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- EMDB-42286: T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learni... -
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Open data
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Basic information
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Title | T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms | |||||||||
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![]() | Nanohedra / protein cage / ![]() ![]() ![]() ![]() ![]() | |||||||||
Biological species | synthetic construct (others) | |||||||||
Method | ![]() ![]() | |||||||||
![]() | Castells-Graells R / Meador K / Sawaya MR / Yeates TO | |||||||||
Funding support | ![]()
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![]() | ![]() 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 | ![]() | 27 MB | ![]() | |
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Header (meta data) | ![]() ![]() | 15.8 KB 15.8 KB | Display Display | ![]() |
Images | ![]() | 80 KB | ||
Filedesc metadata | ![]() | 5.5 KB | ||
Others | ![]() ![]() | 26.7 MB 26.7 MB | ||
Archive directory | ![]() ![]() | HTTPS FTP |
-Related structure data
Related structure data | ![]() 8ui2MC ![]() 8uf0C ![]() 8ujaC ![]() 8ukmC ![]() 8umpC ![]() 8umrC ![]() 8un1C M: atomic model generated by this map C: citing same article ( |
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Links
EMDB pages | ![]() ![]() |
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Map
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Voxel size | X=Y=Z: 1.1 Å | ||||||||||||||||||||
Density |
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Symmetry | Space group: 1 | ||||||||||||||||||||
Details | EMDB XML:
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-Supplemental data
-Half map: #2
File | emd_42286_half_map_1.map | ||||||||||||
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Projections & Slices |
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Density Histograms |
-Half map: #1
File | emd_42286_half_map_2.map | ||||||||||||
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Density Histograms |
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Sample components
-Entire : T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
Entire | Name: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning |
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Components |
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-Supramolecule #1: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning
Supramolecule | Name: T33-ml28 Designed Tetrahedral Protein Cage Using Machine Learning type: complex / ID: 1 / Parent: 0 / Macromolecule list: all |
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Source (natural) | Organism: synthetic construct (others) |
Molecular weight | Theoretical: 400 KDa |
-Macromolecule #1: T33-ml28-redesigned-tandem-BMC-T-fold
Macromolecule | Name: T33-ml28-redesigned-tandem-BMC-T-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: 21.832328 KDa |
Recombinant expression | Organism: ![]() ![]() ![]() |
Sequence | String: 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 |
-Macromolecule #2: T33-ml28-redesigned-CutA-fold
Macromolecule | Name: T33-ml28-redesigned-CutA-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: 11.853737 KDa |
Recombinant expression | Organism: ![]() ![]() ![]() |
Sequence | String: MPIALTVVPP EEAEPLAREL VEAGLAAEVL LVPVRRIYRE KGKVREEEVT LLLILVSREG VPALRAWIEA RHPDDIPLFI VLAVDEEAS NKRYLGYIAA ETHLYSA |
-Experimental details
-Structure determination
Method | ![]() |
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Aggregation state | particle |
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Sample preparation
Buffer | pH: 8 |
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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 |
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Electron microscopy
Microscope | FEI TITAN KRIOS |
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Electron beam | Acceleration voltage: 300 kV / Electron source: ![]() |
Electron optics | Illumination mode: FLOOD BEAM / Imaging mode: BRIGHT FIELD![]() |
Sample stage | Specimen holder model: FEI TITAN KRIOS AUTOGRID HOLDER / Cooling holder cryogen: NITROGEN |
Image recording | Film or detector model: GATAN K3 BIOQUANTUM (6k x 4k) / Average electron dose: 40.0 e/Å2 |
Experimental equipment | ![]() Model: Titan Krios / Image courtesy: FEI Company |
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Image processing
Startup model | Type of model: NONE |
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Initial angle assignment | Type: MAXIMUM LIKELIHOOD |
Final angle assignment | Type: MAXIMUM LIKELIHOOD |
Final reconstruction | Applied symmetry - Point group: T (tetrahedral![]() |
-Atomic model buiding 1
Refinement | Space: REAL / Protocol: FLEXIBLE FIT |
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Output model | ![]() PDB-8ui2: |