Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Toward the atomistic simulation of T cell epitopes automated construction of MHC:peptide structures for free energy calculations

Epitopes mediated by T cells lie at the heart of the adaptive immune response and form the essential nucleus of anti-tumour peptide or epitope-based vaccines. Antigenic T cell epitopes are mediated by major histocompatibility complex (MHC) molecules, which present them to T cell receptors. Calculating the affinity between a given MHC molecule and an antigenic peptide using experimental approaches is both difficult and time consuming, thus various computational methods have been developed for this purpose. A server has been developed to allow a structural approach to the problem by generating specific MHC:peptide complex structures and providing configuration files to run molecular modelling simulations upon them. A system has been produced which allows the automated construction of MHC:peptide structure files and the corresponding configuration files required to execute a molecular dynamics simulation using NAMD. The system has been made available through a web-based front end and stand-alone scripts. Previous attempts at structural prediction of MHC:peptide affinity have been limited due to the paucity of structures and the computational expense in running large scale molecular dynamics simulations. The MHCsim server (http://igrid-ext.cryst.bbk.ac.uk/MHCsim) allows the user to rapidly generate any desired MHC:peptide complex and will facilitate molecular modelling simulation of MHC complexes on an unprecedented scale.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

Selection of conserved epitopes from hepatitis C virus for pan-populational stimulation of T-cell responses

The hepatitis C virus (HCV) is able to persist as a chronic infection, which can lead to cirrhosis and liver cancer. There is evidence that clearance of HCV is linked to strong responses by CD8 cytotoxic T lymphocytes (CTLs), suggesting that eliciting CTL responses against HCV through an epitope-based vaccine could prove an effective means of immunization. However, HCV genomic plasticity as well as the polymorphisms of HLA I molecules restricting CD8 T-cell responses challenges the selection of epitopes for a widely protective vaccine. Here, we devised an approach to overcome these limitations. From available databases, we first collected a set of 245 HCV-specific CD8 T-cell epitopes, all known to be targeted in the course of a natural infection in humans. After a sequence variability analysis, we next identified 17 highly invariant epitopes. Subsequently, we predicted the epitope HLA I binding profiles that determine their potential presentation and recognition. Finally, using the relevant HLA I-genetic frequencies, we identified various epitope subsets encompassing 6 conserved HCV-specific CTL epitopes each predicted to elicit an effective T-cell response in any individual regardless of their HLA I background. We implemented this epitope selection approach for free public use at the EPISOPT web server. © 2013 Magdalena Molero-Abraham et al.

Investigating the ability of antibodies to recognize specific oxidized protein epitopes

There is a growing awareness that inflammatory diseases have an oxidative pathology, which can result in specific oxidation of amino acids within proteins. Antibody-based techniques for detecting oxidative posttranslational modifications (oxPTMs) are often used to identify the level of protein oxidation. There are many commercially available antibodies but some uncertainty to the potential level of cross reactivity they exhibit; moreover little information regarding the specific target epitopes is available. The aim of this work was to investigate the potential of antibodies to distinguish between select peptides with and without oxPTMs. Two peptides, one containing chlorotyrosine (DY-Cl-EDQQKQLC) and the other an unmodified tyrosine (DYEDQQKQLC) were synthesized and complementary anti-sera were produced in sheep using standard procedures. The anti-sera were tested using a half-sandwich ELISA and the anti-serum raised against the chloro-tyrosine containing peptide showed increased binding to the chlorinated peptide, whereas the control anti-serum bound similarly to both peptides. This suggested that antibodies can discriminate between similar peptide sequences with and without an oxidative modification. A peptide (STSYGTGC) and its variants with chlorotyrosine or nitrotyrosine were produced. The anti-sera showed substantially less binding to these alternative peptides than to the original peptides the anti-sera were produced against. Work is ongoing to test commercially available antibodies against the synthetic peptides as a comparison to the anti-sera produced in sheep. In conclusion, the antisera were able to distinguish between oxidatively modified and unmodified peptides, and two different sequences around the modification site.

MHC class I bound to an immunodominant Theileria parva epitope demonstrates unconventional presentation to T cell receptors

T cell receptor (TCR) recognition of peptide-MHC class I (pMHC) complexes is a crucial event in the adaptive immune response to pathogens. Peptide epitopes often display a strong dominance hierarchy, resulting in focusing of the response on a limited number of the most dominant epitopes. Such T cell responses may be additionally restricted by particular MHC alleles in preference to others. We have studied this poorly understood phenomenon using Theileria parva, a protozoan parasite that causes an often fatal lymphoproliferative disease in cattle. Despite its antigenic complexity, CD8+ T cell responses induced by infection with the parasite show profound immunodominance, as exemplified by the Tp1(214-224) epitope presented by the common and functionally important MHC class I allele N*01301. We present a high-resolution crystal structure of this pMHC complex, demonstrating that the peptide is presented in a distinctive raised conformation. Functional studies using CD8+ T cell clones show that this impacts significantly on TCR recognition. The unconventional structure is generated by a hydrophobic ridge within the MHC peptide binding groove, found in a set of cattle MHC alleles. Extremely rare in all other species, this feature is seen in a small group of mouse MHC class I molecules. The data generated in this analysis contribute to our understanding of the structural basis for T cell-dependent immune responses, providing insight into what determines a highly immunogenic p-MHC complex, and hence can be of value in prediction of antigenic epitopes and vaccine design.

