Targeting the HIV virus
(Ivanhoe Newswire) —Back to the future! Researchers are using a new computational approach to help fight HIV.
In 2009, 33.3 million people around the world were living with HIV/AIDS. More than 60 million people have been infected with HIV since the pandemic began. Now a new computational approach has predicted numerous human proteins that the HIV virus requires to replicate itself.
Since viruses such as HIV have very small genomes, researchers are exploiting the cellular machinery of the host to spread. They are examining whether human proteins can be targeted to cure HIV as they evolve at a much slower rate than HIV proteins and accordingly are unlikely to develop mutations that render the drugs ineffective.
To predict new HIV Dependency Factors by their placement within networks of interacting human proteins, they created an algorithm called SinkSource.
“We treated the human protein network as if it were a system of tanks connected by pipes carrying water. This arrangement allowed us to study the flow of predictive information (water) from proteins we are certain about (full tanks) to those we are uncertain about (empty tanks),” Brett Tyler, of the Virginia Bioinformatics Institute at Virginia Tech, was quoted saying. “The further you get from the full tanks, the weaker the trickle, and the less water accumulates in the bottom of the tank. Mathematically you can show that, over time, every empty tank accumulates some stable level of water. At the end of the analysis, tanks accumulating lots of water were judged to be good predictions.”
The authors found that SinkSource made predictions of high quality and used the algorithm to analyze HDF activities in two non-human primate species infected with Simian Immunodeficiency Virus (SIV), one of which develops disease and one of which doesn’t. It showed that predicted HDFs had very different patterns of expression in the two species, especially in lymph nodes and within 10 days after infection with the virus.
They concluded that many HDFs are yet to be discovered and they have potential value as prognostic markers to determine pathological outcome and the likelihood of Acquired Immune Deficiency Syndrome (AIDS) development.
SOURCE: PLoS Computational Biology, September 2011.