VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

Discriminating antigen and non-antigen using proteome dissimilarity:bacterial antigens

It has been postulated that immunogenicity results from the overall dissimilarity of pathogenic proteins versus the host proteome. We have sought to use this concept to discriminate between antigens and non-antigens of bacterial origin. Sets of 100 known antigenic and nonantigenic peptide sequences from bacteria were compared to human and mouse proteomes. Both antigenic and non-antigenic sequences lacked human or mouse homologues. Observed distributions were compared using the non-parametric Mann-Whitney test. The statistical null hypothesis was accepted, indicating that antigen and non-antigens did not differ significantly. Likewise, we were unable to determine a threshold able to separate meaningfully antigen from non-antigen. Thus, antigens cannot be predicted from pathogen genomes based solely on their dissimilarity to the human genome.

Immunological protein-protein interactions:a critical reflection

Life, and the biochemistry of which it is ultimately comprised, is built from the interactions of proteins, and the study of protein-protein interactions is fast becoming a central feature of molecular bioscience. This is as true of immunobiology as it is of other areas of the wider biological milieu. Protein-protein interactions within an immunological setting comprise both the kind familiar from other areas of biology and instantiations of protein-protein interactions special to the immune arena. Of the generic kind of protein-protein interaction, co-stimulatory receptors, such as CD28, and the interaction of accessory proteins, such as CD4 or CD8, are amongst the most prevalent and apposite of examples. The key examples of special immunological instantiations of protein-protein interactions are the binding of antigens by antibodies and the formation of peptide-MHC-TCR complexes; both prime examples of vital molecular recognition events mediated by protein-protein interactions. In this brief review, and within the context of this burgeoning field, we examine immunological protein-protein interactions, focussing on the problematic nature of defining such interactions. © 2011 by Nova Science Publishers, Inc. All rights reserved.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

A multigene family encoding R-SNAREs in the ciliate Paramecium tetraurelia

SNARE proteins (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) mediate membrane interactions and are conventionally divided into Q-SNAREs and R-SNAREs according to the possession of a glutamine or arginine residue at the core of their SNARE domain. Here, we describe a set of R-SNAREs from the ciliate Paramecium tetraurelia consisting of seven families encoded by 12 genes that are expressed simultaneously. The complexity of the endomembrane system in Paramecium can explain this high number of genes. All P. tetraurelia synaptobrevins (PtSybs) possess a SNARE domain and show homology to the Longin family of R-SNAREs such as Ykt6, Sec22 and tetanus toxin-insensitive VAMP (TI-VAMP). We localized four exemplary PtSyb subfamilies with GFP constructs and antibodies on the light and electron microscopic level. PtSyb1-1, PtSyb1-2 and PtSyb3-1 were found in the endoplasmic reticulum, whereas PtSyb2 is localized exclusively in the contractile vacuole complex. PtSyb6 was found cytosolic but also resides in regularly arranged structures at the cell cortex (parasomal sacs), the cytoproct and oral apparatus, probably representing endocytotic compartments. With gene silencing, we showed that the R-SNARE of the contractile vacuole complex, PtSyb2, functions to maintain structural integrity as well as functionality of the osmoregulatory system but also affects cell division.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.

Designing immunogenic peptides

Peptides fulfill many roles in immunology, yet none are more important than their role as immunogenic epitopes driving the adaptive immune response, our ultimate bulwark against infectious disease. Peptide epitopes are mediated primarily by their interaction with major histocompatibility complexes (T-cell epitopes) and antibodies (B-cell epitopes). As pathogen genomes continue to be revealed, both experimental and computational epitope mapping are becoming crucial tools in vaccine discovery1,2. Immunoinformatics offers many tools, techniques and approaches for in silico epitope characterization, which is capable of greatly accelerating epitope design. © 2013 Nature America, Inc. All rights reserved.

Influence of iron deprivation and sub-inhibitory concentrations of antifungal antibiotics on surface antigens of candida albicans yeast cells

This study examined the effect of iron deprivation and sub-inhibitory concentrations of antifungal agents on yeast cell surface antigen recognition by antibodies from patients with Candida infections. Separation of cell wall surface proteins by sodium dodecyl-polyacrylamide gel electrophoresis (SDS-PAGE) and immunological detection by immunoblotting, revealed that antigenic profiles of yeasts were profoundly influenced by the growth environment. Cells grown under iron-depleted conditions expressed several iron-regulated proteins that were recognized by antibodies from patient sera. An attempt to characterize these proteins by lectin blotting with concanavalin A revealed that some could be glycoprotein in nature. Furthermore, these proteins which were located within cell walls and on yeast surfaces, were barely or not expressed in yeasts cultivated under iron-sufficient conditions. The magnitude and heterogeneity of human antibody responses to these iron-regulated proteins were dependent on the type of Candida infection, serum antibody class and yeast strain. Hydroxamate-type siderophores were also detected in supernatants of iron depleted yeast cultures. This evidence suggests that Candida albicans expresses iron-regulated proteins/glycoproteins in vitro which may play a role in siderophore-mediated iron uptake in Candida albicans. Sequential monitoring of IgG antibodies directed against yeast surface antigens during immunization of rabbits revealed that different antigens were recognized particularly during early and later stages of immunization in iron-depleted cells compared to iron-sufficient cells. In vitro and in vivo adherence studies demonstrated that growth phase, yeast strain and growth conditions affect adhesion mechanisms. In particular, growth under iron-depletion in the presence of sub-inhibitory concentrations of polyene and azole antifungals enhanced the hydrophobicity of C.albicans. Growth conditions also influenced MICs of antifungals, notably that of ketoconazole. Sub-inhibitory concentrations of amphotericin B and fluconazole had little effect on surface antigens, whereas nystatin induced profound changes in surface antigens of yeast cells. The effects of such drug concentrations on yeast cells coupled with host defence mechanisms may have a significant affect on the course of Candida infections.