When 17-year-old Eric Chen was preparing his entry for the 2013 Google Science Fair, an online competition for teens with ideas to change the world, he set his sights on finding a systematic way to discover novel compounds for a new kind of anti-flu medicine effective against all influenza viruses, including pandemic strains. While pursuing his research at the National Biomedical Computation Resource at the University of California, San Diego, the high school junior came across just the right software for the job: a computational modeling tool, FTMap, developed by Professor Sandor Vajda (BME, Chemistry) and Research Assistant Professor Dima Kozakov (BME), that was designed to facilitate drug discovery. Applying FTMap to the problem, he was able to pinpoint several candidate compounds.
Impressed with the project and its potential, an international panel of scientists recently named Chen as winner of the 2013 Google Science Fair Grand Prize and of its 17-18 age category. Chen beat out 89 other semifinalists (whittled down to 15 finalists in July) from across the globe who submitted projects advancing solutions to everything from cancer detection to environmental protection. At the awards ceremony in Google’s headquarters in Mountain View, California in late September, he received $50,000 in scholarship funding, a 10-day trip to the Galapagos Islands, and other gifts.
Chen used FTMap to search for novel compounds that could shut down endonuclease, a critical viral protein that enables flu viruses to survive and thrive. Combining FTMap results with biological studies, he identified a number of novel, potent endonuclease inhibitors.
“Chen’s success demonstrates that the FTMap server provides insightful analysis of protein binding sites and thus facilitates drug discovery,” said Kozakov. “Introduced in 2011, FTMap already has more than 1,000 regular users worldwide, and it is easy enough to use that even a talented high school student can generate spectacular results.”
In a nutshell, FTMap searches the surfaces of proteins such as endonuclease for areas that can bind to candidate drug molecules.
“The program places small organic molecules as molecular probes to find binding ‘hot spots’ that are important for protein-drug interactions, and to select specific functional groups [of atoms within the molecular probes] that tend to bind with the highest affinity at these locations,” Vajda explained. “The information provided by FTMap can be used both for virtually screening large libraries of available compounds and for the design of new molecules that incorporate the functional groups identified by the mapping.”