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Structural dissimilarity from self drives neoepitope escape from immune tolerance


T-cell recognition of peptides incorporating nonsynonymous mutations, or neoepitopes, is a cornerstone of tumor immunity and forms the basis of new immunotherapy approaches including personalized cancer vaccines. Yet as they are derived from self-peptides, the means through which immunogenic neoepitopes overcome immune self-tolerance are often unclear. Here we show that a point mutation in a non-major histocompatibility complex anchor position induces structural and dynamic changes in an immunologically active ovarian cancer neoepitope. The changes pre-organize the peptide into a conformation optimal for recognition by a neoepitope-specific T-cell receptor, allowing the receptor to bind the neoepitope with high affinity and deliver potent T-cell signals. Our results emphasize the importance of structural and physical changes relative to self in neoepitope immunogenicity. Considered broadly, these findings can help explain some of the difficulties in identifying immunogenic neoepitopes from sequence alone and provide guidance for developing novel, neoepitope-based personalized therapies.

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Fig. 1: The mutation in the HHATp8F neoepitope alters the surface presented to T cells without changing peptide–MHC binding.
Fig. 2: The HHATp8F neoepitope is more stimulatory with the 302TIL TCR and is recognized with higher affinity and faster association kinetics.
Fig. 3: The altered conformation in the HHATp8F neoepitope is required for TCR binding.
Fig. 4: The mutation in the HHATp8F neoepitope increases the occupancy of the Trp6 binding-competent configuration.

Data availability

Structural data, including coordinates and structure factors, are available at the Protein Data Bank ( under accession codes 6UJQ, 6UJO, 6UK2 and 6UK4. Other data are available upon request.


  1. 1.

    Bräunlein, E. & Krackhardt, A. M. Identification and characterization of neoantigens as well as respective immune responses in cancer patients. Front. Immunol. 8, 1702 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. 2.

    Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Bobisse, S. et al. Sensitive and frequent identification of high avidity neo-epitope specific CD8+ T cells in immunotherapy-naive ovarian cancer. Nat. Commun. 9, 1092 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Ebrahimi-Nik, H. et al. Mass spectrometry–driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight 4, e129152 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    Bassani-Sternberg, M. et al. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput. Biol. 13, e1005725 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    Schumacher, T. N., Scheper, W. & Kvistborg, P. Cancer neoantigens. Annu. Rev. Immunol. 37, 173–200 (2019).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Slansky, J. E. et al. Enhanced antigen-specific antitumor immunity with altered peptide ligands that stabilize the MHC-peptide-TCR complex. Immunity 13, 529–538 (2000).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Duan, F. et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J. Exp. Med. 211, 2231–2248 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Borbulevych, O. Y., Baxter, T. K., Yu, Z., Restifo, N. P. & Baker, B. M. Increased immunogenicity of an anchor-modified tumor-associated antigen is due to the enhanced stability of the peptide/MHC complex: implications for vaccine design. J. Immunol. 174, 4812–4820 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382.e374 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Bjerregaard, A.-M. et al. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Bjerregaard, A.-M., Nielsen, M., Hadrup, S. R., Szallasi, Z. & Eklund, A. C. MuPeXI: prediction of neo-epitopes from tumor sequencing data. Cancer Immunol. Immunother. 66, 1123–1130 (2017).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    Toor, J. S. et al. A recurrent mutation in anaplastic lymphoma kinase with distinct neoepitope conformations. Front. Immunol. 9, 99 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).

    CAS  Article  Google Scholar 

  20. 20.

    Riley, T. P. et al. Structure based prediction of neoantigen immunogenicity. Front. Immunol. 10, 2047 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Vianna, P. et al. pMHC structural comparisons as a pivotal element to detect and validate T-cell targets for vaccine development and immunotherapy—a new methodological proposal. Cells 8, 1488 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  22. 22.

    Tanyi, J. L. et al. Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer. Sci. Transl. Med. 10, eaao5931 (2018).

    PubMed  Article  CAS  Google Scholar 

  23. 23.

