mansoni. The current schistosomiasis treatment frequently does not cure 100% of those treated in high risk communities and the emergence of Schistosoma resistant strains is a real possibility. Thus, the identification of potential drug targets should be further emphasized. The recent sequencing of S. mansoni genome and large scale tran scriptome projects have yielded Belinostat cost crucial information to the identification of new candidate drugs. Understand ing protein structure and function in many model organ isms can help elucidate the function of their parasite homologs and further enable the application of such infor mation in drug design and development. The study of the kinase complement is therefore of major impor tance for the understanding of the physiology of the organism and also provides insights into how to disrupt the fine adaptative mechanisms.

The present work aimed at analyzing the S. mansoni predicted proteome data in order to identify all ePKs encoded in the genome of this parasite. For this purpose, we combined computational approaches such as sequence similarity searches using Hidden Markov Models and distance based phy logenetic analyses. The functional annotation was per formed mainly to yield insights into the signaling process related to the complex lifestyle of S. mansoni. Results and discussion The Schistosoma mansoni ePKinome The ePK complement of S. mansoni, defined as the ePKinome, was identified by searching the parasite predict proteome with a HMM profile of the ePK cataly tic domain of five selected organisms. This analysis revealed 252 ePKs in the S.

mansoni predicted pro teome, representing 1. 9% of the total proteins encoded in the parasite genome. Although the total number of protein kinases found across the analyzed species varies greatly, the percentage values in respect to the genomes of protozoan and helminth para sites as well as other eukaryotes from KinBase range only between 1. 5 to 2%. Amino acid sequences corresponding to the conserved catalytic domain of ePKs were aligned by MAFFT and further used in phylogenetic analysis based on a distance method as implemented in PHYLIP. The dataset for each ePK group also included the ePK homologs from six other eukaryotes, Homo sapiens, Mus musculus, Droso phila melanogaster, Caenorhabditis elegans, Saccharo myces cerevisiae, and Brugia malayi. This approach allowed us to classify the S.

mansoni ePKinome at the group, family, and or subfamily levels based on the hierar chy proposed elsewhere, and sometimes pro vided insights into kinase function and evolution. Detailed information is available in the Additional file 1 that contains, among other things, all S. mansoni Carfilzomib ePKs with the corresponding identifier from the genome project linked to SchistoDB database. SchistoDB allows the community to access to all sequences, annotations and other data types integrated into the genomic information.