Original Research ARTICLE
Structure based prediction of neoantigen immunogenicity
- 1University of Notre Dame, United States
- 2Chemistry & Biochemistry, University of Notre Dame, United States
- 3Chemistry and Biochemistry and Harper Cancer Research Institute, University of Notre Dame, United States
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.
Keywords: Structure, neoantigen, peptide, MHC, rosetta, modeling, Neural Network, Personalized vaccines
Received: 06 May 2019;
Accepted: 13 Aug 2019.
Edited by:Nikolaos G. Sgourakis, University of California, Santa Cruz, United States
Reviewed by:Angelika B. Riemer, German Cancer Research Center (DKFZ), Germany
Philip Bradley, Fred Hutchinson Cancer Research Center, United States
Copyright: © 2019 Riley, Keller, Smith, Devlin, Davancaze, Arbuiso and Baker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Mx. Timothy P. Riley, University of Notre Dame, Notre Dame, 46556, Indiana, United States, email@example.com
Prof. Brian M. Baker, University of Notre Dame, Chemistry and Biochemistry and Harper Cancer Research Institute, Notre Dame, 46530, United States, firstname.lastname@example.org