Structural principles that govern the peptide-binding motifs of class I MHC molecules

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.

CRISPR-Cas9 screening reveals a distinct class of MHC-I binders with precise HLA-peptide recognition

Human leukocyte antigen (HLA) class-I molecules present fragments of the cellular proteome to the T cell receptor (TCR) of cytotoxic T cells to control infectious diseases and cancer. The large number of combinations of HLA class-I allotypes and peptides allows for highly specific and dedicated low-affinity interactions to a diverse array of TCRs and natural killer (NK) cell receptors. Whether the divergent HLA class-I peptide complex is exclusive for interactions with these proteins is unknown. Using genome-wide CRISPR-Cas9 activation and knockout screens, we identified peptide-specific HLA-C∗07 combinations that can interact with the surface molecules CD55 and heparan sulfate. These interactions closely resemble the HLA class-I interaction with the TCR regarding both the affinity range and the specificity of the peptide and HLA allele. These findings indicate that various proteins can specifically bind HLA class-I peptide complexes due to their polymorphic nature, which suggests there are more interactions like the ones we describe here.

Differences in F pocket impact on HLA I genetic associations with autoimmune diabetes

Introduction

Human leukocyte antigen (HLA) I molecules present antigenic peptides to activate CD8+ T cells. Type 1 Diabetes (T1D) is an auto-immune disease caused by aberrant activation of the CD8+ T cells that destroy insulin-producing pancreatic β cells. Some HLA I alleles were shown to increase the risk of T1D (T1D-predisposing alleles), while some reduce this risk (T1D-protective alleles).

Methods

Here, we compared the T1D-predisposing and T1D-protective allotypes concerning peptide binding, maturation, localization and surface expression and correlated it with their sequences and energetic profiles using experimental and computational methods.

Results

T1D-predisposing allotypes had more peptide-bound forms and higher plasma membrane levels than T1D-protective allotypes. This was related to the fact that position 116 within the F pocket was more conserved and made more optimal contacts with the neighboring residues in T1D-predisposing allotypes than in protective allotypes.

Conclusion

Our work uncovers that specific polymorphisms in HLA I molecules potentially influence their susceptibility to T1D.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.

Immunodominant T cell peptides from four candidate malarial antigens as biomarkers of protective immunity against malaria

A malaria vaccine with high efficacy and capable of inducing sterile immunity against malaria within genetically diverse populations is urgently needed to complement ongoing disease control and elimination efforts. Parasite-specific IFN-γ and granzyme B-secreting CD8 + T cells have been identified as key mediators of protection and the rapid identification of malaria antigen targets that elicit these responses will fast-track the development of simpler, cost-effective interventions. This study extends our previous work which used peripheral blood mononuclear cells (PBMCs) from adults with life-long exposure to malaria parasites to identify immunodominant antigen-specific peptide pools composed of overlapping 15mer sequences spanning full length proteins of four malarial antigens. Our current study aimed to identify CD8 + T cell epitopes within these previously identified positive peptide pools. Cryopreserved PBMCs from 109 HLA-typed subjects were stimulated with predicted 9-11mer CD8 + T cell epitopes from P. falciparum circumsporozoite protein (CSP), apical membrane antigen 1 (AMA1), thrombospondin related anonymous protein (TRAP) and cell traversal for ookinetes and sporozoites (CelTOS) in FluoroSpot assays. A total of 135 epitopes out of 297 tested peptides from the four antigens were experimentally identified as positive for IFN-γ and/or granzyme B production in 65 of the 109 subjects. Forty-three of 135 epitopes (32 %) were promiscuous for HLA binding, with 31 of these promiscuous epitopes (72 %) being presented by HLA alleles that fall within at least two different HLA supertypes. Furthermore, about 52 % of identified epitopes were conserved when the respective sequences were aligned with those from 16 highly diverse P. falciparum parasite strains. In summary, we have identified a number of conserved epitopes, immune responses to which could be effective against multiple P. falciparum parasite strains in genetically diverse populations.

HLA Class I Supertype Classification Based on Structural Similarity

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.

