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.
Macrophages are innate immune cells responsible for a variety of tissue-specific homeostatic functions and responding to infiltrating pathogens. A lot of what we know about macrophages comes from studies on unphysiological 2D plastic dishes, however new insights into macrophage biology are emerging thanks to 3D cell culture technology (see the review in this issue by Cutter et al., pages 387–401). Depicted here is a macrophage suspended within a neon 3D dimension. Image provided by Katrina Binger.
Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology
Steven Turner, Jonathan W. Essex, Tim Elliott; Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology. Biochem Soc Trans 27 February 2023; 51 (1): 275–285. doi: https://doi.org/10.1042/BST20220782
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