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Yorodumi- EMDB-42181: T33-ml23 - Designed Tetrahedral Protein Cage Using Machine Learni... -
+Open data
-Basic information
Entry | Database: EMDB / ID: EMD-42181 | |||||||||
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Title | T33-ml23 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms | |||||||||
Map data | ||||||||||
Sample |
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Keywords | Nanohedra / protein cage / tetrahedral / de novo protein interface / machine learning / two components / ProteinMPNN / nanoparticle / DE NOVO PROTEIN | |||||||||
Biological species | synthetic construct (others) | |||||||||
Method | single particle reconstruction / cryo EM / Resolution: 2.02 Å | |||||||||
Authors | Castells-Graells R / Meador K / Sawaya MR / Yeates TO | |||||||||
Funding support | United States, 2 items
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Citation | Journal: 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 |
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-Structure visualization
Supplemental images |
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-Downloads & links
-EMDB archive
Map data | emd_42181.map.gz | 60.2 MB | EMDB map data format | |
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Header (meta data) | emd-42181-v30.xml emd-42181.xml | 16.4 KB 16.4 KB | Display Display | EMDB header |
Images | emd_42181.png | 91.5 KB | ||
Filedesc metadata | emd-42181.cif.gz | 5.7 KB | ||
Others | emd_42181_half_map_1.map.gz emd_42181_half_map_2.map.gz | 59 MB 59 MB | ||
Archive directory | http://ftp.pdbj.org/pub/emdb/structures/EMD-42181 ftp://ftp.pdbj.org/pub/emdb/structures/EMD-42181 | HTTPS FTP |
-Validation report
Summary document | emd_42181_validation.pdf.gz | 869.7 KB | Display | EMDB validaton report |
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Full document | emd_42181_full_validation.pdf.gz | 869.3 KB | Display | |
Data in XML | emd_42181_validation.xml.gz | 12.4 KB | Display | |
Data in CIF | emd_42181_validation.cif.gz | 14.5 KB | Display | |
Arichive directory | https://ftp.pdbj.org/pub/emdb/validation_reports/EMD-42181 ftp://ftp.pdbj.org/pub/emdb/validation_reports/EMD-42181 | HTTPS FTP |
-Related structure data
Related structure data | 8uf0MC 8ui2C 8ujaC 8ukmC 8umpC 8umrC 8un1C M: atomic model generated by this map C: citing same article (ref.) |
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-Links
EMDB pages | EMDB (EBI/PDBe) / EMDataResource |
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-Map
File | Download / File: emd_42181.map.gz / Format: CCP4 / Size: 64 MB / Type: IMAGE STORED AS FLOATING POINT NUMBER (4 BYTES) | ||||||||||||||||||||
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Voxel size | X=Y=Z: 0.86 Å | ||||||||||||||||||||
Density |
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Symmetry | Space group: 1 | ||||||||||||||||||||
Details | EMDB XML:
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-Supplemental data
-Half map: #2
File | emd_42181_half_map_1.map | ||||||||||||
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Projections & Slices |
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Density Histograms |
-Half map: #1
File | emd_42181_half_map_2.map | ||||||||||||
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Projections & Slices |
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Density Histograms |
-Sample components
-Entire : T33-ml23 Designed Tetrahedral Protein Cage Using Machine Learning
Entire | Name: T33-ml23 Designed Tetrahedral Protein Cage Using Machine Learning |
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Components |
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-Supramolecule #1: T33-ml23 Designed Tetrahedral Protein Cage Using Machine Learning