Characterization of the Class I MHC Peptidome Resulting From DNCB Exposure of HaCaT Cells

Skin sensitization following the covalent modification of proteins by low molecular weight chemicals (haptenation) is mediated by cytotoxic T lymphocyte (CTL) recognition of human leukocyte antigen (HLA) molecules presented on the surface of almost all nucleated cells. There exist 3 nonmutually exclusive hypotheses for how haptens mediate CTL recognition: direct stimulation by haptenated peptides, hapten modification of HLA leading to an altered HLA-peptide repertoire, or a hapten altered proteome leading to an altered HLA-peptide repertoire. To shed light on the mechanism underpinning skin sensitization, we set out to utilize proteomic analysis of keratinocyte presented antigens following exposure to 2,4-dinitrochlorobenzene (DNCB). We show that the following DNCB exposure, cultured keratinocytes present cysteine haptenated (dinitrophenylated) peptides in multiple HLA molecules. In addition, we find that one of the DNCB modified peptides derives from the active site of cytosolic glutathione-S transferase-ω. These results support the current view that a key mechanism of skin sensitization is stimulation of CTLs by haptenated peptides. Data are available via ProteomeXchange with identifier PXD021373.

Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches

Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5-832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.

Pre-T cell receptors topologically sample self-ligands during thymocyte β-selection

Self-discrimination, a critical but ill-defined molecular process programmed during thymocyte development, requires myriad pre-T cell receptors (preTCRs) and αβTCRs. Using x-ray crystallography, we show how a preTCR applies the concave β-sheet surface of its single variable domain (Vβ) to "horizontally" grab the protruding MHC α2-helix. By contrast, αβTCRs purpose all six complementarity-determining region (CDR) loops of their paired VαVβ module to recognize peptides bound to major histocompatibility complex molecules (pMHCs) in "vertical" head-to-head binding. The preTCR topological fit ensures that CDR3β reaches the peptide's featured C-terminal segment for pMHC sampling, establishing the subsequent αβTCR canonical docking mode. "Horizontal" docking precludes germline CDR1β- and CDR2β-MHC binding to broaden β-chain repertoire diversification before αβTCR-mediated selection refinement. Thus, one subunit successively attunes the recognition logic of related multicomponent receptors.

Identification and Structure of an MHC Class I-Encoded Protein with the Potential to Present N-Myristoylated 4-mer Peptides to T Cells

Similar to host proteins, N-myristoylation occurs for viral proteins to dictate their pathological function. However, this lipid-modifying reaction creates a novel class of "lipopeptide" Ags targeted by host CTLs. The primate MHC class I-encoded protein, Mamu-B*098, was previously shown to bind N-myristoylated 5-mer peptides. Nevertheless, T cells exist that recognize even shorter lipopeptides, and much remains to be elucidated concerning the molecular mechanisms of lipopeptide presentation. We, in this study, demonstrate that the MHC class I allele, Mamu-B*05104, binds the N-myristoylated 4-mer peptide (C14-Gly-Gly-Ala-Ile) derived from the viral Nef protein for its presentation to CTLs. A phylogenetic tree analysis indicates that these classical MHC class I alleles are not closely associated; however, the high-resolution x-ray crystallographic analyses indicate that both molecules share lipid-binding structures defined by the exceptionally large, hydrophobic B pocket to accommodate the acylated glycine (G1) as an anchor. The C-terminal isoleucine (I4) of C14-Gly-Gly-Ala-Ile anchors at the F pocket, which is distinct from that of Mamu-B*098 and is virtually identical to that of the peptide-presenting MHC class I molecule, HLA-B51. The two central amino acid residues (G2 and A3) are only exposed externally for recognition by T cells, and the methyl side chain on A3 constitutes a major T cell epitope, underscoring that the epitopic diversity is highly limited for lipopeptides as compared with that for MHC class I-presented long peptides. These structural features suggest that lipopeptide-presenting MHC class I alleles comprise a distinct MHC class I subset that mediates an alternative pathway for CTL activation.

Cancer vaccine strategies: translation from mice to human clinical trials

We translated two cancer vaccine strategies from mice into human clinical trials. (1) In preclinical studies on TARP, an antigen expressed in most prostate cancers, we mapped epitopes presented by HLA-A*0201, modified them to increase affinity and immunogenicity in HLA transgenic mice, and induced human T cells that killed human cancer cells ("epitope enhancement"). In a clinical trial, HLA-A2⁺ prostate cancer patients with PSA biochemical recurrence (Stage D0) were vaccinated with two peptides either in Montanide-ISA51 or on autologous dendritic cells (DCs). In stage D0, the Prostate-Specific Antigen (PSA) slope is prognostic of time to radiographic evidence of metastases and death. With no difference between arms, 74% of combined subjects had a decreased PSA slope at 1 year compared to their own baseline slopes (p = 0.0004). For patients vaccinated with DCs, response inversely correlated with a tolerogenic DC signature. A randomized placebo-controlled phase II trial is underway. (2) HER2 is a driver surface oncogene product expressed in multiple tumors. We made an adenoviral vector vaccine expressing the extracellular and transmembrane domains of HER2 and cured mice with large established HER2⁺ tumors, dependent on antibodies to HER2, not T cells. The mechanism differed from that of trastuzumab. We tested a human version in advanced metastatic cancer patients naïve to HER2-directed therapies. At the second and third dose levels, 45% of evaluable patients showed clinical benefit. Circulating tumor cells also declined in some vaccinated patients. Thus, cancer vaccines developed in mice were successfully translated to humans with promising early results.

