chemistry

Supercomputer takes on super flu

TEXAS-AUSTIN (US)—A team of researchers has been using one of the world’s fastest supercomputers to look inside the swine flu virus and study how antiviral medications interact with the flu’s proteins.

Swine flu, also known at the A/H1N1 virus, first surfaced in Veracruz, Mexico in April 2009. There have been more than 11,000 cases reported and 85 fatalities. The researcher team was led by Klaus Schulten, a professor in the department of physics at the University of Illinois at Urbana-Champaign and Thanh Truong, a professor in the department of chemistry at the University of Utah, and their respective coworkers Eric Lee and Ly Le.

The project “turned very hot due to the worldwide health threat from swine flu,” says Schulten, director of the Theoretical and Computational Biophysics Group at the Beckman Institute at Illinois. When evidence emerged that the A/H1N1 virus that led to deaths in Mexico was resistant to Tamiflu, one of the most prescribed anti-flu drugs, the stakes rose higher.

The researchers faced some vital questions: How do drugs bind to the flu viruses? What made some forms of the virus resistant to previously effective drugs? And was it possible to find a new weak spot on the protein by which a drug could permanently disable the whole suite of influenza viruses?

These questions could only be answered, and answered quickly, by computational simulation.

“Simulations are the only way that one can visualize what is actually happening when a drug binds to a flu protein,” Lee says. “They let us to peek into the molecule so we can see the atomic interactions that are responsible for the protein and drug binding events. This is important, because drugs are designed with these specific atomic interactions in mind.”

The group had models of the Spanish and Avian flu virus, but to understand the current outbreak, they needed to determine the three-dimensional structure of the new H1N1a virus, or more specifically the virus’ neuraminidase protein: a mushroom-shaped projection on the surface of the influenza virus that plays a crucial role in the virus’ reproductive cycle.

Neuraminidase is the main target for commercial antiviral drugs, but it is also the most quickly evolving part of the virus, playing cat and mouse with drugs and necessitating new flu medications each year.

Combining genetic sequence data from swine flu samples with information from common ancestral characteristics (swine flu is 91 percent identical to Avian flu), the researchers created the first atomistic model of A/H1N1 neuraminidase.

Then, using a powerful atomic-level 3-D modeling program the Schluten group developed, the scientists prepared simulations to show how the three virus neuraminidases (Spanish, Avian, and Swine flu) interact with the two most-prescribed flu-fighting medicines (Tamiflu and Relenza), two phase III trial drugs candidates (Peramivir and A-315675), and Sialic acid, their natural target.

If they pushed the pace of their simulations, the research could provide useful insights in real time. But the researchers would need access to a supercomputer to develop a rapid prognosis of the swine flu virus’ evasion mechanism.

On April 30, Schulten called the Texas Advanced Computing Center (TACC) and received a special allocation on one of the world’s most powerful supercomputers to execute emergency simulations.

“This is a perfect example of cooperation among TeraGrid staff to provide a research team with time-critical access to the petascale resources funded by the National Science Foundation,” says Chris Hempel, TACC associate director of user services.

The group employed between 2,000 and 3,000 processors continuously over two weeks, and produced simulations that revealed how drugs normally bind to the neuraminidase and how changes to the A/H1N1 protein could cause drug resistance.

Antiviral drugs work by binding in a deep pocket—a receptor—on the surface of the neuraminidase. “If that receptor is occupied by drugs, like Tamiflu or Relenza, then production of the virus is stopped,” Schulten says. “The drug is like an off button.”

But that’s only part of the story. A look using the 3-D computational microscope revealed a side-channel in the Avian and Spanish versions of the protein that provided a shortcut for drugs to reach their binding site. Changes in the swine flu structure had closed the channel, they hypothesized, making necessary an approach through an electrostatically repulsive ring of molecules.

The current strain of A/H1N1 swine flu does not appear to be drug resistant yet, but the high rate of mutations among normal seasonal strains of the flu might transfer their Tamiflu drug-resistance genes to the swine flu. Relenza and the phase III trial drugs are effective against Tamiflu-resistant strains. However, according to researchers, resistance to these new drugs is likely not far behind, underscoring the need to understand precisely how mutations make the flu immune to antiviral therapy.

By anticipating the likely atomic-level mutations in the virus’ structure, they believe it will be possible to intelligently design a drug or vaccine that can’t be resisted.

“We’re in a race against nature,” Lee says. “Mutations happen naturally and we have to predict what might happen and stay one step ahead.”

The project represents a computational, as well as a scientific, breakthrough, according to Schulten. “We were able to learn something in a few weeks that usually would take many months,” he adds. “We investigated the site of the reactions and made sense of the interactions, and we think that our computational view can help create drugs that are optimally attuned to avoid resistance.”

Supercomputers routinely assist in emergency weather forecasting, earthquake predictions, and epidemiological research. Now, says Schulten, they are proving their usefulness in biomedical crises.

“It’s a historic moment,” he says. “For the first time these supercomputers are being used for emergency situations that require a close look with a computational tool in order to shape our strategy.”

University of Texas at Austin news: www.utexas.edu/news/

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