    Calis, J. J. A. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Chowell, D. et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc. Natl Acad. Sci. USA 112, E1754–E1762 (2015).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Fleri, W. et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front. Immunol. 8, 278 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Hellman, L. M. et al. Differential scanning fluorimetry based assessments of the thermal and kinetic stability of peptide–MHC complexes. J. Immunol. Methods 432, 95–101 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Shapovalov, M. V. & Dunbrack, R. L. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19, 844–858 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Blevins, S. J. & Baker, B. M. Using global analysis to extend the accuracy and precision of binding measurements with T cell receptors and their peptide/MHC ligands. Front. Mol. Biosci. 4, 1–9 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Stone, J. D. & Kranz, D. Role of T cell receptor affinity in the efficacy and specificity of adoptive T cell therapies. Front. Immunol. 4, 244 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Hebeisen, M. et al. Molecular insights for optimizing T cell receptor specificity against cancer. Front. Immunol. 4, 154 (2013).

  32. 32.

    Armstrong, K. M. & Baker, B. M. A comprehensive calorimetric investigation of an entropically driven T cell receptor-peptide/major histocompatibility complex interaction. Biophys. J. 93, 597–609 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Davis-Harrison, R. L., Armstrong, K. M. & Baker, B. M. Two different T cell receptors use different thermodynamic strategies to recognize the same peptide/MHC ligand. J. Mol. Biol. 346, 533–550 (2005).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Spear, T. T. et al. Critical biological parameters modulate affinity as a determinant of function in T-cell receptor gene-modified T-cells. Cancer Immunol. Immunother. 66, 1411–1424 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Tian, S., Maile, R., Collins, E. J. & Frelinger, J. A. CD8+ T cell activation is governed by TCR-peptide/MHC affinity, not dissociation rate. J. Immunol. 179, 2952–2960 (2007).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Boehr, D. D., Nussinov, R. & Wright, P. E. The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol. 5, 789–796 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Rossjohn, J. et al. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Blevins, S. J. et al. How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire. Proc. Natl Acad. Sci. USA 113, E1276–E1285 (2016).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Schmidt, A. G. et al. Preconfiguration of the antigen-binding site during affinity maturation of a broadly neutralizing influenza virus antibody. Proc. Natl Acad. Sci. USA 110, 264–269 (2013).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Manivel, V., Sahoo, N. C., Salunke, D. M. & Rao, K. V. S. Maturation of an antibody response is governed by modulations in flexibility of the antigen-combining site. Immunity 13, 611–620 (2000).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Li, Y., Huang, Y., Swaminathan, C. P., Smith-Gill, S. J. & Mariuzza, R. A. Magnitude of the hydrophobic effect at central versus peripheral sites in protein-protein interfaces. Structure 13, 297–307 (2005).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Sundberg, E. J. et al. Estimation of the hydrophobic effect in an antigen–antibody protein–protein interface. Biochemistry 39, 15375–15387 (2000).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Englert, M. et al. Probing the active site tryptophan of Staphylococcus aureus thioredoxin with an analog. Nucleic Acids Res. 43, 11061–11067 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Theodossis, A. et al. Constraints within major histocompatibility complex class I restricted peptides: presentation and consequences for T-cell recognition. Proc. Natl Acad. Sci. USA 107, 5534–5539 (2010).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Singh, N. K. et al. Emerging concepts in TCR specificity: rationalizing and (maybe) predicting outcomes. J. Immunol. 199, 2203–2213 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Ayres, C. M., Riley, T. P., Corcelli, S. A. & Baker, B. M. Modeling sequence-dependent peptide fluctuations in immunologic recognition. J. Chem. Inf. Model. 57, 1990–1998 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Insaidoo, F. K. et al. Loss of T cell antigen recognition arising from changes in peptide and major histocompatibility complex protein flexibility: implications for vaccine design. J. Biol. Chem. 286, 40163–40173 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Duru, A. D. et al. Tuning antiviral CD8 T-cell response via proline-altered peptide ligand vaccination. PLoS Pathog. 16, e1008244 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Wu, D., Gallagher, D. T., Gowthaman, R., Pierce, B. G. & Mariuzza, R. A. Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen. Nat. Commun. 11, 2908 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Menegatti Rigo, M. et al. DockTope: a web-based tool for automated pMHC-I modelling. Sci. Rep. 5, 18413 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  51. 51.