Nuclear export receptor CRM1 recognizes diverse conformations in nuclear export signals

Nuclear export receptor CRM1 binds highly variable nuclear export signals (NESs) in hundreds of different cargoes. Previously we have shown that CRM1 binds NESs in both polypeptide orientations (Fung et al., 2015). Here, we show crystal structures of CRM1 bound to eight additional NESs which reveal diverse conformations that range from loop-like to all-helix, which occupy different extents of the invariant NES-binding groove. Analysis of all NES structures show 5-6 distinct backbone conformations where the only conserved secondary structural element is one turn of helix that binds the central portion of the CRM1 groove. All NESs also participate in main chain hydrogen bonding with human CRM1 Lys568 side chain, which acts as a specificity filter that prevents binding of non-NES peptides. The large conformational range of NES backbones explains the lack of a fixed pattern for its 3-5 hydrophobic anchor residues, which in turn explains the large array of peptide sequences that can function as NESs.

Identification of the core regulators of the HLA I-peptide binding process

During the display of peptide/human leukocyte antigen (HLA) -I complex for further immune recognition, the cleaved and transported antigenic peptides have to bind to HLA-I protein and the binding affinity between peptide epitopes and HLA proteins directly influences the immune recognition ability in human beings. Key factors affecting the binding affinity during the generation, selection and presentation processes of HLA-I complex have not yet been fully discovered. In this study, a new method describing the HLA class I-peptide interactions was proposed. Three hundred and forty features of HLA I proteins and peptide sequences were utilized for analysis by four candidate algorithms, screening the optimal classifier. Features derived from the optimal classifier were further selected and systematically analyzed, revealing the core regulators. The results validated the hypothesis that features of HLA I proteins and related peptides simultaneously affect the binding process, though with discrepant redundancy. Besides, the high relative ratio (16/20) of the amino acid composition features suggests the unique role of sequence signatures for the binding processes. Integrating biological, evolutionary and chemical features of both HLA I molecules and peptides, this study may provide a new perspective of the underlying mechanisms of HLA I-mediated immune reactions.

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

Balancing selection and recombination as evolutionary forces caused population genetic variations in golden pheasant MHC class I genes

BACKGROUND

The major histocompatibility complex (MHC) genes are vital partners in the acquired immune processes of vertebrates. MHC diversity may be directly associated with population resistance to infectious pathogens. Here, we screened for polymorphisms in exons 2 and 3 of the IA1 and IA2 genes in 12 golden pheasant populations across the Chinese mainland to characterize their genetic variation levels, to understand the effects of historical positive selection and recombination in shaping class I diversity, and to investigate the genetic structure of wild golden pheasant populations.

RESULTS

Among 339 individual pheasants, we identified 14 IA1 alleles in exon 2 (IA1-E2), 11 IA1-E3 alleles, 27 IA2-E2 alleles, and 28 IA2-E3 alleles. The non-synonymous substitution rate was significantly greater than the synonymous substitution rate at sequences in the IA2 gene encoding putative peptide-binding sites but not in the IA1 gene; we also found more positively selected sites in IA2 than in IA1. Frequent recombination events resulted in at least 9 recombinant IA2 alleles, in accordance with the intermingling pattern of the phylogenetic tree. Although some IA alleles are widely shared among studied populations, large variation occurs in the number of IA alleles across these populations. Allele frequency analysis across 2 IA loci showed low levels of genetic differentiation among populations on small geographic scales; however, significant genetic differentiation was observed between pheasants from the northern and southern regions of the Yangtze River. Both STRUCTURE analysis and F-statistic (F ST ) value comparison classified those populations into 2 major groups: the northern region of the Yangtze River (NYR) and the southern region of the Yangtze River (SYR).

CONCLUSIONS

More extensive polymorphisms in IA2 than IA1 indicate that IA2 has undergone much stronger positive-selection pressure during evolution. Moreover, the recombination events detected between the genes and the intermingled phylogenetic pattern indicate that interlocus recombination accounts for much of the allelic variation in IA2. Analysis of the population differentiation implied that homogenous balancing selection plays an important part in maintaining an even distribution of MHC variations. The natural barrier of the Yangtze River and heterogeneous balancing selection might help shape the NYR-SYR genetic structure in golden pheasants.

