Public Release: 

New human protein targets for the control of HIV are predicted using computational analysis


A new computational approach has predicted numerous human proteins that the human immunodeficiency virus (HIV) requires to replicate itself, and "constitutes a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV," according to the authors of a study, which will be published in the open-access journal PLoS Computational Biology on Thursday 22nd September 2011.

"Drugs used to cure HIV become rapidly ineffective because HIV is able to develop mutations against drugs," author T. M. Murali of Virginia Tech's Department of Computer Science explained. Since viruses such as HIV have very small genomes, they must exploit the cellular machinery of the host to spread. Researchers 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.

Many hundreds of HIV Dependency Factors (HDFs) have already been discovered but the authors hypothesized that it would be possible to predict new HDFS by their placement within networks of interacting human proteins. To this end, they created an algorithm called SinkSource.

Brett Tyler, of the Virginia Bioinformatics Institute at Virginia Tech, explained the algorithm using this analogy: "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). 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. Using data already published by author Michael G. Katze, associate director of the Washington National Primate Research Center, the authors 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.

The authors 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.


FINANCIAL DISCLOSURE: Public Health Service grants P30DA015625, P51RR000166, and R24RR016354 from the National Institutes of Health to MGK, grants from the Virginia Bioinformatics Institute Fellows program to TMM and BMT, and a grant from the ASPIRES program at the Virginia Polytechnic Institute and State University to TMM supported this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

COMPETING INTERESTS: I have read the journal's policy and have the following conflicts: MDD is employed by and owns stock in Life Technologies.

CITATION: Murali TM, Dyer MD, Badger D, Tyler BM, Katze MG (2011) Network-Based Prediction and Analysis of HIV Dependency Factors. PLoS Comput Biol 7(9): e1002164. doi:10.1371/journal.pcbi.1002164


Lynn Nystrom, Director of News and External Relations, College of Engineering, Virginia Tech
Telephone: 540 231 4371


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