The ERAP1 active site cannot productively access the N-terminus of antigenic peptide precursors stably bound onto MHC class I

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

CD8+ T-cell responses towards conserved influenza B virus epitopes across anatomical sites and age

Influenza B viruses (IBVs) cause substantive morbidity and mortality, and yet immunity towards IBVs remains understudied. CD8+ T-cells provide broadly cross-reactive immunity and alleviate disease severity by recognizing conserved epitopes. Despite the IBV burden, only 18 IBV-specific T-cell epitopes restricted by 5 HLAs have been identified currently. A broader array of conserved IBV T-cell epitopes is needed to develop effective cross-reactive T-cell based IBV vaccines. Here we identify 9 highly conserved IBV CD8+ T-cell epitopes restricted to HLA-B*07:02, HLA-B*08:01 and HLA-B*35:01. Memory IBV-specific tetramer+CD8+ T-cells are present within blood and tissues. Frequencies of IBV-specific CD8+ T-cells decline with age, but maintain a central memory phenotype. HLA-B*07:02 and HLA-B*08:01-restricted NP30-38 epitope-specific T-cells have distinct T-cell receptor repertoires. We provide structural basis for the IBV HLA-B*07:02-restricted NS1196-206 (11-mer) and HLA-B*07:02-restricted NP30-38 epitope presentation. Our study increases the number of IBV CD8+ T-cell epitopes, and defines IBV-specific CD8+ T-cells at cellular and molecular levels, across tissues and age.

Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology

Antigen processing is an immunological mechanism by which intracellular peptides are transported to the cell surface while bound to Major Histocompatibility Complex molecules, where they can be surveyed by circulating CD8+ or CD4+ T-cells, potentially triggering an immunological response. The antigen processing pathway is a complex multistage filter that refines a huge pool of potential peptide ligands derived from protein degradation into a smaller ensemble for surface presentation. Each stage presents unique challenges due to the number of ligands, the polymorphic nature of MHC and other protein constituents of the pathway and the nature of the interactions between them. Predicting the ensemble of displayed peptide antigens, as well as their immunogenicity, is critical for improving T cell vaccines against pathogens and cancer. Our predictive abilities have always been hindered by an incomplete empirical understanding of the antigen processing pathway. In this review, we highlight the role of computational and structural approaches in improving our understanding of antigen processing, including structural biology, computer simulation, and machine learning techniques, with a particular focus on the MHC-I pathway.

Can ERAP1 and ERAP2 Form Functional Heterodimers? A Structural Dynamics Investigation

Endoplasmic reticulum aminopeptidases 1 and 2 (ERAP1 and ERAP2) play important roles in the generation of antigenic peptides presented by Major Histocompatibility Class I (MHCI) molecules and indirectly regulate adaptive immune responses. Although the discrete function of these enzymes has been extensively characterized, recent reports have suggested that they can also form heterodimers with functional consequences. However, lack of structural characterization of a putative ERAP1/ERAP2 dimer has limited our understanding of its biological role and significance. To address this, we employed computational molecular dynamics calculations to explore the topology of interactions between these two, based on experimentally determined homo-dimerization interfaces observed in crystal structures of ERAP2 or homologous enzymes. Our analysis of 8 possible dimerization models, suggested that the most likely ERAP1/ERAP2 heterodimerization topology involves the exon 10 loop, a non-conserved loop previously implicated in interactions between ERAP1 and the disulfide-bond shuffling chaperone ERp44. This dimerization topology allows access to the active site of both enzymes and is consistent with a previously reported construct in which ERAP1 and ERAP2 were linked by Fos/Jun zipper tags. The proposed model constitutes a tentative structural template to help understand the physiological role and significance of ERAP1/ERAP2 molecular interactions.

ERAP2 Inhibition Induces Cell-Surface Presentation by MOLT-4 Leukemia Cancer Cells of Many Novel and Potentially Antigenic Peptides

Recent studies have linked the activity of ER aminopeptidase 2 (ERAP2) to increased efficacy of immune-checkpoint inhibitor cancer immunotherapy, suggesting that pharmacological inhibition of ERAP2 could have important therapeutic implications. To explore the effects of ERAP2 inhibition on the immunopeptidome of cancer cells, we treated MOLT-4 T lymphoblast leukemia cells with a recently developed selective ERAP2 inhibitor, isolated Major Histocompatibility class I molecules (MHCI), and sequenced bound peptides by liquid chromatography tandem mass spectrometry. Inhibitor treatment induced significant shifts on the immunopeptidome so that more than 20% of detected peptides were either novel or significantly upregulated. Most of the inhibitor-induced peptides were 9mers and had sequence motifs and predicted affinity consistent with being optimal ligands for at least one of the MHCI alleles carried by MOLT-4 cells. Such inhibitor-induced peptides could serve as triggers for novel cytotoxic responses against cancer cells and synergize with the therapeutic effect of immune-checkpoint inhibitors.