    Riley, T. P. et al. T cell receptor cross-reactivity expanded by dramatic peptide–MHC adaptability. Nat. Chem. Biol. 14, 934–942 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Wang, Y. et al. How an alloreactive T-cell receptor achieves peptide and MHC specificity. Proc. Natl Acad. Sci. USA 114, E4792–E4801 (2017).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Cole, D. K. et al. Hotspot autoimmune T cell receptor binding underlies pathogen and insulin peptide cross-reactivity. J. Clin. Investig. 126, 2191–2204 (2016).

    PubMed  Article  Google Scholar 

  54. 54.

    Firoz, A., Malik, A., Afzal, O. & Jha, V. ContPro: a web tool for calculating amino acid contact distances in protein from 3D‐structures at different distance threshold. Bioinformation 5, 55–57 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Lovell, S. C., Word, J. M., Richardson, J. S. & Richardson, D. C. The penultimate rotamer library. Proteins 40, 389–408 (2000).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Ayres, C. M., Scott, D. R., Corcelli, S. A. & Baker, B. M. Differential utilization of binding loop flexibility in T cell receptor ligand selection and cross-reactivity. Sci. Rep. 6, 25070 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Zhang, Y. et al. Transduction of human T cells with a novel T-cell receptor confers anti-HCV reactivity. PLoS Pathog. 6, e1001018 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Kowarz, E., Löscher, D. & Marschalek, R. Optimized Sleeping Beauty transposons rapidly generate stable transgenic cell lines. Biotechnol. J. 10, 647–653 (2015).

    CAS  PubMed  Article  Google Scholar 

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We thank L. Hellman for assistance with biophysical and structural work, R. Genolet for assistance with TCR sequences and C. Klebanoff for comments on the manuscript. B.M.B. acknowledges support from the NIGMS, NIH (grant no. R35GM118166). A.H. acknowledges support from the Swiss National Science Foundation (grant no. 310030–182384). D.G. acknowledges support from Swiss Cancer League (grant no. KFS-4104-02-2017). J.R.D. and G.L.J.K. acknowledge support from the Indiana CTSI, funded by the NIH (grant no. UL1TR002529). X-ray diffraction data were collected at the Advanced Photon Source, supported by DOE contract no. DE-AC02-06CH11357, and the NE-CAT and SER-CAT beamlines, supported by member institutions and NIH grants no. P30GM124165, no. S10OD021527, no. S10RR25528 and no. S10RR028976.

Author information




J.R.D. performed crystallography, thermal stability measurements, affinity measurements, kinetic measurements and molecular dynamics simulations. J.A.A. generated cell lines and performed measurements of T-cell function. G.L.J.K. and C.M.A. assisted with molecular dynamics simulations and their analysis. C.W.V.K. assisted with protein crystallography. D.G. and G.C. provided input on the direction of the research. A.H. and S.B. assisted with TCR construction. B.M.B. oversaw the overall project. The manuscript was written and edited by J.R.D., J.A., G.K., D.G., A.H. and B.B.

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Correspondence to Brian M. Baker.

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Extended data

Extended Data Fig. 1 Symmetry-related crystal contacts involving the neoepitope and WT peptide in their respective peptide-HLA-A*02:06 crystal structures.

The top row shows the environment of the neoepitope (left; orange) and the WT peptide (right; green), with symmetry related amino acids containing atoms within 4 Å of the peptides indicated. Atoms within 4 Å are rendered as spheres. The bottom row shows the WT peptide superimposed in the neoepitope environment (left) and the neoepitope superimposed into the WT peptide environment. In both cases, there are no steric clashes between the superimposed peptides and symmetry-related amino acids.

Extended Data Fig. 2 Isothermal calorimetric titration of HHATp8F-HLA-A*02:06 into 302TIL TCR confirms high affinity binding of the TCR to the neoantigen complex.

