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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.

Bacterial secondary metabolites display diverse biological activities, thus having potential as pharmacological agents. Although most of these compounds are discovered by random screening, it is possible to predict and re-design their structures based on the information on their biosynthetic pathways. Biosynthesis of macrolides, governed by modular polyketide synthases (PKS), obeys certain rules, which can be simulated in silico. PKS mode of action theoretically allows for a huge number of macrolides to be produced upon combinatorial manipulation. Since engineering of all possible PKS variants is practically unfeasible, we created Biogenerator software, which simulates manipulation of PKS and generates virtual libraries of macrolides. These libraries can be screened by computer-aided prediction of biological activities, as exemplified by analysis of erythromycin and macrolactin libraries. This approach allows rational selection of macrolides with desired biological activities and provides instructions regarding the composition of the PKS gene clusters necessary for microbial production of such molecules.

Heparan sulphate is one of the candidate receptors for hepatitis C virus (HCV). Envelope glycoproteins of HCV have been proposed to be responsible for recognition and binding with cell receptors. They are characterized by great genetic polymorphism. In this study the mapping of regions with glycosaminoglycan-binding properties within HCV envelope proteins has been undertaken. We prepared a set of overlapping peptides corresponding to conserved regions of these envelope proteins and analysed them by solid phase heparin-binding assay. The search for established glycosaminoglycan-binding motifs in the HCV envelope proteins showed the absence of the sites corresponding to the glycosaminoglycan-binding patterns in consensus sequence. We identified one highly conserved and two less conserved heparin-binding sequences within the envelope protein E2 based on solid phase assay results. We did not find any differences in binding efficiency of these peptides with heparin, heparan sulphate or dextran sulphate. Our data supported the specific association between HCV envelope protein E2 and cell surface glycosaminoglycans. We hypothesize that identified regions from E2 can contribute to HCV binding to cell surface glycosaminoglycans.

Forty-eight overlapping octapeptides covering highly conservative regions of E1 and E2 hepatitis C virus (HCV) envelope proteins were synthesized and tested by ELISA against different groups of sera obtained from HCV-infected patients. All sera from patients with acute infection, except a single case of serum reactivity with the region HINRTALN, were nonreactive with any peptide. Sera obtained from chronic patients reacted with 12 peptides from five selected regions. Two immunodominant B epitopes were found, one being the precisely mapped antigenic site RMAWDM positioned inside the earlier shown immunodominant epitope from E1, and the second site, PALSTGLIH from E2, detected for the first time. New minor antigenic site was determined as PTDCFRKH from E2. We found only minor seroreactivity for one of the putative sites involved in CD81 binding, PYCWHYAP.

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The bacteriophage T4 late gene wac (whisker antigen control) encodes the protein which forms the fibrous structure on the neck of the virion called whiskers. Amino acid sequence analysis of wac gene product, as deduced from the nucleotide sequence, indicate ten alpha-helical domains (19-40 residues long) with coiled-coil structural patterns. These regions comprise about 70% of the entire 486 amino acid sequence. The alpha-helices are separated by short stretches of polypeptide chain which are similar to the loop regions of the globular protein sequences. We propose a structural model for the dimer of wac gene product molecule, that we call fibritin in which two polypeptide chains associate in a parallel fashion and form a segmented alpha-helical coiled-coil rod similar to epidermal keratins.

BackgroundThe 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.ResultsWe used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.ConclusionsIt was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.