- Three HPC techniques for fighting cancer: Virtual screening, molecular modeling, and evolutionary comparison
- Simulations reveal a complexity impossible to view via other experiments
- Supercomputers save time sifting through possible drug compounds — saving time saves lives
Surgery and radiation remove, kill, or damage cancer cells in a specific area. But chemotherapy —using medicines or drugs to treat cancer — can work throughout the whole body, killing cancer cells that have spread far from the original tumor.
Finding new drugs that can more effectively kill cancer cells or disrupt the growth of tumors is one way to improve survival rates for patients.
Increasingly, researchers looking to uncover and test new drugs use powerful supercomputers like those developed and deployed by the Texas Advanced Computing Center (TACC).
"Advanced computing is a cornerstone of drug design and the theoretical testing of drugs," says Matt Vaughn, TACC's director of Life Science Computing.
"The sheer number of potential combinations that can be screened in parallel before you ever go in the laboratory makes high-performance computing (HPC) resources invaluable for cancer research."
Virtual screening
Shuxing Zhang, a researcher at Experimental Therapeutics at the University of Texas (UT) MD Anderson Cancer Center, leads a lab dedicated to computer-assisted rational drug design and discovery of novel targeted therapeutic agents.
The group develops new computational methods, using artificial intelligence and HPC-based virtual screening strategies to explore how a large number of small molecule compounds 'dock,' or bind, to a target to determine if they may be candidates for future drugs.
One specific biological target Zhang's group investigates is an enzyme called TNIK (TRAF2- and NCK-interacting kinase). TNIK plays a key role in cell signaling related to colon cancer. Silencing TNIK may suppress the proliferation of colorectal cancer cells.
Writing in Nature Scientific Reports, Zhang reports results of a study that investigates known compounds with desirable properties that might act as TNIK inhibitors.
In silico virtual screening paints a clear picture of what types of molecules may and may not bind to a receptor, saving a lot of time. ~ Joe Allen, TACC research associate
Using the Lonestar supercomputer, they screened 1,448 Food and Drug Administration-approved small molecule drugs to determine which had the molecular features needed to bind and inhibit TNIK.
They discovered that one — mebendazole, a drug that fights parasites — could effectively bind to the target. After testing it experimentally, they further found that the drug could also selectively inhibit TNIK's enzymatic activity.
As an FDA-approved drug that can be used at higher dosages without severe side effects, mebendazole is a strong candidate for further exploration and may even exhibit a synergic anti-tumor effect when used with other anti-cancer drugs.
"Such advantages render the possibility of quickly translating the discovery into a clinical setting for cancer treatment in the near future," Zhang writes.
Guardian of the genome
Tumor protein 53 (p53) plays a crucial role in multicellular organisms, conserving the stability of DNA by preventing mutations and thereby acting as a tumor suppressor.
However, in about half of all human cancers, p53 is mutated and rendered inactive. Reactivation of mutant p53 using small molecules has been a long-sought-after anticancer therapeutic strategy.
Rommie Amaro, professor of chemistry and biochemistry at the University of California, San Diego has been studying this important molecule for years trying to understand how it works.
In September 2016 she reported results from the largest atomic-level simulation of the tumor suppression protein to date —more than 1.5 million atoms. The simulations helped to identify new binding sites on the protein surface where it may be possible to insert a small molecule that could reactivate p53.
"We could see how when the full-length p53 was bound to a DNA sequence that was a recognition sequence, the tetramer clamps down and grips onto the DNA – which was unexpected," Amaro says.
In contrast, with the negative control DNA, p53 stays more open. "It actually relaxes and loosens its grip on the DNA," she says. "It suggested a mechanism by which this molecule could actually change its dynamics depending on the exact sequence of DNA."
According to Amaro, computing provides a better understanding of cancer mechanisms and possible novel therapeutic avenues. They reveal complexity that is very difficult, if not impossible, to experimentally test.
When most people think about cancer research they probably don't think about computers, but biophysical models are getting to the point where they have a great impact on the science. ~ Rommie Amaro
Evolutionary comparisons
A completely different approach to drug discovery involves studying the evolution of plants that are known to be effective chemotherapeutic agents and their genetic relatives, since plants that share an evolutionary history often share related collections of chemical compounds.
UT researchers working with researchers from King Abdulaziz University in Saudi Arabia, the University of Ottawa and Université de Montréal — have been studying Rhazya stricta, an environmentally stressed, poisonous evergreen shrub found in Saudi Arabia.
According to Robert Jansen, professor of Integrative Biology at UT and lead researcher on the project, the computational resources at TACC were essential for constructing and studying the plant's genome.
"These analyses allowed the identification of genes involved in the monoterpene indole alkaloid pathway, and in some cases expansions of gene families were detected," he says.
"There are a nearly infinite number of possible drug compounds," Vaughn says. "But knowing the principles of what a good drug might look like – how it might bind to a certain pocket or what it might need to resemble – helps narrow the scope immensely, accelerating discoveries while reducing costs."
