Subunit vaccines distearoylphosphatidylcholine-based liposomes entrapping antigen offer a neutral alternative to dimethyldioctadecylammonium-based cationic liposomes as an adjuvant delivery system

The adjuvanticity of liposomes can be directed through formulation to develop a safe yet potent vaccine candidate. With the addition of the cationic lipid dimethyldioctadecylammonium bromide (DDA) to stable neutral distearoylphosphatidylcholine (DSPC):cholesterol (Chol) liposomes, vesicle size reduces while protein entrapment increases. The addition of the immunomodulator, trehalose 6,6-dibehenate (TDB) to either the neutral or cationic liposomes did not affect the physiochemical characteristics of these liposome vesicles. However, the protective immune response, as indicated by the amount of IFN-? production, increases considerably when TDB is present. High levels of IFN-? were observed for cationic liposomes; however, there was a marked reduction in IFN-? release over time. Conversely, for neutral liposomes containing TDB, although the initial amount of IFN-? was slightly lower than the cationic equivalent, the overall protective immune responses of these neutral liposomes were effectively maintained over time, generating good levels of protection. To that end, although the addition of DSPC and Chol reduced the protective immunity of DDA:TDB liposomes, relatively high protection was observed for the neutral counterpart, DSPC:Chol:TDB, which may offer an effective neutral alternative to the DDA:TDB cationic system, especially for the delivery of either zwitterionic (neutral) or cationic molecules or antigens.

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.