The fit to the data yielded a KD of 1.7 μM. Data are representative of two independent titrations with similar results (second experiment KD = 1.4 μM).

Extended Data Fig. 3 Overviews of the structures of the 302TIL TCR with the HHATp8F neoepitope and WT peptide-HLA-A*02:06 complexes.

a, Electron density (2Fo-Fc, 1σ) of the CDR loops in the neoepitope complex. b, Overview of the neoepitope ternary complex. The top shows the TCR variable domains, peptide, and the peptide-MHC binding groove (TCR constant domains, β2m, and the HLA-A*02:06 α3 domain are not shown). The bottom shows the positioning of the TCR over the peptide-MHC, with the spheres indicating the centers of mass of the Vα and Vβ domains. The TCR crossing and incident angles are indicated. c, Contact matrices for the 302TIL TCR-neoepitope-HLA-A*02:06 complex. Contacting amino acids are shown, with contacts defined as interatomic distances ≤ 4 Å. The numbers in each cell give the number of interatomic contacts for each amino acid pair; cells are colored according to the number of contacts, from white (minimum) to green (maximum). Red outlines indicate the presence of at least one hydrogen bond or salt-bridge. d–f, As in panels ac except for the complex with the WT peptide.

Extended Data Fig. 4 Comparison of the structures of the 302TIL TCR bound to the HHATp8F and WT peptide-HLA-A*02:06 complexes.

a, Superimposition of the TCR Vα/Vβ regions, peptides, and peptide binding grooves showing the overall similarities. The color scheme is given below and used for all panels. b, Cross-eyed stereo view of the TCR-peptide interface, highlighting the TCR interactions with Trp6 and Phe8/Leu8 of the peptides. c, As in panel b but rotated approximately 45° clockwise as indicated.

Extended Data Fig. 5 Binding and functional data for the HHATp8F neoepitope, WT peptide, and variants.

a, SPR binding curves for 302TIL TCR recognition of each peptide-HLA-A*02:06 complex. Each experiment consists of duplicate injections that were fit simultaneously. For each peptide, one series of duplicate injections and associated fits are shown. Experiments with peptides other than the neoepitope were fit together with a separate neoepitope titration to ensure accurate determination of weak KD values as described in ref. 29. KD values are indicated and reflect the averages and standard deviations of the number of independent experiments shown in Table 1. nbd, no binding detected; p6B, position 6 substituted with the Trp analogue Bta; RU, resonance unit. b, IL-2 production by 302TIL-transduced Jurkat 76 cells when co-cultured with HLA-A*02:06+ T2 cells pulsed with 100 μM peptide. VLF is an irrelevant negative control peptide (VLFGFTNFL). Data points are from three replicates in two independent experiments. Bars show the average of all six data points.

Extended Data Fig. 6 Comparison of tryptophan with 3-benzothienyl-l-alanine (Bta).

Chemical structures are shown on the left, with the sole difference between Trp and Bta found in the substitution of the NH in the tryptophan indole with a sulfur atom. Three dimensional structures are in the center, with an overlay on the right. The conformation of the Bta ring system is taken from ref. 43.

Extended Data Fig. 7 Conformational clustering of the Trp6 sidechain from the MD simulations of the neoepitope and WT peptide-HLA-A*02:06 complexes.

a, Conformations of the five most populated clusters in both simulations. These five accounted for >99% of the conformations sampled in both simulations. Cluster 1 (orange) matches the binding-competent conformation and cluster 4 (green) matches the WT conformation as shown in Figs. 1 and 3 and defined in Fig. 4. b, Total occupancy of the clusters in the two simulations. c, Time resolved charts of the occupancy of clusters. Each simulation enters and exits the binding competent conformation (cluster 1) multiple times, indicating that the greater occupancy of cluster 1 in the neoepitope simulation is not the result of a simulation trapped in a local minimum.

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Devlin, J.R., Alonso, J.A., Ayres, C.M. et al. Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nat Chem Biol 16, 1269–1276 (2020).

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