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Background  This study describes a bioinformatics approach designed to identify  Plasmodium vivax  proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the  P. vivax  intra-erythrocyte developmental cycle was then screened using these classifiers.    Results  A bioinformatics methodology for identifying potentially secreted  P. vivax  proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the  P. vivax  intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25.    Conclusions  The results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against  P. vivax  malaria.

Identification of Plasmodium vivax Proteins with Potential Role in Invasion Using Sequence Redundancy Reduction and Profile Hidden Markov Models