The first step of peptide selection in antigen presentation by MHC class I molecules

MHC class I molecules present a variable but limited repertoire of antigenic peptides for T-cell recognition. Understanding how peptide selection is achieved requires mechanistic insights into the interactions between the MHC I and candidate peptides. We find that, at first encounter, MHC I H-2K(b) considers a wide range of peptides, including those with expanded N termini and unfitting anchor residues. Discrimination occurs in the second step, when noncanonical peptides dissociate with faster exchange rates. This second step exhibits remarkable temperature sensitivity, as illustrated by numerous noncanonical peptides presented by H-2K(b) in cells cultured at 26 °C relative to 37 °C. Crystallographic analyses of H-2K(b)-peptide complexes suggest that a conformational adaptation of H-2K(b) drives the decisive step in peptide selection. We propose that MHC class I molecules consider initially a large peptide pool, subsequently refined by a temperature-sensitive induced-fit mechanism to retain the canonical peptide repertoire.

Clustering HLA class I superfamilies using structural interaction patterns

Human leukocyte antigen (HLA) class I molecules are critical components of the cell-mediated immune system that bind and present intracellular antigenic peptides to CD8(+) T cell receptors. To understand the interaction mechanism underlying human leukocyte antigen (HLA) class I specificity in detail, we studied the structural interaction characteristics of 16,393 nonameric peptides binding to 58 HLA-A and -B molecules. Our analysis showed for the first time that HLA-peptide intermolecular bonding patterns vary among different alleles and may be grouped in a superfamily dependent manner. Through the use of these HLA class I 'fingerprints', a high resolution HLA class I superfamily classification schema was developed. This classification is capable of separating HLA alleles into well resolved, non-overlapping clusters, which is consistent with known HLA superfamily definitions. Such structural interaction approach serves as an excellent alternative to the traditional methods of HLA superfamily definitions that use peptide binding motifs or receptor information, and will help identify appropriate antigens suitable for broad-based subunit vaccine design.

Genome-derived vaccines

Vaccine research entered a new era when the complete genome of a pathogenic bacterium was published in 1995. Since then, more than 97 bacterial pathogens have been sequenced and at least 110 additional projects are now in progress. Genome sequencing has also dramatically accelerated: high-throughput facilities can draft the sequence of an entire microbe (two to four megabases) in 1 to 2 days. Vaccine developers are using microarrays, immunoinformatics, proteomics and high-throughput immunology assays to reduce the truly unmanageable volume of information available in genome databases to a manageable size. Vaccines composed by novel antigens discovered from genome mining are already in clinical trials. Within 5 years we can expect to see a novel class of vaccines composed by genome-predicted, assembled and engineered T- and Bcell epitopes. This article addresses the convergence of three forces--microbial genome sequencing, computational immunology and new vaccine technologies--that are shifting genome mining for vaccines onto the forefront of immunology research.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

Dipeptides promote folding and peptide binding of MHC class I molecules

MHC class I molecules bind only those peptides with high affinity that conform to stringent length and sequence requirements. We have now investigated which peptides can aid the in vitro folding of class I molecules, and we find that the dipeptide glycyl-leucine efficiently supports the folding of HLA-A*02:01 and H-2K(b) into a peptide-receptive conformation that rapidly binds high-affinity peptides. Treatment of cells with glycyl-leucine induces accumulation of peptide-receptive H-2K(b) and HLA-A*02:01 at the surface of cells. Other dipeptides with a hydrophobic second amino acid show similar enhancement effects. Our data suggest that the dipeptides bind into the F pocket like the C-terminal amino acids of a high-affinity peptide.

Structural and functional distinctiveness of HLA-A2 allelic variants

Human leukocyte antigen (HLA) class I molecules are involved in the presentation of antigenic peptides to CD8(+) cytotoxic T lymphocytes (CTLs), which is important for the development of cellular immunity during viral infections and in cancers. HLA-A2 is one of the most frequent HLA class I specificities and thus is extensively studied structurally and functionally. Since its discovery, more than 300 allelic variants of this HLA specificity have been recorded. Among the HLA-A2 allelic variants, HLA-A*02:01 is the most prevalent, hence commonly used as a model to study HLA-A2-restricted CTL responses. However, HLA-A2 alleles are unevenly distributed globally such that HLA-A2 allelic variants besides A*02:01 are expressed at considerably high frequencies in Asian and African populations. Furthermore, increasing evidence of variations in the peptide-binding repertoire and CTL responses among HLA-A2 allelic variants suggests the need to understand these differences among various frequently expressed HLA-A2 molecules. In this review, the structural and functional distinctiveness of HLA-A2 allelic variants will be discussed.

