Perpetual complexity: predicting human CD8⁺ T‐cell responses to pathogenic peptides

Abstract

The accurate prediction of human CD8 T-cell epitopes has great potential clinical and translational implications in the context of infection, cancer and autoimmunity. Prediction algorithms have traditionally focused on calculated peptide affinity for the binding groove of MHC-I. However, over the years it has become increasingly clear that the ultimate T-cell recognition of MHC-I-bound peptides is governed by many contributing factors within the complex antigen presentation pathway. Recent advances in next-generation sequencing and immunnopeptidomics have increased the precision of HLA-I sub-allele classification, and have led to the discovery of peptide processing events and individual allele-specific binding preferences. Here, we review some of the discoveries that initiated the development of peptide prediction algorithms, and outline some of the current available online tools for CD8 T-cell epitope prediction.

In vitro proteasome processing of neo-splicetopes does not predict their presentation in vivo

“…The cleavage properties of proteasomes have been intensively studied. However, due to the complexity of protein sequences and lacking information on cleavage strength that decisively determines epitope generation efficiency, algorithms predicting proteasomal generation of immune-relevant non-spliced epitopes still do not reach prediction efficiencies sufficient for large-scale ‘reverse immunology’ approaches ( Calis et al, 2015 ; Di Carluccio et al, 2018 ; Singh and Mishra, 2016 ). Thus, many predicted epitopes may be false-positives, which could impede immunotherapy, for example, neoantigen vaccines.…”

High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array

“…However, these methods work best for MHC molecules that have been extensively characterized. Although they can predict binding for uncharacterized MHC molecules, they still rely on previously generated peptide binding datasets for pattern recognition, creating a self-fulfilling prophecy of sorts (6)(7)(8). A different computational technique, molecular docking, uses the three-dimensional crystal structure of MHC molecules to predict peptides likely to bind (9,10).…”