9FKD | pdb_00009fkd

Progesterone-bound DB3 Fab in complex with computationally designed DBPro1156_2 protein binder

• PDB DOI: https://doi.org/10.2210/pdb9FKD/pdb
• EM Map EMD-50522: EMDB EMDataResource

• Classification: DE NOVO PROTEIN
• Organism(s): synthetic construct
• Expression System: Escherichia coli, Homo sapiens
• Mutation(s): No 


• Deposited: 2024-06-03 Released: 2024-10-30 
• Deposition Author(s): Pacesa, M., Marchand, A., Correia, B.E.
• Funding Organization(s): Swiss National Science Foundation

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Experimental Data Snapshot

• Method: ELECTRON MICROSCOPY
• Resolution: 3.30 Å
• Aggregation State: PARTICLE 
• Reconstruction Method: SINGLE PARTICLE 

Literature

Targeting protein-ligand neosurfaces with a generalizable deep learning tool.

Marchand, A., Buckley, S., Schneuing, A., Pacesa, M., Elia, M., Gainza, P., Elizarova, E., Neeser, R.M., Lee, P.W., Reymond, L., Miao, Y., Scheller, L., Georgeon, S., Schmidt, J., Schwaller, P., Maerkl, S.J., Bronstein, M., Correia, B.E.

(2025) Nature 639: 522-531

• PubMed: 39814890 
• DOI: https://doi.org/10.1038/s41586-024-08435-4
• Primary Citation Related Structures: 
  8S1X, 9FKD


• PubMed Abstract: 
  

  Molecular recognition events between proteins drive biological processes in living systems ¹ . However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules ^2-5 . Despite recent advances, computational tools for the design of new chemically induced protein interactions have remained a challenging task for the field ^6,7 . Here we present a computational strategy for the design of proteins that target neosurfaces, that is, surfaces arising from protein-ligand complexes. To develop this strategy, we leveraged a geometric deep learning approach based on learned molecular surface representations ^8,9 and experimentally validated binders against three drug-bound protein complexes: Bcl2-venetoclax, DB3-progesterone and PDF1-actinonin. All binders demonstrated high affinities and accurate specificities, as assessed by mutational and structural characterization. Remarkably, surface fingerprints previously trained only on proteins could be applied to neosurfaces induced by interactions with small molecules, providing a powerful demonstration of generalizability that is uncommon in other deep learning approaches. We anticipate that such designed chemically induced protein interactions will have the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells for innovative drug-controlled cell-based therapies ¹⁰ .

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Organizational Affiliation: 
• Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland.
Monte Rosa Therapeutics, Boston, MA, USA.
Laboratory of Chemical Artificial Intelligence, Institute of Chemical Sciences and Engineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland.
Laboratory of Biological Network Characterization, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland.
Biomolecular Screening Core Facility, School of Life Sciences, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland.
Department of Computer Science, University of Oxford, Oxford, UK.
Aithyra Research Institute for Biomedical Artificial Intelligence, Austrian Academy of Sciences, Vienna, Austria.
Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland. [e-mail].

Biological Assembly 1  

Global Symmetry: Asymmetric - C1 
Global Stoichiometry: Hetero 5-mer - A1B1C1D1E1 Pseudo Symmetry: Asymmetric - C1 
Pseudo Stoichiometry: Hetero 5-mer - A3B1C1 

Biological assembly 1 assigned by authors and generated by (software)

Biological Assembly Evidence: electron microscopy

Macromolecule Content 

• Total Structure Weight: 108.13 kDa 
• Atom Count: 7,193 
• Modeled Residue Count: 933 
• Deposited Residue Count: 983 
• Unique protein chains: 5

Macromolecules

Entity ID: 1
Molecule	Chains	Sequence Length	Organism	Details	Image
De novo designed DBPro1156_2 binder	A [auth B]	70	synthetic construct	Mutation(s): 0

Entity ID: 2
Molecule	Chains	Sequence Length	Organism	Details	Image
DB3 Fab Heavy chain	B [auth H]	239	synthetic construct	Mutation(s): 0

Entity ID: 3
Molecule	Chains	Sequence Length	Organism	Details	Image
DB3 Fab Light Chain	C [auth L]	222	synthetic construct	Mutation(s): 0

Entity ID: 4
Molecule	Chains	Sequence Length	Organism	Details	Image
Anti-kappa Fab Light Chain	D [auth K]	217	synthetic construct	Mutation(s): 0

Entity ID: 5
Molecule	Chains	Sequence Length	Organism	Details	Image
Anti-kappa Fab Heavy Chain	E [auth I]	235	synthetic construct	Mutation(s): 0

Small Molecules

Ligands 1 Unique
ID	Chains	Name / Formula / InChI Key	2D Diagram
STR (Subject of Investigation/LOI)	F [auth H]	PROGESTERONE C21 H30 O2 RJKFOVLPORLFTN-LEKSSAKUSA-N

Experimental Data & Validation

Experimental Data

• Method: ELECTRON MICROSCOPY
• Resolution: 3.30 Å
• Aggregation State: PARTICLE 
• Reconstruction Method: SINGLE PARTICLE 

EM Software:

Task	Software Package	Version
MODEL REFINEMENT	PHENIX	dev_5316
RECONSTRUCTION	cryoSPARC	4.4.1

Structure Validation

Entry History 

& Funding Information

Deposition Data

• Released Date: 2024-10-30 
Deposition Author(s): Pacesa, M., Marchand, A., Correia, B.E.

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Funding Organization	Location	Grant Number
Swiss National Science Foundation	Switzerland	310030_197724

Revision History  

• Version 1.0: 2024-10-30
  Type: Initial release
  
• Version 1.1: 2024-11-20
  Changes: Data collection
• Version 1.2: 2025-01-15
  Changes: Data collection, Database references
• Version 1.3: 2025-01-29
  Changes: Data collection, Database references
• Version 1.4: 2025-03-26
  Changes: Data collection, Database references