8T5F

De novo design of high-affinity protein binders to bioactive helical peptides


Experimental Data Snapshot

  • Method: X-RAY DIFFRACTION
  • Resolution: 1.99 Å
  • R-Value Free: 0.251 
  • R-Value Work: 0.219 
  • R-Value Observed: 0.221 

Starting Model: other
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Literature

De novo design of high-affinity binders of bioactive helical peptides.

Vazquez Torres, S.Leung, P.J.Y.Venkatesh, P.Lutz, I.D.Hink, F.Huynh, H.H.Becker, J.Yeh, A.H.Juergens, D.Bennett, N.R.Hoofnagle, A.N.Huang, E.MacCoss, M.J.Exposit, M.Lee, G.R.Bera, A.K.Kang, A.De La Cruz, J.Levine, P.M.Li, X.Lamb, M.Gerben, S.R.Murray, A.Heine, P.Korkmaz, E.N.Nivala, J.Stewart, L.Watson, J.L.Rogers, J.M.Baker, D.

(2024) Nature 626: 435-442

  • DOI: https://doi.org/10.1038/s41586-023-06953-1
  • Primary Citation of Related Structures:  
    8GJG, 8GJI, 8T5E, 8T5F

  • PubMed Abstract: 

    Many peptide hormones form an α-helix on binding their receptors 1-4 , and sensitive methods for their detection could contribute to better clinical management of disease 5 . De novo protein design can now generate binders with high affinity and specificity to structured proteins 6,7 . However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion 8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.


  • Organizational Affiliation

    Department of Biochemistry, University of Washington, Seattle, WA, USA.


Macromolecules
Find similar proteins by:  (by identity cutoff)  |  3D Structure
Entity ID: 1
MoleculeChains Sequence LengthOrganismDetailsImage
Parathyroid hormoneA [auth C],
B,
C [auth A]
34Homo sapiensMutation(s): 0 
UniProt & NIH Common Fund Data Resources
Find proteins for P01270 (Homo sapiens)
Explore P01270 
Go to UniProtKB:  P01270
PHAROS:  P01270
GTEx:  ENSG00000152266 
Entity Groups  
Sequence Clusters30% Identity50% Identity70% Identity90% Identity95% Identity100% Identity
UniProt GroupP01270
Sequence Annotations
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  • Reference Sequence
Experimental Data & Validation

Experimental Data

  • Method: X-RAY DIFFRACTION
  • Resolution: 1.99 Å
  • R-Value Free: 0.251 
  • R-Value Work: 0.219 
  • R-Value Observed: 0.221 
  • Space Group: P 4 21 2
Unit Cell:
Length ( Å )Angle ( ˚ )
a = 91.324α = 90
b = 91.324β = 90
c = 37.732γ = 90
Software Package:
Software NamePurpose
PHENIXrefinement
XDSdata reduction
XSCALEdata scaling
PHASERphasing

Structure Validation

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Entry History & Funding Information

Deposition Data


Funding OrganizationLocationGrant Number
National Science Foundation (NSF, United States)United States--
Howard Hughes Medical Institute (HHMI)United States--

Revision History  (Full details and data files)

  • Version 1.0: 2024-01-10
    Type: Initial release
  • Version 1.1: 2024-02-14
    Changes: Database references