Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens

Abstract

Using personalized peptide vaccines (PPVs) to target tumor-specific nonself-antigens (neoantigens) is a promising approach to cancer treatment. However, the development of PPVs is hindered by the challenge of identifying tumor-specific neoantigens, in part because current in silico methods for identifying such neoantigens have limited effectiveness. In this article, we report the results of molecular dynamics simulations of 12 oligopeptides bound with an HLA, revealing a previously unrecognized association between the inability of an oligopeptide to elicit a T cell response and the contraction of the peptide-binding groove upon binding of the oligopeptide to the HLA. Our conformational analysis showed that this association was due to incompatibility at the interface between the contracted groove and its αβ–T cell Ag receptor. This structural demonstration that having the capability to bind HLA does not guarantee immunogenicity prompted us to develop an atom-based method (SEFF12MC) to predict immunogenicity through using the structure and energy of a peptide·HLA complex to assess the propensity of the complex for further complexation with its TCR. In predicting the immunogenicities of the 12 oligopeptides, SEFF12MC achieved a 100% success rate, compared with success rates of 25–50% for 11 publicly available residue-based methods including NetMHC-4.0. Although further validation and refinements of SEFF12MC are required, our results suggest a need to develop in silico methods that assess peptide characteristics beyond their capability to form stable binary complexes with HLAs to help remove hurdles in using the patient tumor DNA information to develop PPVs for personalized cancer immunotherapy.

Structure Based Prediction of Neoantigen Immunogenicity

“…Other approaches to model peptides in class I MHC binding grooves have incorporated docking, molecular dynamics simulations, protein threading, or combinations of these methods. These other methods have reported Cα or full-atom RMSD values between model and experiment within the approximate range of 1–2 Å (8, 16, 37, 43–50). Our approach thus compares favorably with or even outperforms other efforts.…”

“…Yet for a peptide bound to an MHC protein, the impact of features such as exposed hydrophobic surface and charges are determined by the peptide's conformation within the binding groove, as well as the size and position of the various amino acid side chains. Thus, efforts to predict peptide immunogenicity should be strengthened by approaches that account for the structural properties of peptide/MHC complexes (8, 16, 35).…”

Neoantigen Cancer Vaccines: Generation, Optimization, and Therapeutic Targeting Strategies

“…To complement existing NGS-based methods for neoantigen identification, various studies have investigated additional quantifiable measures to improve neo-epitope prediction scoring. Novel methods described include peptidome analysis [32,51,52], differentiation from self-antigens [53,54], similarity to viral or bacterial antigens [54,55], RNA expression [51,56], and spatial variation in antigen recognition [57,58]. Additionally, novel advances in the knowledge of neoantigen mechanisms has also indicated that methodologies may have to be even more varied in the future to account for factors other than what is traditionally considered for the peptides [59,60].…”

“…In fact, one study found that mutated anchor residues on epitopes are conservative, and including this knowledge in a prediction algorithm contributed to increased accuracy in neoantigen identification [61]. Additionally, an atom-based method has been used to assess the propensity of the HLA-peptide complex to bind with TCR based on conformational changes that happen after the HLA-peptide association [58].…”

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

“…However, physics-based and structure-based models can provide a rational background to these findings: for instance, the energetic gain of burying a hydrophobic residue by complex formation strongly depends on the 3D structural features. Given these premises, considering structural aspects alongside sequence holds the promise of giving better prediction of peptide immunogenicity and TCRs binding specificities. − …”