T cell antigen receptor recognition of antigen-presenting molecules.

The Major Histocompatibility Complex (MHC) locus encodes classical MHC class I and MHC class II molecules and nonclassical MHC-I molecules. The architecture of these molecules is ideally suited to capture and present an array of peptide antigens (Ags). In addition, the CD1 family members and MR1 are MHC class I-like molecules that bind lipid-based Ags and vitamin B precursors, respectively. These Ag-bound molecules are subsequently recognized by T cell antigen receptors (TCRs) expressed on the surface of T lymphocytes. Structural and associated functional studies have been highly informative in providing insight into these interactions, which are crucial to immunity, and how they can lead to aberrant T cell reactivity. Investigators have determined over thirty unique TCR-peptide-MHC-I complex structures and twenty unique TCR-peptide-MHC-II complex structures. These investigations have shown a broad consensus in docking geometry and provided insight into MHC restriction. Structural studies on TCR-mediated recognition of lipid and metabolite Ags have been mostly confined to TCRs from innate-like natural killer T cells and mucosal-associated invariant T cells, respectively. These studies revealed clear differences between TCR-lipid-CD1, TCR-metabolite-MR1, and TCR-peptide-MHC recognition. Accordingly, TCRs show remarkable structural and biological versatility in engaging different classes of Ag that are presented by polymorphic and monomorphic Ag-presenting molecules of the immune system.

Antigen presentation in the autoimmune diabetes of the NOD mouse.

This paper reviews the presentation of peptides by major histocompatibility complex (MHC) class II molecules in the autoimmune diabetes of the nonobese diabetic (NOD) mouse. Islets of Langerhans contain antigen-presenting cells that capture the proteins and peptides of the beta cells' secretory granules. Peptides bound to I-A(g7), the unique MHC class II molecule of NOD mice, are presented in islets and in pancreatic lymph nodes. The various beta cell-derived peptides interact with selected CD4 T cells to cause inflammation and beta cell demise. Many autoreactive T cells are found in NOD mice, but not all have a major role in the initiation of the autoimmune process. I emphasize here the evidence pointing to insulin autoreactivity as a seminal component in the diabetogenic process.

HLAs, TCRs, and KIRs, a Triumvirate of Human Cell-Mediated Immunity.

In all human cells, human leukocyte antigen (HLA) class I glycoproteins assemble with a peptide and take it to the cell surface for surveillance by lymphocytes. These include natural killer (NK) cells and γδ T cells of innate immunity and αß T cells of adaptive immunity. In healthy cells, the presented peptides derive from human proteins, to which lymphocytes are tolerant. In pathogen-infected cells, HLA class I expression is perturbed. Reduced HLA class I expression is detected by KIR and CD94:NKG2A receptors of NK cells. Almost any change in peptide presentation can be detected by αß CD8+ T cells. In responding to extracellular pathogens, HLA class II glycoproteins, expressed by specialized antigen-presenting cells, present peptides to αß CD4+ T cells. In comparison to the families of major histocompatibility complex (MHC) class I, MHC class II and αß T cell receptors, the antigenic specificity of the γδ T cell receptors is incompletely understood.

De novo identification of CD4+ T cell epitopes.

CD4+ T cells recognize peptide antigens presented on class II major histocompatibility complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of signaling and antigen-presenting bifunctional receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ T cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Furthermore, the SABR-II design is modular in signaling and deployment to T cells and B cells. Here, we demonstrate that SABR-IIs libraries presenting endogenous and non-contiguous epitopes can be used for antigen discovery in the context of type 1 diabetes. SABR-II libraries provide a rapid, flexible, scalable and versatile approach for de novo identification of CD4+ T cell ligands from single-cell RNA sequencing data using experimental and computational approaches.

T cell receptor reversed polarity recognition of a self-antigen major histocompatibility complex.

Central to adaptive immunity is the interaction between the αß T cell receptor (TCR) and peptide presented by the major histocompatibility complex (MHC) molecule. Presumably reflecting TCR-MHC bias and T cell signaling constraints, the TCR universally adopts a canonical polarity atop the MHC. We report the structures of two TCRs, derived from human induced T regulatory (iT(reg)) cells, complexed to an MHC class II molecule presenting a proinsulin-derived peptide. The ternary complexes revealed a 180° polarity reversal compared to all other TCR-peptide-MHC complex structures. Namely, the iT(reg) TCR α-chain and ß-chain are overlaid with the α-chain and ß-chain of MHC class II, respectively. Nevertheless, this TCR interaction elicited a peptide-reactive, MHC-restricted T cell signal. Thus TCRs are not 'hardwired' to interact with MHC molecules in a stereotypic manner to elicit a T cell signal, a finding that fundamentally challenges our understanding of TCR recognition.

