Liposomal vaccine delivery systems

INTRODUCTION: Liposomes remain at the forefront of drug and vaccine design owing to their well-documented abilities to act as delivery vehicles. Nevertheless, the concept of liposomes as delivery vehicles is not a new one, with most works focusing on their use for the delivery of genes and drugs. However, in the last 10 years a significant amount of research has focused on using liposomes as vaccine adjuvants, not only as an antigen delivery vehicle but also as a tool to increase the immunogenicity of peptide and protein antigens. AREAS COVERED: This paper reviews liposomal adjuvants now in vaccine development, with particular emphasis on their adjuvant mechanism and how specific physicochemical characteristics of liposomes affect the immune response. The inclusion of immunomodulators is also discussed, with prominence given to Toll-like receptor ligands. EXPERT OPINION: The use of liposomes as vaccine delivery systems is evolving rapidly owing to the combined increase in technological advances and understanding of the immune system. Liposomes that contain and deliver immunostimulators and antigens are now being developed to target diseases that require stimulation of both humoral and cell-mediated immune responses. The CAF liposomal system, described in detail in this review, is one liposomal model that shows such flexibility.

Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

Proteins accessible to immune surveillance show significant T-cell epitope depletion:implications for vaccine design

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.

Vaccine entrapment in liposomes

The use of liposomes as carriers of peptide, protein, and DNA vaccines requires simple, easy-to-scale-up technology capable of high-yield vaccine entrapment. Work from this laboratory has led to the development of techniques that can generate liposomes of various sizes, containing soluble antigens such as proteins and particulate antigens (e.g., killed or attenuated bacteria or viruses), as well as antigen-encoding DNA vaccines. Entrapment of vaccines is carried out by the dehydration-rehydration procedure which entails freeze-drying of a mixture of "empty" small unilamellar vesicles and free vaccines. On rehydration, the large multilamellar vesicles formed incorporate up to 90% or more of the vaccine used. When such liposomes are microfluidized in the presence of nonentrapped material, their size is reduced to about 100 nm in diameter, with much of the originally entrapped vaccine still associated with the vesicles. A similar technique applied for the entrapment of particulate antigens (e.g., Bacillus subtilis spores) consists of freeze-drying giant vesicles (4-5 microm in diameter) in the presence of spores. On rehydration and sucrose gradient fractionation of the suspension, up to 30% or more of the spores used are associated with generated giant liposomes of similar mean size.

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.

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.

A comparative molecular similarity indices (CoMSIA) study of peptide binding to the HLA-A3 superfamily

Epitope identification is the basis of modern vaccine design. The present paper studied the supermotif of the HLA-A3 superfamily, using comparative molecular similarity indices analysis (CoMSIA). Four alleles with high phenotype frequencies were used: A*1101, A*0301, A*3101 and A*6801. Five physicochemical properties—steric bulk, electrostatic potential, local hydro-phobicity, hydrogen-bond donor and acceptor abilities—were considered and ‘all fields’ models were produced for each of the alleles. The models have a moderate level of predictivity and there is a good correlation between the data. A revised HLA-A3 supermotif was defined based on the comparison of favoured and disfavoured properties for each position of the MHC bound peptide. The present study demonstrated that CoMSIA is an effective tool for studying peptide–MHC interactions.