Structure Based Prediction of Neoantigen Immunogenicity

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.

Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

“…Given that αβ TCRs generally employ a diagonal binding mode to engage pMHC-I antigens where the CDR3α and CDR3β TCR loops form direct contacts with key peptide residues (39, 40), knowledge of the surface features for different epitopes adds an extra layer of information to interpret sequence variability between different viral strains. For other important antigens with known structures in the PDB, such features can be derived from an annotated database connecting pMHC-I/TCR co-crystal structures with biophysical binding data (41), and were recently employed in an artificial neural network approach to predict the immunogenicity of different HLA-A*02:01 bound peptides in the context of tumor neoantigen display (42). A separate study has shown that the electrostatic compatibility between self vs foreign HLA surfaces can be used to determine antibody alloimmune responses (43).…”

Neoantigens in Hematologic Malignancies

“…Presentation of a peptide epitope on an HLA molecule is necessary but not sufficient for T cell recognition. Currently there are no reliable in silico tools to assess the immunogenicity of a neoantigen peptide, although this is an active area of research (65)(66)(67)(68)(69)(70). There are three starting pools of cells in which one can assess the immunogenicity of a putative neoantigen: patient TIL or marrow-infiltrating lymphocytes (MIL), patient peripheral blood T cells, and healthy donor peripheral blood T cells after primary in vitro stimulation.…”

Main Strategies for the Identification of Neoantigens

“…Additionally, MS allows us to reveal tumor-specific post-translational modifications [ 58 , 59 ] and proteasome-generated spliced peptides [ 60 ] that also could contribute to the tumor-specific antigenic landscape. Finally, structure-based approaches could also improve the accuracy of the peptide selection process [ 61 ] by helping identify the effects of the structure and physicochemical properties of peptides on their immunogenic potential. A serious limitation of such approaches is due to the need for high-resolution crystallographic models and significant computing power to implement this analysis [ 32 ].…”

“…Here we attempt to give a brief review of existing methods that are used to investigate neoantigens, including genomics-based in silico predictions, MS- and structure-based approaches, and describe their possible interactions and cross-validation potential. This review does not aim to give a detailed description of the available approaches and tools that are described in numerous reviews (see, e.g., [ 49 , 50 , 52 , 61 , 62 , 63 ]). It is meant to provide a bird’s eye view of the main trends in the context of neoantigen identification, present interactions between different approaches and propose possible improvements.…”