Abstract
BackgroundThis 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.ResultsA 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.ConclusionsThe 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.
Highlights
Human malaria is caused by five parasite species from the genus Plasmodium, of which Plasmodium falciparum has a preferential distribution in African countries and is important, since it produces most of the fatal cases
The first one consisted of constructing 36 profile hidden Markov models (HMMs) and a target data set of 582 P. vivax open reading frames (ORFs) with predominant transcription toward the end of the intra-erythrocyte cycle
In the second phase, the 36 profile HMMs were used for exploring the target data set, leading to the discovery of 46 P. vivax ORFs scanned by the profile HMMs
Summary
Human malaria is caused by five parasite species from the genus Plasmodium, of which Plasmodium falciparum has a preferential distribution in African countries and is important, since it produces most of the fatal cases. Progress in P. vivax research has been notably delayed by contrast with P. falciparum, partly due to the difficulty of establishing a long-term in vitro culture of this species given that it is restricted to invading human reticulocytes which only account for ,1–2% of circulating red blood cells. This difficulty has been reflected in the delayed release of its genome sequence [3], the transcriptional profile of its intra-erythrocyte developmental cycle [4] and the partial proteome of its schizont stage [5] compared to the release of the same studies in P. falciparum. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was screened using these classifiers
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