Structural insights into human MHC-II association with invariant chain.

The loading of processed peptides on to major histocompatibility complex II (MHC-II) molecules for recognition by T cells is vital to cell-mediated adaptive immunity. As part of this process, MHC-II associates with the invariant chain (Ii) during biosynthesis in the endoplasmic reticulum to prevent premature peptide loading and to serve as a scaffold for subsequent proteolytic processing into MHC-II-CLIP. Cryo-electron microscopy structures of full-length Human Leukocyte Antigen-DR (HLA-DR) and HLA-DQ complexes associated with Ii, resolved at 3.0 to 3.1 Å, elucidate the trimeric assembly of the HLA/Ii complex and define atomic-level interactions between HLA, Ii transmembrane domains, loop domains, and class II-associated invariant chain peptides (CLIP). Together with previous structures of MHC-II peptide loading intermediates DO and DM, our findings complete the structural path governing class II antigen presentation.

Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Energy landscapes of peptide-MHC binding.

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

α-Mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues.

Immunopeptides are cell surface-located protein fragments that aid our immune system to recognise and respond to pathogenic insult and malignant transformation. In this two-part communication, we firstly summarise and reflect on our recent discovery documenting that MHC-II-bound immunopeptides from immortalised cell lines prevalently carry N-glycans that differ from the cellular glycoproteome (Goodson, Front Immunol, 2023). These findings are important as immunopeptide glycosylation remains poorly understood in immunosurveillance. The study also opened up new technical and biological questions that we address in the second part of this communication. Our study highlighted that the performance of the search engines used to detect glycosylated immunopeptides from LC-MS/MS data remains untested and, importantly, that little biochemical in vivo evidence is available to document the nature of glycopeptide antigens in tumour tissues. To this end, we compared the N-glycosylated MHC-II-bound immunopeptides that were reported from tumour tissues of 14 meningioma patients in the MSFragger-HLA-Glyco database (Bedran, Nat Commun, 2023) to those we identified with the commercial Byonic software. Encouragingly, the search engines produced similar outputs supporting that N-glycosylated MHC-II-bound immunopeptides are prevalent in meningioma tumour tissues. Consistent also with in vitro findings, the tissue-derived MHC-II-bound immunopeptides were found to predominantly carry hyper-processed (paucimannosidic- and chitobiose core-type) and hypo-processed (oligomannosidic-type) N-glycans that varied in prevalence and distribution between patients. Taken together, evidence is emerging suggesting that α-mannosidic glycoepitopes abundantly decorate MHC-II-bound immunopeptides presented in both immortalised cells and tumour tissues warranting further research into their functional roles in immunosurveillance.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

The dominantly expressed class II molecule from a resistant MHC haplotype presents only a few Marek's disease virus peptides by using an unprecedented binding motif.

Viral diseases pose major threats to humans and other animals, including the billions of chickens that are an important food source as well as a public health concern due to zoonotic pathogens. Unlike humans and other typical mammals, the major histocompatibility complex (MHC) of chickens can confer decisive resistance or susceptibility to many viral diseases. An iconic example is Marek's disease, caused by an oncogenic herpesvirus with over 100 genes. Classical MHC class I and class II molecules present antigenic peptides to T lymphocytes, and it has been hard to understand how such MHC molecules could be involved in susceptibility to Marek's disease, given the potential number of peptides from over 100 genes. We used a new in vitro infection system and immunopeptidomics to determine peptide motifs for the 2 class II molecules expressed by the MHC haplotype B2, which is known to confer resistance to Marek's disease. Surprisingly, we found that the vast majority of viral peptide epitopes presented by chicken class II molecules arise from only 4 viral genes, nearly all having the peptide motif for BL2*02, the dominantly expressed class II molecule in chickens. We expressed BL2*02 linked to several Marek's disease virus (MDV) peptides and determined one X-ray crystal structure, showing how a single small amino acid in the binding site causes a crinkle in the peptide, leading to a core binding peptide of 10 amino acids, compared to the 9 amino acids in all other reported class II molecules. The limited number of potential T cell epitopes from such a complex virus can explain the differential MHC-determined resistance to MDV, but raises questions of mechanism and opportunities for vaccine targets in this important food species, as well as providing a basis for understanding class II molecules in other species including humans.

Discovery of an ancient MHC category with both class I and class II features.

Two classes of major histocompatibility complex (MHC) molecules, MHC class I and class II, play important roles in our immune system, presenting antigens to functionally distinct T lymphocyte populations. However, the origin of this essential MHC class divergence is poorly understood. Here, we discovered a category of MHC molecules (W-category) in the most primitive jawed vertebrates, cartilaginous fish, and also in bony fish and tetrapods. W-category, surprisingly, possesses class II-type α- and ß-chain organization together with class I-specific sequence motifs for interdomain binding, and the W-category α2 domain shows unprecedented, phylogenetic similarity with ß2-microglobulin of class I. Based on the results, we propose a model in which the ancestral MHC class I molecule evolved from class II-type W-category. The discovery of the ancient MHC group, W-category, sheds a light on the long-standing critical question of the MHC class divergence and suggests that class II type came first.

