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- PDB-8f4x: Top-down design of protein architectures with reinforcement learning -

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Basic information

Entry
Database: PDB / ID: 8f4x
TitleTop-down design of protein architectures with reinforcement learning
ComponentsRC_I_1-H11
KeywordsDE NOVO PROTEIN / nanoparticle / capsid / oligomer / de novo design / rosetta / cryoEM / reinforcement learning
Biological speciessynthetic construct (others)
MethodELECTRON MICROSCOPY / single particle reconstruction / cryo EM / Resolution: 3.01 Å
AuthorsBorst, A.J. / Baker, D.
Funding support United States, 1items
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, 2022Deposition site: RCSB / Processing site: RCSB
Revision 1.0May 10, 2023Provider: repository / Type: Initial release
Revision 1.1Jun 19, 2024Group: Data collection / Category: chem_comp_atom / chem_comp_bond

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Structure visualization

Structure viewerMolecule:
MolmilJmol/JSmol

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Assembly

Deposited unit
0: RC_I_1-H11
1: RC_I_1-H11
2: RC_I_1-H11
3: RC_I_1-H11
4: RC_I_1-H11
5: RC_I_1-H11
6: RC_I_1-H11
7: RC_I_1-H11
8: RC_I_1-H11
9: RC_I_1-H11
A: RC_I_1-H11
B: RC_I_1-H11
C: RC_I_1-H11
D: RC_I_1-H11
E: RC_I_1-H11
F: RC_I_1-H11
G: RC_I_1-H11
H: RC_I_1-H11
I: RC_I_1-H11
J: RC_I_1-H11
K: RC_I_1-H11
L: RC_I_1-H11
M: RC_I_1-H11
N: RC_I_1-H11
O: RC_I_1-H11
P: RC_I_1-H11
Q: RC_I_1-H11
R: RC_I_1-H11
S: RC_I_1-H11
T: RC_I_1-H11
U: RC_I_1-H11
V: RC_I_1-H11
W: RC_I_1-H11
X: RC_I_1-H11
Y: RC_I_1-H11
Z: RC_I_1-H11
a: RC_I_1-H11
b: RC_I_1-H11
c: RC_I_1-H11
d: RC_I_1-H11
e: RC_I_1-H11
f: RC_I_1-H11
g: RC_I_1-H11
h: RC_I_1-H11
i: RC_I_1-H11
j: RC_I_1-H11
k: RC_I_1-H11
l: RC_I_1-H11
m: RC_I_1-H11
n: RC_I_1-H11
o: RC_I_1-H11
p: RC_I_1-H11
q: RC_I_1-H11
r: RC_I_1-H11
s: RC_I_1-H11
t: RC_I_1-H11
u: RC_I_1-H11
v: RC_I_1-H11
w: RC_I_1-H11
x: RC_I_1-H11


Theoretical massNumber of molelcules
Total (without water)465,89860
Polymers465,89860
Non-polymers00
Water00
1


  • Idetical with deposited unit
  • defined by author
  • Evidence: electron microscopy, negative stain EM, cryoEM
TypeNameSymmetry operationNumber
identity operation1_5551

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Components

#1: Protein ...
RC_I_1-H11


Mass: 7764.962 Da / Num. of mol.: 60
Source method: isolated from a genetically manipulated source
Source: (gene. exp.) synthetic construct (others) / Production host: Escherichia coli (E. coli)

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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: RC_I_1-H11 / Type: COMPLEX / Entity ID: all / Source: RECOMBINANT
Source (natural)Organism: Escherichia coli (E. coli)
Source (recombinant)Organism: Escherichia coli (E. coli)
Buffer solutionpH: 7.5
SpecimenConc.: 0.8 mg/ml / Embedding applied: NO / Shadowing applied: NO / Staining applied: NO / Vitrification applied: YES
Specimen supportDetails: Thin layer of continuous carbon / Grid type: Quantifoil R2/2
VitrificationCryogen name: ETHANE

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Electron microscopy imaging

MicroscopyModel: TFS GLACIOS
Electron gunElectron source: FIELD EMISSION GUN / Accelerating voltage: 200 kV / Illumination mode: FLOOD BEAM
Electron lensMode: BRIGHT FIELD / Nominal defocus max: 1700 nm / Nominal defocus min: 800 nm
Image recordingElectron dose: 59 e/Å2 / Detector mode: COUNTING / Film or detector model: GATAN K2 SUMMIT (4k x 4k)

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Processing

SoftwareName: PHENIX / Version: 1.16_3549: / Classification: refinement
EM software
IDNameVersionCategory
7Rosettamodel fitting
8MDFFmodel fitting
9PHENIXmodel fitting
11cryoSPARC3.2initial Euler assignment
12cryoSPARC3.2final Euler assignment
15Rosettamodel refinement
16MDFFmodel refinement
17PHENIXmodel refinement
CTF correctionType: NONE
Particle selectionNum. of particles selected: 433599
3D reconstructionResolution: 3.01 Å / Resolution method: FSC 0.143 CUT-OFF / Num. of particles: 153765 / Symmetry type: POINT
Refine LS restraints
Refine-IDTypeDev idealNumber
ELECTRON MICROSCOPYf_bond_d0.00433120
ELECTRON MICROSCOPYf_angle_d0.45144520
ELECTRON MICROSCOPYf_dihedral_angle_d2.54421000
ELECTRON MICROSCOPYf_chiral_restr0.0335040
ELECTRON MICROSCOPYf_plane_restr0.0036000

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