Articular cartilage glycosaminoglycans inhibit the adhesion of endothelial cells

Articular cartilage undergoes severe loss of proteoglycan and its constituent glycosaminoglycans (GAGs) in osteoarthritis. We hypothesize that the low GAG content of osteoarthritic cartilage renders the tissue susceptible to pathological vascularization. This was investigated using an in vitro angiogenesis model assessing endothelial cell adhesion to GAG-depleted cartilage explants. Bovine cartilage explants were treated with hyaluronidase to deplete GAG content and then seeded with fluorescently tagged human endothelial cells (HMEC-1). HMEC-1 adherence was assessed after 4 hr and 7 days. The effect of hyaluronidase treatment on GAG content, chondrocyte viability, and biochemical composition of the extracellular matrix was also determined. Hyaluronidase treatment reduced the GAG content of cartilage explants by 78 ± 3% compared with that of controls (p <0.0001). GAG depletion was associated with significantly more HMEC-1 adherence on both the surface (superficial zone) and the underside (deep zone) of the explants (both p <0.0001). The latter provided a more favorable environment for extended culture of HMEC-1 compared with the articulating surface. Hyaluronidase treatment altered the immunostaining for chondroitin sulfate epitopes, but not for lubricin. Our results support the hypothesis that articular cartilage GAGs are antiadhesive to endothelial cells and suggest that chondroitin sulfate and/or hyaluronan are responsible. The loss of these GAGs in osteoarthritis may allow osteochondral angiogenesis resulting in disease progression.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

MHC class II binding prediction:a little help from a friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Unexpected role of surface transglutaminase type II in celiac disease

Background & Aims: In celiac disease (CD), transglutaminase type II (TG2) has 2 fundamental roles: (1) as the autoantigen recognized by highly specific autoantibodies and (2) the modifier of pathogenic gliadin T-cell epitopes. It follows that inhibition of TG2 might represent an attractive strategy to curb the toxic action of gliadin. Here we studied the validity of this strategy using the organ culture approach. Methods: Duodenal biopsy specimens from 30 treated patients with CD, 33 untreated patients with CD, and 24 controls were cultured with or without gliadin peptides p31-43, pα-9, and deamidated pα-9 for 20 minutes, 3 hours, and 24 hours. In 31 patients with CD and 16 controls, TG2 inhibitor R283 or anti-TG CUB 7402 or anti-surface TG2 (6B9) mAbs were used in cultures. T84 cells were also cultured with or without peptides with or without TG inhibitors. Mucosal modifications after culture were assessed by immunofluorescence, in situ detection of TG activity, confocal microscopy, and fluorescence-activated cell sorter analysis. Results: The enzymatic inhibition of TG2 only controlled gliadin-specific T-cell activation. The binding of surface TG2 contained gliadin-specific T-cell activation and p31-43-induced actin rearrangement, epithelial phosphorylation, and apoptosis, both in organ cultures and T84 cells. Conclusions: These data indicate a novel and unexpected biological role for surface TG2 in the pathogenesis of CD suggesting a third role for TG2 in CD. These results have a specific impact for celiac disease, with wider implications indicating a novel biologic function of TG2 with possible repercussions in other diseases. © 2005 by the American Gastroenterological Association.

AntigenDB:an immunoinformatics database of pathogen antigens

The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates:E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 <50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.

Antibody detection of Burkholderia pseudommallei and Burkholderia mallei

Monoclonal and polyclonaI antibodies have been produced for use in immunological assays for the detection of Burkholderia pseudomallei and Burkholderia mallei. Monoclonal antibodies recognising a high molecular weight polysaccharide material found in some strains of both species have been shown to be effective in recognising B. pseudomallei and B. mallei and distinguishing them from other organisms. The high molecular weight polysaccharide material is thought to be the capsule of B. pseudomallei and B. mallei and may have important links with virulence. B. pseudomallei and B. mallei are known to be closely related, sharing many epitopes, but antigenic variation has been demonstrated within both the species. The lipopolysaccharide from strains of B. pseudomal/ei and B. mallei has been isolated and the silver stain profiles found to be visually very similar. A monoclonal antibody raised to B. mallei LPS has been found to recognise both B. mallei and B. pseudomallei strains. However, in a small number of B. pseudomallei strains a visually atypical LPS profile has been demonstrated. A monoclonal ant ibody rai sed against this atypical LPS showed no recognition of the typical LPS profile of either B. mallei or B. pseudomallei. This atypical LPS structure has not been reported and may be immunologically distinct from the typical LPS. Molecular biology and antibody engineering techniques have been used in an attempt to produce single-chain antibody fragments reactive to B. pseudomallei. Sequencing of one of the single-chain antibody fragments produced showed high homology with murine immunoglobulin genes, but none of the single-chain antibody fragments were found to be specific to B. pselldomallei.