Immunoglobulin light chain, Blimp-1 and cytochrome P4501B1 peptides as potential vaccines for AL amyloidosis

Amyloid light chain (AL) amyloidosis is a lethal disorder characterized by the pathologic deposition of clonal plasma cell-derived, fibrillogenic immunoglobulin light chains in vital organs. Current chemotherapeutic regimens are problematic in patients with compromised organ function and are not effective for all patients. Here, a platform of computer-based prediction and preclinical mouse modeling was used to begin development of a complementary, immunotherapeutic approach for AL amyloidosis. Three peptide/MHC I-binding algorithms identified immunogenic peptides from three AL plasma cell-associated proteins: (1) amyloidogenic λ6 light chains, (2) CYP1B1, a universal tumor antigen hyper-expressed in AL plasma cells and (3) B lymphocyte-induced maturation protein 1 (Blimp-1), a transcription factor required for plasma cell differentiation. The algorithms correctly predicted HLA-A(*)0201-binding native and heteroclitic peptides. In HLA-A2 transgenic mice, these peptides, given individually or in combination, induced potent CTL which kill peptide-loaded human lymphoma cells and/or lymphoma cells producing target protein. Blimp-1 peptide-immunized mice exhibited a reduced percentage of splenic, lymph node and bone marrow plasma cells and a decrease in the absolute number of splenic plasma cells demonstrating (1) presentation of target peptide by endogenous plasma cells and (2) appropriate CTL homing to lymphoid organs followed by killing of target plasma cells. These studies suggest that AL amyloidosis, with its relatively low tumor cell burden, may be an attractive target for peptide-based multivalent vaccines.

Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes

Previous studies have attempted to define human leukocyte antigen (HLA) class II supertypes, analogous to the case for class I, on the basis of shared peptide-binding motifs or structure. In the present study, we determined the binding capacity of a large panel of non-redundant peptides for a set of 27 common HLA DR, DQ, and DP molecules. The measured binding data were then used to define class II supertypes on the basis of shared binding repertoires. Seven different supertypes (main DR, DR4, DRB3, main DQ, DQ7, main DP, and DP2) were defined. The molecules associated with the respective supertypes fell largely along lines defined by MHC locus and reflect, in broad terms, commonalities in reported peptide-binding motifs. Repertoire overlaps between molecules within the same class II supertype were found to be similar in magnitude to what has been observed for HLA class I supertypes. Surprisingly, however, the degree to which repertoires between molecules in the different class II supertypes also overlapped was found to be five to tenfold higher than repertoire overlaps noted between molecules in different class I supertypes. These results highlight a high degree of repertoire overlap amongst all HLA class II molecules, perhaps reflecting binding in multiple registers, and more pronounced dependence on backbone interactions rather than peptide anchor residues. This fundamental difference between HLA class I and class II would not have been predicted on the basis of analysis of either binding motifs or the sequence/predicted structures of the HLA molecules.

Cheetah paradigm revisited: MHC diversity in the world's largest free-ranging population

For more than two decades, the cheetah (Acinonyx jubatus) has been considered a paradigm of disease vulnerability associated with low genetic diversity, particularly at the immune genes of the major histocompatibility complex (MHC). Cheetahs have been used as a classic example in numerous conservation genetics textbooks as well as in many related scientific publications. However, earlier studies used methods with low resolution to quantify MHC diversity and/or small sample sizes. Furthermore, high disease susceptibility was reported only for captive cheetahs, whereas free-ranging cheetahs show no signs of infectious diseases and a good general health status. We examined whether the diversity at MHC class I and class II-DRB loci in 149 Namibian cheetahs was higher than previously reported using single-strand conformation polymorphism analysis, cloning, and sequencing. MHC genes were examined at the genomic and transcriptomic levels. We detected ten MHC class I and four class II-DRB alleles, of which nine MHC class I and all class II-DRB alleles were expressed. Phylogenetic analyses and individual genotypes suggested that the alleles belong to four MHC class I and three class II-DRB putative loci. Evidence of positive selection was detected in both MHC loci. Our study indicated that the low number of MHC class I alleles previously observed in cheetahs was due to a smaller sample size examined. On the other hand, the low number of MHC class II-DRB alleles previously observed in cheetahs was further confirmed. Compared with other mammalian species including felids, cheetahs showed low levels of MHC diversity, but this does not seem to influence the immunocompetence of free-ranging cheetahs in Namibia and contradicts the previous conclusion that the cheetah is a paradigm species of disease vulnerability.

