ml28-redesigned-CutA-fold

MacromoleculeName: T33-ml28-redesigned-CutA-fold / type: protein_or_peptide / ID: 2 / Number of copies: 1 / Enantiomer: LEVO
Source (natural)Organism: synthetic construct (others)
Molecular weightTheoretical: 11.853737 KDa
Recombinant expressionOrganism: Escherichia coli BL21(DE3) (bacteria)
SequenceString:
MPIALTVVPP EEAEPLAREL VEAGLAAEVL LVPVRRIYRE KGKVREEEVT LLLILVSREG VPALRAWIEA RHPDDIPLFI VLAVDEEAS NKRYLGYIAA ETHLYSA

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Experimental details

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Structure determination

Methodcryo EM
Processingsingle particle reconstruction
Aggregation stateparticle

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Sample preparation

BufferpH: 8
GridModel: Quantifoil R2/1 / Material: COPPER / Mesh: 300
VitrificationCryogen name: ETHANE / Chamber humidity: 90 % / Chamber temperature: 291 K / Instrument: FEI VITROBOT MARK IV

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Electron microscopy

MicroscopeFEI TITAN KRIOS
Image recordingFilm or detector model: GATAN K3 BIOQUANTUM (6k x 4k) / Average electron dose: 40.0 e/Å2
Electron beamAcceleration voltage: 300 kV / Electron source: FIELD EMISSION GUN
Electron opticsIllumination mode: FLOOD BEAM / Imaging mode: BRIGHT FIELD / Cs: 2.7 mm / Nominal defocus max: 2.5 µm / Nominal defocus min: 0.5 µm
Sample stageSpecimen holder model: FEI TITAN KRIOS AUTOGRID HOLDER / Cooling holder cryogen: NITROGEN
Experimental equipment
Model: Titan Krios / Image courtesy: FEI Company

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Image processing

Startup modelType of model: NONE
Final reconstructionApplied symmetry - Point group: T (tetrahedral) / Resolution.type: BY AUTHOR / Resolution: 2.73 Å / Resolution method: FSC 0.143 CUT-OFF / Software - Name: cryoSPARC / Number images used: 857483
Initial angle assignmentType: MAXIMUM LIKELIHOOD
Final angle assignmentType: MAXIMUM LIKELIHOOD

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Atomic model buiding 1

RefinementSpace: REAL / Protocol: FLEXIBLE FIT
Output model

PDB-8ui2:
T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms

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