The leishmaniases are protozoal diseases that severely affect large populations in tropical and subtropical regions. There are only limited treatment options and preventative measures. Vaccines will be important for prevention, control and elimination of leishmaniasis, and could reduce the transmission and burden of disease in endemic populations. We report the development of a DNA vaccine against leishmaniasis that induced T cell-based immunity and is a candidate for clinical trials. The vaccine antigens were selected as conserved in various Leishmania species, different endemic regions, and over time. They were tested with T cells from individuals cured of leishmaniasis, and shown to be immunogenic and to induce CD4(+) and CD8(+) T cell responses in genetically diverse human populations of different endemic regions. The vaccine proved protective in a rodent model of infection. Thus, the immunogenicity of candidate vaccine antigens in human populations of endemic regions, as well as proof of principle for induction of specific immune responses and protection against Leishmania infection in mice, provides a viable strategy for T cell vaccine development.

Cancer testis antigen-based vaccines seem slightly superior over vaccines based on differentiation antigens providing support for the hypothesis.

Ant Colony Optimization (ACO) is a meta-heuristic that utilizes a computational analogue of ant trail pheromones to solve combinatorial optimization problems. The size of the ant colony and the representation of the ants' pheromone trails is unique referring to the given optimization problem. In the present study, we employed ACO to generate novel peptides that stabilize MHC I protein on the plasma membrane of a murine lymphoma cell line. A jury of feedforward neural network classifiers served as fitness function for peptide design by ACO. Bioactive murine MHC I H-2K(b) stabilizing as well as nonstabilizing octapeptides were designed, synthesized and tested. These peptides reveal residue motifs that are relevant for MHC I receptor binding. We demonstrate how the performance of the implemented ACO algorithm depends on the colony size and the size of the search space. The actual peptide design process by ACO constitutes a search path in sequence space that can be visualized as trajectories on a self-organizing map (SOM). By projecting the sequence space on a SOM we visualize the convergence of the different solutions that emerge during the optimization process in sequence space. The SOM representation reveals attractors in sequence space for MHC I binding peptides. The combination of ACO and SOM enables systematic peptide optimization. This technique allows for the rational design of various types of bioactive peptides with minimal experimental effort. Here, we demonstrate its successful application to the design of MHC-I binding and nonbinding peptides which exhibit substantial bioactivity in a cell-based assay.

Experimental results are presented for 180 in silico designed octapeptide sequences and their stabilizing effects on the major histocompatibility class I molecule H-2Kb. Peptide sequence design was accomplished by a combination of an ant colony optimization algorithm with artificial neural network classifiers. Experimental tests yielded nine H-2Kb stabilizing and 171 nonstabilizing peptides. 28 among the nonstabilizing octapeptides contain canonical motif residues known to be favorable for MHC I stabilization. For characterization of the area covered by stabilizing and non-stabilizing octapeptides in sequence space, we visualized the distribution of 100,603 octapeptides using a self-organizing map. The experimental results present evidence that the canonical sequence motives of the SYFPEITHI database on their own are insufficient for predicting MHC I protein stabilization.