Peptide synthesis of gold nanoparticles: the early steps of gold reduction investigated by density functional theory

Gold nanoparticles can be synthesized by reducing chloroaurate(III) ions in the presence of peptides. Here, such reduction for serine and tyrosine is studied by density functional theory including solvent effects. We find that the formation of chloroaurate complexes of these amino acids is thermodynamically viable and facilitates the reduction of Au(III), to a greater degree for tyrosine as found in experiments. Our results also suggest a rationale for the behavior of tyrosine-intercalated peptides.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines

Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

Essential differences in ligand presentation and T cell epitope recognition among HLA molecules of the HLA-B44 supertype

Human leukocyte antigens (HLA) have long been grouped into supertypes to facilitate peptide-based immunotherapy. Analysis of several hundreds of peptides presented by all nine antigens of the HLA-B44 supertype (HLA-B*18, B*37, B*40, B*41, B*44, B*45, B*47, B*49 and B*50) revealed unique peptide motifs for each of them. Taking all supertype members into consideration only 25 out of 670 natural ligands were found on more than one HLA molecule. Further direct comparisons by two mass spectrometric methods--isotope labeling as well as a label-free approach--consistently demonstrated only minute overlaps of below 3% between the ligandomes of different HLA antigens. In addition, T cell reactions of healthy donors against immunodominant HLA-B*44 and HLA-B*40 epitopes from EBV lacked promiscuous T-cell recognition within the HLA-B44 supertype. Taken together, these results challenge the common paradigm of broadly presented epitopes within this supertype.

Improving peptide-MHC class I binding prediction for unbalanced datasets

Background

Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms.

Results

We have developed a decision-theoretic framework to construct cost-sensitive trees to predict peptide-MHCI binding and have used them to 1) Assess the impact of the training data's class distribution on classifier accuracy, and 2) Compare resampling and cost-sensitive methods as approaches to compensate for training data imbalance. Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI binding context, resampling methods do not improve the classifier performance. In contrast, cost-sensitive methods significantly improve accuracy of decision trees. Finally, we propose the use of a training scheme that, when the training set is enriched for nonbinders, consistently improves the overall classifier accuracy compared to cost-insensitive classifiers and, in particular, increases the sensitivity of the classifiers. This method minimizes the expected classification cost for large datasets.

Conclusion

Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing techniques to compensate for the imbalance in the training dataset.

Structural basis for HLA-A2 supertypes

The human leukocyte antigen (HLA) alleles are extremely polymorphic among ethnic population, and the peptide-binding specificity varies for different alleles in a combinatorial manner. However, it has been suggested that majority of alleles can be covered within few HLA supertypes, where different members of a supertype bind similar peptides, yet exhibiting distinct repertoires. Nonetheless, the structural basis for HLA supertype-like function is not clearly known. Here, we use structural data to explain the molecular basis for HLA-A2 supertypes.

HLA class I supertypes: a revised and updated classification

BACKGROUND

Class I major histocompatibility complex (MHC) molecules bind, and present to T cells, short peptides derived from intracellular processing of proteins. The peptide repertoire of a specific molecule is to a large extent determined by the molecular structure accommodating so-called main anchor positions of the presented peptide. These receptors are extremely polymorphic, and much of the polymorphism influences the peptide-binding repertoire. However, despite this polymorphism, class I molecules can be clustered into sets of molecules that bind largely overlapping peptide repertoires. Almost a decade ago we introduced this concept of clustering human leukocyte antigen (HLA) alleles and defined nine different groups, denominated as supertypes, on the basis of their main anchor specificity. The utility of this original supertype classification, as well several other subsequent arrangements derived by others, has been demonstrated in a large number of epitope identification studies.

RESULTS

Following our original approach, in the present report we provide an updated classification of HLA-A and -B class I alleles into supertypes. The present analysis incorporates the large amount of class I MHC binding data and sequence information that has become available in the last decade. As a result, over 80% of the 945 different HLA-A and -B alleles examined to date can be assigned to one of the original nine supertypes. A few alleles are expected to be associated with repertoires that overlap multiple supertypes. Interestingly, the current analysis did not identify any additional supertype specificities.

