NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0.     

Peptide binding motifs and specificities for HLA-DQ molecules

 HLA-DQ molecules have been associated with susceptibility to a number of autoimmune and other diseases, possibly through the peptide repertoire that can be presented by different allelic products. It is thus of importance to understand which peptides can be bound by different HLA-DQ allelic products. Recently, a model for HLA-DQ has been described and used to derive peptide positional environments for HLA-DQ allelic products. By combining the peptide positional environments with known HLA-DQ peptide binding motifs, a set of predictions of likely anchor motifs for many of the products of HLA-DQ allelic variants are made and presented in a table referred to as a roadmap for HLA-DQ peptide binding specificities. Received: 3 March 1999 / Revised: 20 May 1999     

Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization

Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.     

Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.     

A computational pipeline to generate MHC binding motifs

BACKGROUND: Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.     

Modeling alternative binding registers of a minimal immunogenic peptide on two class II major histocompatibility complex (MHC II) molecules predicts polarized T-cell receptor (TCR) contact positions.

Several major histocompatibility complex class II (MHC II) complexes with known minimal immunogenic peptides have now been solved by X-ray crystallography. Specificity pockets within the MHC II binding groove provide distinct peptide contacts that influence peptide conformation and define the binding register within different allelic MHC II molecules. Altering peptide ligands with respect to the residues that contact the T-cell receptor (TCR) can drastically change the nature of the ensuing immune response. Here, we provide an example of how MHC II (I-A) molecules may indirectly effect TCR contacts with a peptide and drive functionally distinct immune responses. We modeled the same immunogenic 12-amino acid peptide into the binding grooves of two allelic MHC II molecules linked to distinct cytokine responses against the peptide. Surprisingly, the favored conformation of the peptide in each molecule was distinct with respect to the exposure of the N- or C-terminus of the peptide above the MHC II binding groove. T-cell clones derived from each allelic MHC II genotype were found to be allele-restricted with respect to the recognition of these N- vs. C-terminal residues on the bound peptide. Taken together, these data suggest that MHC II alleles may influence T-cell functions by restricting TCR access to specific residues of the I-A-bound peptide. Thus, these data are of significance to diseases that display genetic linkage to specific MHC II alleles, e.g. type 1 diabetes and rheumatoid arthritis.     

Crystal structure of a non-canonical low-affinity peptide complexed with MHC class I: a new approach for vaccine design

Peptides bind with high affinity to MHC class I molecules by anchoring certain side-chains (anchors) into specificity pockets in the MHC peptide-binding groove. Peptides that do not contain these canonical anchor residues normally have low affinity, resulting in impaired pMHC stability and loss of immunogenicity. Here, we report the crystal structure at 1.6 A resolution of an immunogenic, low-affinity peptide from the tumor-associated antigen MUC1, bound to H-2Kb. Stable binding is still achieved despite small, non-canonical residues in the C and F anchor pockets. This structure reveals how low-affinity peptides can be utilized in the design of novel peptide-based tumor vaccines. The molecular interactions elucidated in this non-canonical low-affinity peptide MHC complex should help uncover additional immunogenic peptides from primary protein sequences and aid in the design of alternative approaches for T-cell vaccines.     

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

In all vertebrate animals, CD8⁺ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.     

Molecular dynamics study of the human insulin B peptide SHLVEALYLVCGERGG complexed with HLA-DQ8 reveals important hydrogen bond interactions

The basis of proper recognition of pathogens and tumours is provided by adaptive immunity. This immunological reaction of the recognition function of T-cell receptors on T lymphocytes detects antigenic peptides bound to major histocompatibility complex (MHC) molecules. Structural insight into this process has few grown considerably in the last years. In some of the cases, antigens are self-protein fragments causing autoimmunity diseases. Type 1 diabetes is such a disease connected with the human leukocyte antigen-DQ8 molecule, a class II MHC glycoprotein. Its crystal structure, complexed with LVEALYLVCGERGG peptide (insulin B peptide), has been solved, and important information about the significance of P1, P4 and P9 binding pockets has been discovered. The complex structure also revealed an unusual large number of intermolecular hydrogen bonds between insulin B peptide and MHC molecule. To further investigate the dynamics of peptide/MHC interactions, we perform molecular dynamic simulations in explicit water. Analysis of the results provided useful information of the binding of the peptide antigen to MHC molecule, which is supported by numerous hydrogen bonds besides the electrostatic (P1 and P9 pockets) or hydrophobic interactions (P4). Results also allowed some implications to be drawn for the role of residues located outside of the binding groove.     

