Philip Bourne of the University of California, San Diego, was curious as to why the anti-HIV drug nelfinavir had a surprisingly beneficial side-effect. It also reduced tumors in a variety of cancers.
Science Now, an AAAS publication, reports on how Bourne and colleagues assembled a database that contained all of the known shapes of the proteins that make up the human body. Then they developed an algorithm based nelfinavir's structure and found 92 binding partners, 85 of which belonged to a family of proteins called protein kinases that are linked to growth of tumors. Kinases work differently from HIV enzymes, but their shapes are similar. So, nelfinavir apparently works not only works against HIV, it also works as an anti-cancer kinase inhibitor.
Vijay Pande, a biophysicist at Stanford University who was not involved in the study, reacted to the study with some enthusiasm, telling Science Now, "This is part of a paradigm shift in how people are thinking about drug design. [It] will open our eyes to compounds that we might not have found before."
The researchers, writing in the open-access journal PLoS Computational Biology, say this approach can be used to identify many drug targets on the cheap. "In this next century, [drug design] is going to be considerably harder than in the last 50 years," Pande tells Science Now, adding that computational biology approaches like Bourne's illustrate effective alternatives to fighting disease.
- read the story from ScienceNow
- and the article in PLoS Computational Biology