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- EMDB-28860: Top-down design of protein architectures with reinforcement learning -

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Basic information

Entry
Database: EMDB / ID: EMD-28860
TitleTop-down design of protein architectures with reinforcement learning
Map dataSharpened Map
Sample
  • Complex: RC_I_1
    • Protein or peptide: RC_I_1
Biological speciesEscherichia coli (E. coli) / synthetic construct (others)
Methodsingle particle reconstruction / cryo EM / Resolution: 2.5 Å
AuthorsBorst AJ / Baker D
Funding support United States, 1 items
OrganizationGrant numberCountry
Howard Hughes Medical Institute (HHMI) United States
CitationJournal: Science / Year: 2023
Title: Top-down design of protein architectures with reinforcement learning.
Authors: Isaac D Lutz / Shunzhi Wang / Christoffer Norn / Alexis Courbet / Andrew J Borst / Yan Ting Zhao / Annie Dosey / Longxing Cao / Jinwei Xu / Elizabeth M Leaf / Catherine Treichel / Patrisia ...Authors: Isaac D Lutz / Shunzhi Wang / Christoffer Norn / Alexis Courbet / Andrew J Borst / Yan Ting Zhao / Annie Dosey / Longxing Cao / Jinwei Xu / Elizabeth M Leaf / Catherine Treichel / Patrisia Litvicov / Zhe Li / Alexander D Goodson / Paula Rivera-Sánchez / Ana-Maria Bratovianu / Minkyung Baek / Neil P King / Hannele Ruohola-Baker / David Baker /
Abstract: As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function ...As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.
History
DepositionNov 11, 2022-
Header (metadata) releaseMay 10, 2023-
Map releaseMay 10, 2023-
UpdateMay 10, 2023-
Current statusMay 10, 2023Processing site: RCSB / Status: Released

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Structure visualization

Supplemental images

emd_28860.png

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Map

FileDownload / File: emd_28860.map.gz / Format: CCP4 / Size: 178 MB / Type: IMAGE STORED AS FLOATING POINT NUMBER (4 BYTES)
AnnotationSharpened Map
Voxel sizeX=Y=Z: 0.84 Å
Density
Contour LevelBy AUTHOR: 0.769
Minimum - Maximum-3.4754443 - 4.519881
Average (Standard dev.)0.0011278368 (±0.15361546)
SymmetrySpace group: 1
Details

EMDB XML:

Map geometry
Axis orderXYZ
Origin000
Dimensions360360360
Spacing360360360
CellA=B=C: 302.4 Å
α=β=γ: 90.0 °

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Supplemental data

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Additional map: Unsharpened Map

Fileemd_28860_additional_1.map
AnnotationUnsharpened Map
Projections & Slices
AxesZYX

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Half map: Half Map B

Fileemd_28860_half_map_1.map
AnnotationHalf Map B
Projections & Slices
AxesZYX

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Half map: Half Map A

Fileemd_28860_half_map_2.map
AnnotationHalf Map A
Projections & Slices
AxesZYX

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Sample components

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Entire : RC_I_1

EntireName: RC_I_1
Components
  • Complex: RC_I_1
    • Protein or peptide: RC_I_1

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Supramolecule #1: RC_I_1

SupramoleculeName: RC_I_1 / type: complex / ID: 1 / Chimera: Yes / Parent: 0 / Macromolecule list: all
Source (natural)Organism: Escherichia coli (E. coli)

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Macromolecule #1: RC_I_1

MacromoleculeName: RC_I_1 / type: protein_or_peptide / ID: 1 / Number of copies: 60 / Enantiomer: LEVO
Source (natural)Organism: synthetic construct (others)
Molecular weightTheoretical: 7.828097 KDa
Recombinant expressionOrganism: Escherichia coli (E. coli)
SequenceString:
PDEDLKAELA ATEAIWLLRQ GRPEEVWKLM QRLYEKGDPA LWAVLRALLR SGDEIAILIA WNFMQRI

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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

Concentration1.0 mg/mL
BufferpH: 7.5
GridModel: Quantifoil R2/2 / Support film - Material: CARBON / Support film - topology: CONTINUOUS
VitrificationCryogen name: ETHANE

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Electron microscopy

MicroscopeTFS KRIOS
Electron beamAcceleration voltage: 300 kV / Electron source: FIELD EMISSION GUN
Electron opticsIllumination mode: FLOOD BEAM / Imaging mode: BRIGHT FIELDBright-field microscopy / Nominal defocus max: 1.7 µm / Nominal defocus min: 0.8 µm
Image recordingFilm or detector model: GATAN K3 (6k x 4k) / Detector mode: COUNTING / Average electron dose: 61.155 e/Å2
Experimental equipment
Model: Titan Krios / Image courtesy: FEI Company

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Image processing

Particle selectionNumber selected: 730457
Startup modelType of model: NONE / Details: Ab initio
Initial angle assignmentType: NOT APPLICABLE / Software - Name: cryoSPARC (ver. 3.2)
Final angle assignmentType: MAXIMUM LIKELIHOOD / Software - Name: cryoSPARC (ver. 3.2)
Final reconstructionResolution.type: BY AUTHOR / Resolution: 2.5 Å / Resolution method: FSC 0.143 CUT-OFF / Software - Name: cryoSPARC (ver. 3.2) / Number images used: 678727
FSC plot (resolution estimation)

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