6OPD

Crystal Structure of ILNAMIVKI peptide bound to HLA-A2

Experimental Data Snapshot

  • Method: X-RAY DIFFRACTION
  • Resolution: 1.79 Å
  • R-Value Free: 0.204 
  • R-Value Work: 0.166 
  • R-Value Observed: 0.167 

wwPDB Validation   3D Report Full Report


This is version 1.2 of the entry. See complete history. 


Literature

Structure Based Prediction of Neoantigen Immunogenicity.

Riley, T.P., Keller, G.L.J., Smith, A.R., Davancaze, L.M., Arbuiso, A.G., Devlin, J.R., Baker, B.M.

(2019) Front Immunol 10: 2047-2047

  • DOI: 10.3389/fimmu.2019.02047
  • Primary Citation of Related Structures:  
    6OPD, 6PTB, 6PTE

  • PubMed Abstract: 

    • The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although ...

    The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8 + T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.

    Organizational Affiliation: 

    Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States.



Macromolecules
Find similar proteins by: 
(by identity cutoff)  |  Structure
Entity ID: 1
MoleculeChainsSequence LengthOrganismDetailsImage
HLA class I histocompatibility antigen, A-2 alpha chain A275Homo sapiensMutation(s): 0 
Gene Names: HLA-A, HLAA
Find proteins for P04439 (Homo sapiens)
Explore P04439 
Go to UniProtKB:  P04439
NIH Common Fund Data Resources
PHAROS:  P04439
GTEx:  ENSG00000206503
Protein Feature View
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  • Reference Sequence
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(by identity cutoff)  |  Structure
Entity ID: 2
MoleculeChainsSequence LengthOrganismDetailsImage
Beta-2-microglobulin B100Homo sapiensMutation(s): 0 
Gene Names: B2M, CDABP0092, HDCMA22P
Find proteins for P61769 (Homo sapiens)
Explore P61769 
Go to UniProtKB:  P61769
NIH Common Fund Data Resources
PHAROS:  P61769
GTEx:  ENSG00000166710
Protein Feature View
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  • Reference Sequence
  • Find similar proteins by:  Sequence   |   Structure
Entity ID: 3
MoleculeChainsSequence LengthOrganismDetailsImage
Melanoma antigen variant C9Homo sapiensMutation(s): 0 
Protein Feature View
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  • Reference Sequence
Experimental Data & Validation

Experimental Data

  • Method: X-RAY DIFFRACTION
  • Resolution: 1.79 Å
  • R-Value Free: 0.204 
  • R-Value Work: 0.166 
  • R-Value Observed: 0.167 
  • Space Group: P 21 21 21
Unit Cell:
Length ( Å )Angle ( ˚ )
a = 49.561α = 90
b = 74.641β = 90
c = 122.849γ = 90
Software Package:
Software NamePurpose
HKL-2000data scaling
PHENIXrefinement
PDB_EXTRACTdata extraction
HKL-2000data reduction
PHASERphasing

Structure Validation

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View more in-depth experimental data

Entry History & Funding Information

Deposition Data

Funding OrganizationLocationGrant Number
National Institutes of Health/National Cancer Institute (NIH/NCI)United States--

Revision History 

  • Version 1.0: 2019-09-04
    Type: Initial release
  • Version 1.1: 2019-10-09
    Changes: Data collection, Database references
  • Version 1.2: 2019-12-04
    Changes: Author supporting evidence