CONCLUSION

As a result of this updated analysis, HLA supertype associations have been defined for over 750 different HLA-A and -B alleles. This information is expected to facilitate epitope identification and vaccine design studies, as well as investigations into disease association and correlates of immunity. In addition, the approach utilized has been made more transparent, allowing others to utilize the classification approach going forward.

Comparative modeling of marsupial MHC class I molecules identifies structural polymorphisms affecting functional motifs

Major histocompatibility complex (MHC) class I molecules are transmembrane glycoproteins that present antigenic peptides to CD8+ T cells and are subsequently important for the initiation of an immune response. In this study novel MHC class I sequences from the tammar wallaby (Macropus eugenii) have been characterized. Analysis and comparative modeling of these and existing marsupial molecules reveals potential functional polymorphisms within peptide-binding grooves, MHC assembly motifs and the T cell receptor recognition interface. In addition, we show that a previously identified marsupial-specific insertion is within a region, which is known as a putative NK cell receptor (Ly49A) binding site in the mouse, suggesting that this site may be functionally active in marsupials. Further, the analysis highlighted differences in structural and sequence based grouping of marsupial MHC class I molecules.

Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Background

A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the identification of appropriate peptides to effect this process. There are several algorithms currently in use for making such predictions, but these are limited to a small number of MHC molecules and have good but imperfect prediction power.

Results

We have undertaken an exploration of the power gained by taking advantage of a natural representation of the amino acids in terms of their biophysical properties. We used several well-known statistical classifiers using either a naive encoding of amino acids by name or an encoding by biophysical properties. In all cases, the encoding by biophysical properties leads to substantially lower misclassification error.

Conclusion

Representation of amino acids using a few important bio-physio-chemical property provide a natural basis for representing peptides and greatly improves peptide-MHC class I binding prediction.

Comparative molecular dynamics analysis of tapasin-dependent and -independent MHC class I alleles

MHC class I molecules load antigenic peptides in the endoplasmic reticulum and present them at the cell surface. Efficiency of peptide loading depends on the class I allele and can involve interaction with tapasin and other proteins of the loading complex. Allele HLA-B*4402 (Asp at position 116) depends on tapasin for efficient peptide loading, whereas HLA-B*4405 (identical to B*4402 except for Tyr116) can efficiently load peptides in the absence of tapasin. Both alleles adopt very similar structures in the presence of the same peptide. Comparative unrestrained molecular dynamics simulations on the alpha(1)/alpha(2) peptide binding domains performed in the presence of bound peptides resulted in structures in close agreement with experiments for both alleles. In the absence of peptides, allele-specific conformational changes occurred in the first segment of the alpha(2)-helix that flanks the peptide C-terminal binding region (F-pocket) and contacts residue 116. This segment is also close to the proposed tapasin contact region. For B*4402, a shift toward an altered F-pocket structure deviating significantly from the bound form was observed. Subsequent free energy simulations on induced F-pocket opening in B*4402 confirmed a conformation that deviated significantly from the bound structure. For B*4405, a free energy minimum close to the bound structure was found. The simulations suggest that B*4405 has a greater tendency to adopt a peptide receptive conformation in the absence of peptide, allowing tapasin-independent peptide loading. A possible role of tapasin could be the stabilization of a peptide-receptive class I conformation for HLA-B*4402 and other tapasin-dependent alleles.

First glimpse of the peptide presentation by rhesus macaque MHC class I: crystal structures of Mamu-A*01 complexed with two immunogenic SIV epitopes and insights into CTL escape

The infection of rhesus macaques (Macaca mulatta) by the SIV is the best animal model for studying HIV infection and for AIDS vaccine development. A prevalent MHC class I allele, Mamu-A*01, is known to correlate with containment of SIV, which has been extensively explored in studies of CTL-based vaccination concepts. We determined the crystal structures of Mamu-A*01 complexed with two immunodominant SIV epitopes: the nonamer CM9 of group-specific Ag (Gag, 181-189; CTPYDINQM) and the octamer TL8 of transcription activator (Tat, 28-35; TTPESANL). The overall structures of the two Mamu-A*01 complexes are similar to other MHC class I molecules. Both structures confirm the presence of an absolutely conserved proline anchor residue in the P3 position of the Ag, bound to a D pocket of the Mamu-A*01 H chain with optimal surface complementarity. Like other MHC/peptide complex structures, the P2 and C-terminal residues of the epitopes are also important for anchoring to the MHC molecule, whereas the middle residues form an arch and their side chains are directed into solvent. These two structures reveal details of how Mamu-A*01 interacts with two well-studied epitopes at the atomic level. We discuss the structural basis of CTL escape, based on molecular models made possible by these two structures. The results we present in this study are most relevant for the rational design of Mamu-A*01-restricted CTL epitopes with improved binding, as a step toward development of AIDS vaccines.

