Computational analysis points to potential HIV targets

Computer geeks are hot on the trail of HIV. Building on the knowledge that HIV needs certain human proteins to spread, a group of researchers used a specialized algorithm to analyze networks of proteins and further our understanding of what proteins in our bodies serve the interests of the virus. Their work could lead to targets for HIV therapies or prognostic markers of disease.

Computer scientist T.M. Murali and Brett Tyler of the Virginia Bioinformatics Institute created the algorithm, called SinkSource, which was used to predict so-called HIV dependency factors (HDFs). These are the host proteins that aid HIV's survival. And while hundreds of HDFs have been studied before, the researchers hypothesized that many of the HIV-enabling proteins remained undiscovered.

"We found that SinkSource and one of its variants made predictions of very high quality," Murali said in statement. "We evaluated predicted HDFs using a number of additional datasets that we did not use during the prediction step."

The group analyzed data from an earlier study of simian immunodeficiency virus (SIV) activities in non-human primates, showing that predicted HDFs had different levels of activity in the African green monkey and the pig-tailed macaque. It was a clever comparison because SIV doesn't cause disease in the green monkey as it does in the macaque. The analysis shed light on the roles of the HDFs in aspects of host cellular processes that HIV corrupts for its survival.

"Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of [AIDS] development," the authors concluded in a paper published Sept. 22 in PLoS Computational Biology.

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