SARS coronavirus nucleocapsid immunodominant T-cell epitope cluster is common to both exogenous recombinant and endogenous DNA-encoded immunogens

Correspondence between the T-cell epitope responses of vaccine immunogens and those of pathogen antigens is critical to vaccine efficacy. In the present study, we analyzed the spectrum of immune responses of mice to three different forms of the SARS coronavirus nucleocapsid (N): (1) exogenous recombinant protein (N-GST) with Freund's adjuvant; (2) DNA encoding unmodified N as an endogenous cytoplasmic protein (pN); and (3) DNA encoding N as a LAMP-I chimera targeted to the lysosomal MHC II compartment (p-LAMP-N). Lysosomal trafficking of the LAMP/N chimera in transfected cells was documented by both confocal and immunoelectron microscopy. The responses of the immunized mice differed markedly. The strongest T-cell IFN-gamma and CTL responses were to the LAMP-N chimera followed by the pN immunogen. In contrast, N-GST elicited strong T cell IL-4 but minimal IFN-gamma responses and a much greater antibody response. Despite these differences, however, the immunodominant T-cell ELISpot responses to each of the three immunogens were elicited by the same N peptides, with the greatest responses being generated by a cluster of five overlapping peptides, N76-114, each of which contained nonameric H2(d) binding domains with high binding scores for both class I and, except for N76-93, class II alleles. These results demonstrate that processing and presentation of N, whether exogenously or endogenously derived, resulted in common immunodominant epitopes, supporting the usefulness of modified antigen delivery and trafficking forms and, in particular, LAMP chimeras as vaccine candidates. Nevertheless, the profiles of T-cell responses were distinctly different. The pronounced Th-2 and humoral response to N protein plus adjuvant are in contrast to the balanced IFN-gamma and IL-4 responses and strong memory CTL responses to the LAMP-N chimera. (C) 2005 Elsevier Inc. All rights reserved.

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

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.

A deimmunised form of the ribotoxin, α-sarcin, lacking CD4+ T cell epitopes and its use as an immunotoxin warhead

Fungal ribotoxins that block protein synthesis can be useful warheads in the context of a targeted immunotoxin. α-Sarcin is a small (17 kDa) fungal ribonuclease produced by Aspergillus giganteus that functions by catalytically cleaving a single phosphodiester bond in the sarcin–ricin loop of the large ribosomal subunit, thus making the ribosome unrecognisable to elongation factors and leading to inhibition of protein synthesis. Peptide mapping using an ex vivo human T cell assay determined that α-sarcin contained two T cell epitopes; one in the N-terminal 20 amino acids and the other in the C-terminal 20 amino acids. Various mutations were tested individually within each epitope and then in combination to isolate deimmunised α-sarcin variants that had the desired properties of silencing T cell epitopes and retention of the ability to inhibit protein synthesis (equivalent to wild-type, WT α-sarcin). A deimmunised variant (D9T/Q142T) demonstrated a complete lack of T cell activation in in vitro whole protein human T cell assays using peripheral blood mononuclear cells from donors with diverse HLA allotypes. Generation of an immunotoxin by fusion of the D9T/Q142T variant to a single-chain Fv targeting Her2 demonstrated potent cell killing equivalent to a fusion protein comprising the WT α-sarcin. These results represent the first fungal ribotoxin to be deimmunised with the potential to construct a new generation of deimmunised immunotoxin therapeutics.

A Study of pH Manipulation on Tumor Proliferation and the CTL Response

Adoptive Cell Transfer (ACT) Therapy is a cancer treatment that enhances and utilizes the body’s own immune system. However, this treatment has had limited success in clinical trials. We hypothesized that this is due to the immunosuppressive, acidic microenvironment of cancer tumors. We tested the effects of acidic, neutral, and basic environments in vitro on cytotoxic T lymphocyte (CTL) survival, activation, migration and killing ability and on cancer cell survival. We found that CTLs have most optimum survival, activation, and migration in a neutral environment, while the optimal extracellular conditions for EG-7 lymphoma are slightly acidic and B16-OVA melanoma survives best in physiological conditions. Future research should further study the killing ability of T cells in the three different environments and look to move to in vivo experiments.

Structural Basis for Clonal Diversity of the Public T Cell Response to Dominant Epitopes from Cytomegalovirus and Influenza

A diverse T cell receptor (TCR) repertoire is a prerequisite for effective viral clearance. However, knowledge of human TCR repertoire to defined viral antigens is limited. Recent advances in high-throughput sequencing (HTS) and single-cell sorting have revolutionized the study of human TCR repertoires to different types of viruses. In collaboration with the laboratory of Dr. Nan-ping Weng (National Institute on Aging, NIH), we applied unique molecular identifier (UMI)-labelled HTS, single-cell paired TCR analysis, surface plasmon resonance, and X-ray crystallography to exhaustively interrogate CD8+ TCR repertoires specific for cytomegalovirus (CMV) and influenza A (Flu) in HLA-A2+ humans. Our two CMV-specific TCR-pMHC structures and two Flu-specific TCR-pMHC structures provide a plausible explanation for the much higher diversity of CMV-specific than Flu-specific TCR repertoires in humans. Our comprehensive biochemical and structural portrait of two different anti-viral T cell responses may contribute to the future development of predictors of immunity or disease at the individual level.