Dynamical characterization of two differentially disease associated MHC class I proteins in complex with viral and self-peptides

Major histocompatibility complex (MHC) class I proteins are expressed on the cell surface where they present foreign and self-peptides to effector cells of the immune system. While an understanding of the structural prerequisites for antigen presentation has already been achieved, insight into subtype- or peptide-dependent dynamical characteristics of a peptide–MHC antigen is so far largely obscure. We approached this problem by employing 400-ns molecular dynamics simulations with two human MHC class I subtypes as model systems: the ankylosing spondylitis-associated HLA-B127:05 and the non-ankylosing spondylitis-associated HLA-B127:09. Both proteins differ only by a micropolymorphism at the floor of the peptide binding groove (Asp116His). A viral (pLMP2) and three self-peptides (pVIPR, pGR, and TIS) were evaluated. The stability of the binding grooves was found to be both subtype dependent and peptide dependent. A detachment from the C- and/or N-terminal pockets was observed for all peptides except TIS, resulting in a stabilization of the α1-helix in both TIS-displaying subtypes. Estimates of the entropy associated with the bound peptides showed an increased entropy for pLMP2 presented by B127:05 as compared to B127:09, in contrast to the self-peptides. Additionally, the flexibility of the α1-helix that is probably important for receptor binding to the B27:peptide epitope is significantly enhanced for B127:05. These in silico results show that the dynamic properties of peptide–MHC complexes are affected both by the bound peptide and by micropolymorphisms of the heavy chain. Our findings suggest a role for the conformational flexibility of MHC class I molecules in the context of recognition by receptors on effector cells.     

Nonequivalence of Classical MHC Class I Loci in Ability to Direct Effective Antiviral Immunity

Structural diversity in the peptide binding sites of the redundant classical MHC antigen presenting molecules is strongly selected in humans and mice. Although the encoded antigen presenting molecules overlap in antigen presenting function, differences in polymorphism at the MHC I A, B and C loci in humans and higher primates indicate these loci are not functionally equivalent. The structural basis of these differences is not known. We hypothesize that classical class I loci differ in their ability to direct effective immunity against intracellular pathogens. Using a picornavirus infection model and chimeric H-2 transgenes, we examined locus specific functional determinants distinguishing the ability of class I sister genes to direct effective anti viral immunity. Whereas, parental FVB and transgenic FVB mice expressing the H-2K^b gene are highly susceptible to persisting Theiler''s virus infection within the CNS and subsequent demyelination, mice expressing the D^b transgene clear the virus and are protected from demyelination. Remarkably, animals expressing a chimeric transgene, comprised primarily of K^b but encoding the peptide binding domain of D^b, develop a robust anti viral CTL response yet fail to clear virus and develop significant demyelination. Differences in expression of the chimeric K^bα1α2D^b gene (low) and D^b (high) in the CNS of infected mice mirror expression levels of their endogenous H-2^q counterparts in FVB mice. These findings demonstrate that locus specific elements other than those specifying peptide binding and T cell receptor interaction can determine ability to clear virus infection. This finding provides a basis for understanding locus-specific differences in MHC polymorphism, characterized best in human populations.     

Peptide motif for the rat MHC class II molecule RT1.Da: similarities to the multiple sclerosis-associated HLA-DRB1*1501 molecule

