PDBsum entry 6ptb

Immune system

PDB id: 6ptb

Contents

Protein chains

275 a.a.

100 a.a.

Ligands

ILE-LEU-ASN-ALA-MET-ILE-ALA-LYS-ILE ×2

GOL ×2

Waters ×537

PDB id:

6ptb

Name:

Immune system

Title:

Crystal structure of ilnamiaki peptide bound to hla-a2

Structure:

Hla class i histocompatibility antigen, a-2 alpha chain. Chain: a, d. Fragment: unp residues 25-299. Synonym: mhc class i antigen a 2. Engineered: yes. Beta-2-microglobulin. Chain: b, e. Fragment: unp residues 21-119. Engineered: yes.

Source:

Homo sapiens. Human. Organism_taxid: 9606. Gene: hla-a, hlaa. Expressed in: escherichia coli. Expression_system_taxid: 562. Gene: b2m, cdabp0092, hdcma22p. Synthetic: yes. Organism_taxid: 9606

Resolution:

2.15Å R-factor: 0.197 R-free: 0.232

Authors:

G.L.J.Keller,A.Arbuiso,B.M.Baker

Key ref:

T.P.Riley et al. (2019). Structure Based Prediction of Neoantigen Immunogenicity. Front Immunol, 10, 2047. PubMed id: 31555277 DOI: 10.3389/fimmu.2019.02047

Date:

15-Jul-19 Release date: 04-Sep-19

Protein chains

P04439  (1A03_HUMAN) -  HLA class I histocompatibility antigen, A alpha chain from Homo sapiens

Seq: 365 a.a.

Struc: 275 a.a.*

Protein chains

P61769  (B2MG_HUMAN) -  Beta-2-microglobulin from Homo sapiens

Seq: 119 a.a.

Struc: 100 a.a.*

* PDB and UniProt seqs differ at 20 residue positions (black crosses)

DOI no: 10.3389/fimmu.2019.02047

Front Immunol 10:2047 (2019)

PubMed id: 31555277

Structure Based Prediction of Neoantigen Immunogenicity.

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

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 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.