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Title: Structure Based Prediction of Neoantigen Immunogenicity

Journal Article · · Frontiers in Immunology
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  1. Univ. of Notre Dame, IN (United States)
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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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Organization:
National Institutes of Health (NIH); USDOE
OSTI ID:
1577151
Journal Information:
Frontiers in Immunology, Journal Name: Frontiers in Immunology Journal Issue: 08, 2019 Vol. 10; ISSN 1664-3224
Publisher:
Frontiers Research FoundationCopyright Statement
Country of Publication:
United States
Language:
ENGLISH

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Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens journal August 2018
The Length Distribution of Class I–Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele–Specific Binding Preference journal January 2016
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NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data journal October 2017
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TCR contact residue hydrophobicity is a hallmark of immunogenic CD8 + T cell epitopes journal March 2015
How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire journal February 2016
Most viral peptides displayed by class I MHC on infected cells are immunogenic journal February 2019
Unconventional Peptide Presentation by Major Histocompatibility Complex (MHC) Class I Allele HLA-A*02:01: BREAKING CONFINEMENT journal February 2017
Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity journal September 2014
Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information journal January 2018
Computational prediction of neoantigens: do we need more data or new approaches? journal February 2018
PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta journal January 2010
Gapped sequence alignment using artificial neural networks: application to the MHC class I system journal October 2015
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Features and development of Coot journal March 2010
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Reliable prediction of T-cell epitopes using neural networks with novel sequence representations journal May 2003
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Targeting of cancer neoantigens with donor-derived T cell receptor repertoires journal May 2016
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NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data journal October 2017
Increased Immunogenicity of an Anchor-Modified Tumor-Associated Antigen Is Due to the Enhanced Stability of the Peptide/MHC Complex: Implications for Vaccine Design journal April 2005
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Cited By (2)

Neoantigens in Hematologic Malignancies journal February 2020
Additional file 1 of Spatiotemporal evolution of the clear cell renal cell carcinoma microenvironment links intra-tumoral heterogeneity to immune escape dataset January 2023

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
MHC
Rosetta
modeling
neoantigen
neural network
peptide
personalized vaccines
structure