Grouping of class I HLA alleles using electrostatic distribution maps of the peptide binding grooves

Human leukocyte antigen (HLA) molecules involved in immune function by binding to short peptides (8-20 residues) have different sequences in different individuals belonging to distinct ethnic population. Hence, the peptide-binding function of HLA alleles is specific. Class I HLA alleles (alternative forms of a gene) are associated with CD8+ T cells, and their allele-specific sequence information is available at the IMGT/HLA database. The available sequences are one-dimensional (ID), and the peptide-binding functional inference often requires 3-dimensional (3D) structural models of respective alleles. Hence, 3D structures were constructed for 1,000 class I HLA alleles (310 A, 570 B, and 120 C) using MODELLER (a comparative protein modeling program for modeling protein structures). The electrostatic distribution maps were generated for each modeled structure using Deep View (Swiss PDB Viewer Version 3.7). The 1,000 models were then grouped into different categories by visual inspection of their electrostatic distribution maps in the peptide binding grooves. The distribution of the models based on electrostatic distribution was 30% negative (300), 1% positive (12), 8% neutral (84), and 60% (604) mixed (random mixture of negative, positive, and neutral). This grouping provides insight toward the inference for functional overlap among HLA alleles.

In silico grouping of peptide/HLA class I complexes using structural interaction characteristics

MOTIVATION

Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored.

METHODS

We describe the use of structural interaction descriptors for the analysis of 68 peptide/HLA class I crystallographic structures. Interaction parameters computed include the number of intermolecular hydrogen bonds between each HLA protein and its corresponding bound peptide, solvent accessibility, gap volume and gap index.

RESULTS

The structural interactions patterns of peptide/HLA class I complexes investigated herein vary among individual alleles and may be grouped in a supertype dependent manner. Using the proposed methodology, eight HLA class I supertypes were defined based on existing experimental crystallographic structures which largely overlaps (77% consensus) with the definitions by binding motifs. This mode of classification, which considers conformational information of both peptide and HLA proteins, provides an alternative to the characterization of supertypes using either peptide or HLA protein information alone.

MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties

Major histocompatibility complex (MHC)-binding peptides are essential for antigen recognition by T-cell receptors and are being explored for vaccine design. Computational methods have been developed for predicting MHC-binding peptides of fixed lengths, based on the training of relatively few non-binders. It is desirable to introduce methods applicable for peptides of flexible lengths and trained by using more diverse sets of non-binders. MHC-BPS is a web-based MHC-binder prediction server that uses support vector machines for predicting peptide binders of flexible lengths for 18 MHC class I and 12 class II alleles from sequence-derived physicochemical properties, which were trained by using 4,208 approximately 3,252 binders and 234,333 approximately 168,793 non-binders, and evaluated by an independent set of 545 approximately 476 binders and 110,564 approximately 84,430 non-binders. The binder prediction accuracies are 86 approximately 99% for 25 and 70 approximately 80% for five alleles, and the non-binder accuracies are 96 approximately 99% for 30 alleles. A screening of HIV-1 genome identifies 0.01 approximately 5% and 5 approximately 8% of the constituent peptides as binders for 24 and 6 alleles, respectively, including 75 approximately 100% of the known epitopes. This method correctly predicts 73.3% of the 15 newly published epitopes in the last 4 months of 2005. MHC-BPS is available at http://bidd.cz3.nus.edu.sg/mhc/ .

Polymorphism, natural selection, and structural modeling of class Ia MHC in the African clawed frog (Xenopus laevis)

In the African clawed frog (Xenopus laevis), two deeply divergent allelic lineages of multiple genes of the class I MHC region have been discovered. For the MHC class I UAA locus, functional differences and the molecular basis for lineages maintenance are unknown. Alleles of linked class I region genes also exhibit strong disequilibrium with specific MHC alleles, but the underlying cause is not clear. We use MHC class Ia sequence data to estimate substitution rates and investigate structural differences between allelic lineages from protein models. Results indicate the operation of natural selection, and differences in the steric properties in the F pocket of the peptide-binding region among lineages. Variability in this pocket likely enables allelic lineages to bind very different sets of peptides and to interact differently with MHC chaperones in the endoplasmic reticulum. These results constitute evidence of the molecular evolutionary basis for 1) the maintenance of allelic lineages, 2) functional differences among lineages, and 3) strong linkage disequilibrium of allelic variants of class I region genes in X. laevis.