Loading dynamics of one SARS-CoV-2-derived peptide into MHC-II revealed by kinetic models.

Major histocompatibility complex class II (MHC-II) plays an indispensable role in activating CD4+ T cell immune responses by presenting antigenic peptides on the cell surface for recognition by T cell receptors. The assembly of MHC-II and antigenic peptide is therefore a prerequisite for the antigen presentation. To date, however, the atomic-level mechanism underlying the peptide-loading dynamics for MHC-II is still elusive. Here, by constructing Markov state models based on extensive all-atom molecular dynamics simulations, we reveal the complete peptide-loading dynamics into MHC-II for one SARS-CoV-2 S-protein-derived antigenic peptide (235ITRFQTLLALHRSYL249). Our Markov state model identifies six metastable states (S1-S6) during the peptide-loading process and determines two dominant loading pathways. The peptide could potentially approach the antigen-binding groove via either its N- or C-terminus. Then, the consecutive insertion of several anchor residues into the binding pockets profoundly dictates the peptide-loading dynamics. Notably, the MHC-II αA52-E55 motif could guide the peptide loading into the antigen-binding groove via forming ß-sheets conformation with the incoming peptide. The rate-limiting step, namely S5→S6, is mainly attributed to a considerable desolvation penalty triggered by the binding of the peptide C-terminus. Moreover, we further examined the conformational changes associated with the peptide exchange process catalyzed by the chaperon protein HLA-DM. A flipped-out conformation of MHC-II αW43 captured in S1-S3 is considered a critical anchor point for HLA-DM to modulate the structural dynamics. Our work provides deep structural insights into the key regulatory factors in MHC-II responsible for peptide recognition and guides future design for peptide vaccines against SARS-CoV-2.

Suppression of Plasmodium MIF-CD74 signaling protects against severe malaria.

The deadliest complication of infection by Plasmodium parasites, cerebral malaria, accounts for the majority of malarial fatalities. Although our understanding of the cellular and molecular mechanisms underlying the pathology remains incomplete, recent studies support the contribution of systemic and neuroinflammation as the cause of cerebral edema and blood-brain barrier (BBB) dysfunction. All Plasmodium species encode an orthologue of the innate cytokine, Macrophage Migration Inhibitory Factor (MIF), which functions in mammalian biology to regulate innate responses. Plasmodium MIF (PMIF) similarly signals through the host MIF receptor CD74, leading to an enhanced inflammatory response. We investigated the PMIF-CD74 interaction in the onset of experimental cerebral malaria (ECM) and liver stage Plasmodium development by using a combination of CD74 deficient (Cd74-/- ) hosts and PMIF deficient parasites. Cd74-/- mice were found to be protected from ECM and the protection was associated with the inability of brain microvessels to present parasite antigen to sequestered and pathogenic Plasmodium-specific CD8+ T cells. Infection of WT hosts with PMIF-deficient sporozoites or infection of Cd74-/- hosts with WT sporozoites impacted the survival of infected hepatocytes and subsequently reduced blood-stage associated inflammation, contributing to protection from ECM. We recapitulated these finding with a novel pharmacologic PMIF-selective antagonist that reduced PMIF/CD74 signaling and fully protected mice from ECM. These findings reveal a conserved mechanism for Plasmodium usurpation of host CD74 signaling and suggest a tractable approach for new pharmacologic intervention.

The zinc ions stabilize the three-dimensional structure and are required for the binding of staphylococcal enterotoxin-like protein P (SEIP) with MHC-II receptors.

Staphylococcus aureus is a common human and animal pathogen. These bacteria have various pathogenicity factors, including enterotoxin-like proteins. SElP (staphylococcal enterotoxin-like protein P) has potential zinc ion-binding sites and is able to interact with major histocompatibility complex class II (MHCII) and T-cell receptor (TCR). A method for the expression and isolation of the enterotoxin-like protein of Staphylococcus aureus (SElP) was developed. The expression was carried out in E. coli cells, and the protein was isolated by affinity chromatography on a NiNTA column. The endotoxins were separated by affinity chromatography on Affi-Prep® polymyxin. It was shown by gel filtration that the resulting protein had a monomeric form. The protein in zinc-bound and zinc-free forms was characterized by protein melting using fluorescence method and it was shown that zinc stabilizes the spatial structure of SElP. The functional activity of SElP was investigated by the ability to interact with the histocompatibility antigen class II receptor (MHC-II) exposed on the B cell line Raji by flow cytofluorometry. The zinc-bound and zinc-free forms were shown to differ in their interaction with MHC-II. The localization of the zinc-binding site was confirmed by the introduction of the H225 and D227 mutations. The mutant protein was characterized by melting, and its propensity to form aggregates was shown.