Experimental autoimmune encephalomyelitis induced with myelin proteins in DA and LEW.1AV1 rats is a model of multiple sclerosis (MS). It reproduces major aspects of this detrimental disease of the central nervous system. MS is associated with the HLA-DRB1*1501, DRB5*0101, and DQB1*0602 haplotype. DA and LEW.1AV1 rats share the RT1^av1 haplotype. So far, no MHC class II peptide motif of RT1.D^a molecules has been described. Sequence alignment of the chain of the rat MHC class II molecule RT1.D^a with human HLA class II molecules revealed strong similarity in the peptide-binding groove of RT1.D^a and HLA-DRB1*1501. According to the putative peptide-binding pockets of RT1.D^a, after comparison with the pockets of HLA-DRB1*1501, we predicted the peptide motif of RT1.D^a. To verify the predicted motif, naturally processed peptides were eluted by acidic treatment from immunoaffinity-purified RT1.D^a molecules of lymphoid tissue of DA rats and subsequently analyzed by ESI tandem mass spectrometry. In addition, we performed binding studies with combinatorial nonapeptide libraries to purified RT1.D^a molecules. Based on these studies we could define a peptide-binding motif for RT1.D^a characterized by aliphatic amino acid residues (L, I, V, M) and of F for the peptide pocket P1, aromatic residues (F, Y, W) for P4, basic residues (K, R) for P6, aliphatic residues (I, L, V) for P7, and aromatic residues (F, Y, W) and L for P9. Both methods revealed similar binding characteristics for peptides to RT1.D^a. This data will allow epitope predictions for analysis of peptides, relevant for experimental autoimmune diseases.     

Flanking p10 contribution and sequence bias in matrix based epitope prediction: revisiting the assumption of independent binding pockets

Background  

Eluted natural peptides from major histocompatibility molecules show patterns of conserved residues. Crystallographic structures show that the bound peptide in class II major histocompatibility complex adopts a near uniform polyproline II-like conformation. This way allele-specific favoured residues are able to anchor into pockets in the binding groove leaving other peptide side chains exposed for recognition by T cells. The anchor residues form a motif. This sequence pattern can be used to screen large sequences for potential epitopes. Quantitative matrices extend the motif idea to include the contribution of non-anchor peptide residues. This report examines two new matrices that extend the binding register to incorporate the polymorphic p10 pocket of human leukocyte antigen DR1. Their performance is quantified against experimental binding measurements and against the canonical nine-residue register matrix.     

Sequence and diversity of DRB genes of Aotus nancymaae, a primate model for human malaria parasites

 The New World primate Aotus nancymaae is susceptible to infection with the human malaria parasite Plasmodium falciparum and Plasmodium vivax and has therefore been recommended by the World Health Organization as a model for evaluation of malaria vaccine candidates. We present here a first step in the molecular characterization of the major histocompatibility complex (MHC) class II DRB genes of Aotus nancymaae (owl monkey or night monkey) by nucleotide sequence analysis of the polymorphic exon 2 segments. In a group of 15 nonrelated animals captivated in the wild, 34 MHC DRB alleles could be identified. Six allelic lineages were detected, two of them having human counterparts, while two other lineages have not been described in any other New World monkey species studied. As in the common marmoset, the diversity of DRB alleles appears to have arisen largely by point mutations in the β-pleated sheets and by frequent exchange of fixed sequence motifs in the α-helical portion. Pairs of alleles differing only at amino acid position b86 by an exchange of valine to glycine are present in Aotus, as in humans. Essential amino acid residues contributing to MHC DR peptide binding pockets number 1 and 4 are conserved or semiconserved between HLA-DR and Aona-DRB molecules, indicating a capacity to bind similar peptide repertoires. These results support fully our using Aotus monkeys as an animal model for evaluation of future subunit vaccine candidates. Received: 10 August 1999 / Revised: 11 October 1999     

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at .     

Structural and immunological reactivity of the principal neutralizing determinant V3 of glycoprotein gp 120 of HIV-1

The variable domain V3 in the outer glycoprotein gp 120 of HIV-1 is a highly important region with respect to immune response during the course of viral infection. Neutralizing antibodies are produced against this domain; in addition, it has been shown to be a functionally active epitope for T helper and cytotoxic T cells. The high degree of amino acid variability in individual HIV-isolates, however, limits the use of the V3-domain in approaches to vaccine development. In order to characterize the residues important for antibody interaction and binding to MHC class I proteins, we constructed a consensus sequence of the V3-domain with broad reactivity [1] and used synthetic peptides derived from this consensus with individual residues altered to alanine. These peptides were used as antigens in ELISA tests to define the amino acids which are important for binding to human and rabbit/anti-peptide immunoglobulins. In addition, we used these alanine-derived peptides in interaction studies with human HLA-A2.1 and mouse H-2D^d by testing their capacity to stabilize the respective MHC class I protein complexes on the surface of mutant cell lines T2 and RMA-S transfected with D^d gene. The experimental tests allowed us to define individual residues involved in antibody and MHC-protein interaction, respectively. In a further approach, we used those results to design interaction models with HLA-A2.1 and H-2D^d. Therefore, a structural model for H-2D^d was built that exhibits an overall similar conformation to the parental crystal structure of HLA-A2.1. The resulting interaction models show V3-peptide bound in an extended β-conformation with a bulge in its centre for both H-2D^d and HLA-A2.1 complexes. The N- and C-termini of V3 peptide reside in conserved pockets within both MHC-proteins. Anchoring residues could be determined that are crucial for the binding of the respective MHC class I haplotype. The cross-reactivity of V3-peptide in enhancing the expression of two different MHC class I molecules (H-2D^d and HLA-A2.1) is shown to be based on similar peptide binding that induces an almost identical peptide conformation.     

