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Vaccines based on structure-based design provide protection against infectious diseases.
ABSTRACT Vaccines elicit immune responses, provide protection against microorganisms and are considered as one of the most successful medical interventions against infectious diseases. Vaccines can be produced using attenuated virus or bacteria, recombinant proteins, bacterial polysaccharides, carbohydrates or plasmid DNA. Conventional vaccines rely on the induction of immune responses against antigenic proteins to be effective. The genetic diversity of microorganisms, coupled with the high degree of sequence variability in antigenic proteins, presents a challenge to developing broadly effective conventional vaccines. The observation that whole protein antigens are not necessarily essential for inducing immunity has led to the emergence of a new branch of vaccine design termed 'structural vaccinology'. Structure-based vaccines are designed on the rationale that protective epitopes should be sufficient to induce immune responses and provide protection against pathogens. Recent studies demonstrated that designing structure-based vaccine candidates with multiple epitopes induce a higher immune response. As yet there are no commercial vaccines available based on structure-based design and most of the structure-based vaccine candidates are in the preclinical stages of development. This review focuses on recent advances in structure-based vaccine candidates and their application in providing protection against infectious diseases.
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ABSTRACT: Vaccines are one of the most effective interventions to improve public health, however, the generation of highly effective vaccines for many diseases has remained difficult. Three chronic diseases that characterise these difficulties include malaria, tuberculosis and HIV, and they alone account for half of the global infectious disease burden. The whole organism vaccine approach pioneered by Jenner in 1796 and refined by Pasteur in 1857 with the "isolate, inactive and inject" paradigm has proved highly successful for many viral and bacterial pathogens causing acute disease but has failed with respect to malaria, tuberculosis and HIV as well as many other diseases. A significant advance of the past decade has been the elucidation of the genomes, proteomes and transcriptomes of many pathogens. This information provides the foundation for new 21(st) Century approaches to identify target antigens for the development of vaccines, drugs and diagnostic tests. Innovative genome-based vaccine strategies have shown potential for a number of challenging pathogens, including malaria. We advocate that genome-based rational vaccine design will overcome the problem of poorly immunogenic, poorly protective vaccines that has plagued vaccine developers for many years.International Journal for Parasitology 09/2014; 44(12). DOI:10.1016/j.ijpara.2014.07.010 · 3.40 Impact Factor
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ABSTRACT: In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.PLoS ONE 12/2014; 9(12):e115664. DOI:10.1371/journal.pone.0115664 · 3.53 Impact Factor