Expanding antigen-specific regulatory networks to treat autoimmunity.

Regulatory T cells hold promise as targets for therapeutic intervention in autoimmunity, but approaches capable of expanding antigen-specific regulatory T cells in vivo are currently not available. Here we show that systemic delivery of nanoparticles coated with autoimmune-disease-relevant peptides bound to major histocompatibility complex class II (pMHCII) molecules triggers the generation and expansion of antigen-specific regulatory CD4(+) T cell type 1 (TR1)-like cells in different mouse models, including mice humanized with lymphocytes from patients, leading to resolution of established autoimmune phenomena. Ten pMHCII-based nanomedicines show similar biological effects, regardless of genetic background, prevalence of the cognate T-cell population or MHC restriction. These nanomedicines promote the differentiation of disease-primed autoreactive T cells into TR1-like cells, which in turn suppress autoantigen-loaded antigen-presenting cells and drive the differentiation of cognate B cells into disease-suppressing regulatory B cells, without compromising systemic immunity. pMHCII-based nanomedicines thus represent a new class of drugs, potentially useful for treating a broad spectrum of autoimmune conditions in a disease-specific manner.

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data.

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.

Amino acid signatures of HLA Class-I and II molecules are strongly associated with SLE susceptibility and autoantibody production in Eastern Asians.

Human leukocyte antigen (HLA) is a key genetic factor conferring risk of systemic lupus erythematosus (SLE), but precise independent localization of HLA effects is extremely challenging. As a result, the contribution of specific HLA alleles and amino-acid residues to the overall risk of SLE and to risk of specific autoantibodies are far from completely understood. Here, we dissected (a) overall SLE association signals across HLA, (b) HLA-peptide interaction, and (c) residue-autoantibody association. Classical alleles, SNPs, and amino-acid residues of eight HLA genes were imputed across 4,915 SLE cases and 13,513 controls from Eastern Asia. We performed association followed by conditional analysis across HLA, assessing both overall SLE risk and risk of autoantibody production. DR15 alleles HLA-DRB1*15:01 (P = 1.4x10-27, odds ratio (OR) = 1.57) and HLA-DQB1*06:02 (P = 7.4x10-23, OR = 1.55) formed the most significant haplotype (OR = 2.33). Conditioned protein-residue signals were stronger than allele signals and mapped predominantly to HLA-DRB1 residue 13 (P = 2.2x10-75) and its proxy position 11 (P = 1.1x10-67), followed by HLA-DRB1-37 (P = 4.5x10-24). After conditioning on HLA-DRB1, novel associations at HLA-A-70 (P = 1.4x10-8), HLA-DPB1-35 (P = 9.0x10-16), HLA-DQB1-37 (P = 2.7x10-14), and HLA-B-9 (P = 6.5x10-15) emerged. Together, these seven residues increased the proportion of explained heritability due to HLA to 2.6%. Risk residues for both overall disease and hallmark autoantibodies (i.e., nRNP: DRB1-11, P = 2.0x10-14; DRB1-13, P = 2.9x10-13; DRB1-30, P = 3.9x10-14) localized to the peptide-binding groove of HLA-DRB1. Enrichment for specific amino-acid characteristics in the peptide-binding groove correlated with overall SLE risk and with autoantibody presence. Risk residues were in primarily negatively charged side-chains, in contrast with rheumatoid arthritis. We identified novel SLE signals in HLA Class I loci (HLA-A, HLA-B), and localized primary Class II signals to five residues in HLA-DRB1, HLA-DPB1, and HLA-DQB1. These findings provide insights about the mechanisms by which the risk residues interact with each other to produce autoantibodies and are involved in SLE pathophysiology.

Structural aspects of chaperone-mediated peptide loading in the MHC-I antigen presentation pathway.

Recognition of foreign and dysregulated antigens by the cellular innate and adaptive immune systems is in large part dependent on the cell surface display of peptide/MHC (pMHC) complexes. The formation of such complexes requires the generation of antigenic peptides, proper folding of MHC molecules, loading of peptides onto MHC molecules, glycosylation, and transport to the plasma membrane. This complex series of biosynthetic, biochemical, and cell biological reactions is known as "antigen processing and presentation". Here, we summarize recent work, focused on the structural and functional characterization of the key MHC-I-dedicated chaperones, tapasin, and TAPBPR. The mechanisms reflect the ability of conformationally flexible molecules to adapt to their ligands, and are comparable to similar processes that are exploited in peptide antigen loading in the MHC-II pathway.