Identification of peptide epitopes of MAGE-1, -2, -3 that demonstrate HLA-A3-specific binding

 The MAGE gene family of tumour antigens are expressed in a wide variety of human cancers. We have identified 43 nonamer peptide sequences, from MAGE-1, -2 and -3 proteins that contain binding motifs for HLA-A3 MHC class I molecules. The T2 cell line, transfected with the cDNA for the HLA-A3 gene, was used in a MHC class I stabilisation assay performed at 37°C and 26°C. At 37°C, 2 peptides were identified that stabilised HLA-A3 with high affinity (fluorescence ratio, FR >1.5), 4 peptides with low affinity (FR 1.11 – 1.49) and 31 peptides that did not stabilise this HLA haplotype (FR <1.1). At 26°C, 12 peptides were identified that stabilised HLA-A3 with high affinity, 8 peptides with low affinity and 17 peptides that did not stabilise this HLA haplotype. Two peptides stabilised HLA-A3 at both temperatures. Small changes in one to three amino acids at positions distinct from the anchor residues altered peptide affinity. Data were compared to a similar study in which a peptide competition assay was used to investigate MAGE-1 peptide binding to several HLA haplotypes. This study demonstrates that anchor residues do not accurately predict peptide binding to specific HLA haplotypes, changes in one to three amino acids at positions distinct from anchor residues influence peptide binding and alternative methods of determining peptide binding yield different results. We are currently investigating the ability of these peptides to induce antitumour cytotoxic T lymphocyte activity as they may be of potential therapeutic value. Received: 4 January 1996 / Accepted: 20 March 1996     

C-terminal anchoring of a peptide to class II MHC via the P10 residue is compatible with a peptide bulge.

The binding of antigenic peptide to class II MHC is mediated by hydrogen bonds between the MHC and the peptide, by salt bridges, and by hydrophobic interactions. The latter are confined to a number of deeper pockets within the peptide binding groove, and peptide side chains that interact with these pockets are referred to as anchor residues. T cell recognition involves solvent-accessible peptide residues along with minor changes in MHC helical pitch induced by the anchor residues. In class I MHC there is an added level of epitope complexity that results from binding of longer peptides that bulge out into the solvent-accessible, T cell contact area. Unlike class I MHC, class II MHC does not bind peptides of discrete length, and the possibility of peptide bulging has not been clearly addressed. A peptide derived from position 24-37 of integrin beta(3) can either bind or not bind to the class II MHC molecule HLA DRB3*0101 based on a polymorphism at the P9 anchor. We show that the loss of binding can be compensated by changes at the P10 position. We propose that this could be an example of a class II peptide bulge. Although not as efficient as P9 anchoring, the use of P10 as an anchor adds another possible mechanism by which T cell epitopes can be generated in the class II presentation system.     

Prediction of MHC binding peptide using Gibbs motif sampler,weight matrix and artificial neural network

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40¢), however above the random prediction (14¢). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.     

The Cell Biology of Antigen Processing

Abstract

T lymphocytes recognize antigen only after a series of intracellular events known as antigen processing. The result of antigen processing is the production of short segments of the primary peptide sequence bound to a polypeptide-binding groove on major histo compatibility complex (MHC) molecules. Antigen originates from one of two sites: intracellular or extra cellular. There are two corresponding pathways for antigen processing and two corresponding classes of MHC molecule. Analysis of each pathway has demonstrated that their separation is not purely anatomical, but is maintained by molecular interactions with other molecules. Antigen processing has been shown to regulate the overall immune response, but the mechanisms involved remain obscure.