Supramolecule | Name: T33-ml23 Designed Tetrahedral Protein Cage Using Machine Learning type: complex / ID: 1 / Parent: 0 / Macromolecule list: #1-#2 |
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Source (natural) | Organism: synthetic construct (others) |
Molecular weight | Theoretical: 400 KDa |
-Macromolecule #1: T33-ml23-redesigned-CutA-fold
Macromolecule | Name: T33-ml23-redesigned-CutA-fold / type: protein_or_peptide / ID: 1 / Number of copies: 1 / Enantiomer: LEVO |
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Source (natural) | Organism: synthetic construct (others) |
Molecular weight | Theoretical: 11.231908 KDa |
Recombinant expression | Organism: Escherichia coli BL21(DE3) (bacteria) |
Sequence | String: MAIIETTTPT EEEAKAIAKK LLENRLIAEA IITPALTKIY RENGEIKSET VTRVTLYTEE ENVPKAVTYI KAIHPDPIPP IIVITPTDA NPAYKGWVAF ET |
-Macromolecule #2: T33-ml23-redesigned-tandem-BMC-T-fold
Macromolecule | Name: T33-ml23-redesigned-tandem-BMC-T-fold / type: protein_or_peptide / ID: 2 / Number of copies: 1 / Enantiomer: LEVO |
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Source (natural) | Organism: synthetic construct (others) |
Molecular weight | Theoretical: 21.998457 KDa |
Recombinant expression | Organism: Escherichia coli BL21(DE3) (bacteria) |
Sequence | String: MHHHHHHGGS HHWGGDPERP ALGILELSSY ARGVKVADAA LKAAPVKLLK CEPVEPGRAL IMLLGEPEDV AKAMIAALDV AGLGSGNLI DYALIPEIHP QLLPFLKEYK KSEPIKDPNK AIIVAEVSTV AAAIEAADVA LRLANVELTS MRLAEHIGGR A SFTLIGDK ...String: MHHHHHHGGS HHWGGDPERP ALGILELSSY ARGVKVADAA LKAAPVKLLK CEPVEPGRAL IMLLGEPEDV AKAMIAALDV AGLGSGNLI DYALIPEIHP QLLPFLKEYK KSEPIKDPNK AIIVAEVSTV AAAIEAADVA LRLANVELTS MRLAEHIGGR A SFTLIGDK EDVEKAARAI RGVAGERLLD LEIIEKPVEA LIGNEFF |
-Macromolecule #3: water
Macromolecule | Name: water / type: ligand / ID: 3 / Number of copies: 67 / Formula: HOH |
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Molecular weight | Theoretical: 18.015 Da |
Chemical component information | ChemComp-HOH: |
-Experimental details
-Structure determination
Method | cryo EM |
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Processing | single particle reconstruction |
Aggregation state | particle |
-Sample preparation
Concentration | 0.6 mg/mL |
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Buffer | pH: 8 |
Grid | Model: Quantifoil R2/1 / Material: COPPER / Mesh: 300 |
Vitrification | Cryogen name: ETHANE / Chamber humidity: 90 % / Chamber temperature: 291 K / Instrument: FEI VITROBOT MARK IV |
-Electron microscopy
Microscope | FEI TITAN KRIOS |
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Image recording | Film or detector model: GATAN K3 BIOQUANTUM (6k x 4k) / Average exposure time: 1.16 sec. / Average electron dose: 40.0 e/Å2 |
Electron beam | Acceleration voltage: 300 kV / Electron source: FIELD EMISSION GUN |
Electron optics | Illumination mode: FLOOD BEAM / Imaging mode: BRIGHT FIELD / Cs: 2.7 mm / Nominal defocus max: 2.0 µm / Nominal defocus min: 0.5 µm |
Sample stage | Specimen holder model: FEI TITAN KRIOS AUTOGRID HOLDER / Cooling holder cryogen: NITROGEN |
Experimental equipment | Model: Titan Krios / Image courtesy: FEI Company |
-Image processing
Startup model | Type of model: NONE |
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Final reconstruction | Applied symmetry - Point group: T (tetrahedral) / Resolution.type: BY AUTHOR / Resolution: 2.02 Å / Resolution method: FSC 0.143 CUT-OFF / Software - Name: cryoSPARC / Number images used: 3900288 |
Initial angle assignment | Type: MAXIMUM LIKELIHOOD |
Final angle assignment | Type: MAXIMUM LIKELIHOOD |
-Atomic model buiding 1
Refinement | Space: REAL / Protocol: FLEXIBLE FIT |
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Output model | PDB-8uf0: |