Identification of Schistosoma mansoni candidate antigens for diagnosis of schistosomiasis

The development of a more sensitive diagnostic test for schistosomiasis is needed to overcome the limitations of the use of stool examination in low endemic areas. Using parasite antigens in enzyme linked immunosorbent assay is a promising strategy, however a more rational selection of parasite antigens is necessary. In this study we performed in silico analysis of the Schistosoma mansoni genome, using SchistoDB database and bioinformatic tools for screening immunogenic antigens. Based on evidence of expression in all parasite life stage within the definitive host, extracellular or plasmatic membrane localization, low similarity to human and other helminthic proteins and presence of predicted B cell epitopes, six candidates were selected: a glycosylphosphatidylinositol-anchored 200 kDa protein, two putative cytochrome oxidase subunits, two expressed proteins and one hypothetical protein. The recognition in unidimensional and bidimensional Western blot of protein with similar molecular weight and isoelectric point to the selected antigens by sera from S. mansoni infected mice indicate a good correlation between these two approaches in selecting immunogenic proteins

Clustering and artificial neural networks: classification of variable lengths of Helminth antigens in set of domains

A new scheme for representing proteins of different lengths in number of amino acids that can be presented to a fixed number of inputs Artificial Neural Networks (ANNs) speel-out classification is described. K-Means's clustering of the new vectors with subsequent classification was then possible with the dimension reduction technique Principal Component Analysis applied previously. The new representation scheme was applied to a set of 112 antigens sequences from several parasitic helminths, selected in the National Center for Biotechnology Information and classified into fourth different groups. This bioinformatic tool permitted the establishment of a good correlation with domains that are already well characterized, regardless of the differences between the sequences that were confirmed by the PFAM database. Additionally, sequences were grouped according to their similarity, confirmed by hierarchical clustering using ClustalW