Development of computer methods in molecular biology and fast growth of microbial genomics data enabled new approach based on selecting in silico antigenic components to design vaccine constructs. It is expected that application of this technology will eliminate side effects of new vaccines and reduce the time consumption and financial expenses. The bioinformatics methods of sequence analysis are used to reveal the most prospective proteins or protein fragments of infectious agents as candidates for vaccine design. In these studies the specialized molecular immunology databases are widely used. The new approach (“Reverse vaccinology”) could help in designing vaccines against diseases where traditional methods are not successful, e.g. when the viral genome reveals the extreme variability and permanent changes of antigenic properties that make difficulties for selection of molecular targets for medicines and candidate vaccines. A number of informational resources are already designed to collect and provide genomic data on certain microbes or viruses. The peculiarity of such resources is presentation of data, characterizing the different genomic variants of the same infectious agents. These structural data coupled with information on functional / immune features and software tools have to compose basis for constructing a new generation of vaccines against “common” and new infections such as AIDS, Hepatitis C, and SARS. The approaches published in literature, as well as the authors original results are discussed.

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.