An important element of computational simulation is the bridge it creates between theory and experimental testing. Perhaps no other field exemplifies the significance of this link quite like pharmaceutical drug design—arguably one of the most costly and universally essential fields of scientific endeavor. With the help of one of the most powerful supercomputers in the world, scientists Yuri Peterson of the Medical University of South Carolina (MUSC) and Bhanu Rekepalli of the National Institute for Computational Sciences (NICS) are working on taking drug simulation to a new level—the petascale.
Petascale refers to computer systems capable of performing a quadrillion calculations per second (petaflops)—that’s a one followed by 15 zeros. HPC systems like Kraken make it possible to sort through vast arrays of compounds that may work as treatments against viruses and diseases. However, having this kind of computational power is only half of the solution; researchers need codes that can scale to a machine of such proportions. Rekepalli and Peterson have taken on this challenge by improving the speed and scaling of the molecular docking code Dock6.
The researchers are using Kraken, a Cray XT5 system housed at NICS, which has a peak performance of nearly 1.2 petaflops. NICS is funded by the National Science Foundation (NSF) and managed by the University of Tennessee.
Tweaking the process
Contemporary drug discovery starts with a process known as high-throughput screening (HTS) where huge public libraries of chemical compounds are tested for their effectiveness against a biological target—like a protein—that is known to play a key role in a disease. These libraries number into the millions and grow every year, making laboratory testing complex to say the least.
“Experimentalists identify 100 or so promising chemical compounds and they order perhaps 50 of these from a company and begin testing them experimentally against their target proteins,” explained Rekepalli. “It’s tedious, time consuming, and very expensive.”
While clinical testing (using microorganisms and eventually humans) will always be necessary before a drug can be brought to the market, computers can help to significantly narrow down a subset of compounds to be tested.
Yet the size of chemical libraries has even made computational HTS difficult as few scientists have access to systems capable of modeling millions of compounds in a reasonable time frame. Thankfully, powerful machines like Kraken provide a means for academic scientists and their students to study drug candidates at the molecular and even atomic level in record time, but it does require codes that can utilize such large systems, which is precisely what Rekepalli and Peterson have teamed up to do. Their collaboration grew from NICS’ involvement in EPSCoR, an NSF-funded program aimed at strengthening research and education in science and engineering throughout the United States.
“Our EPScOR project addresses how we can help researchers from various universities in South Carolina and in Tennessee to achieve high-level science with the facilities that we have at NICS,” explained Rekepalli.
Peterson’s group at MUSC was running Dock6 on a small cluster called CBRC (Computational Biology Resource Center) located on campus. The group was using the High Performance Docking (HP-D) application to study how 1.3 million compounds interact, or dock, with seven target proteins that have been identified in ovarian cancer. If this weren’t a large enough endeavor, the team also wanted to change the conformation of the proteins, meaning each compound would be docked to a few variations of each of the seven proteins.