The flu virus is a wily target, constantly mutating to avoid attack from the immune system and from antiviral drugs like Tamiflu. But in research presented Sunday at the annual meeting of the American Society for Cell Biology (ASCB) in San Diego, scientists announced a new method for fighting pandemic influenzas such as H1N1 (swine) and H5N1 (avian).
The approach involves using massive amount of computer power to simulate never-before-seen conformations of a virus. Using the method, researchers at the University of California at San Diego have not only identified a new molecular target for influenza drugs, they have also found drugs already approved by the U.S. Food and Drug Administration that just might hit the target perfectly.
The target in question is a single, large protein called neuraminidase–one of two major proteins present on the surface of the influenza virus–that allows newly replicated viruses to be released into their host. Because most pandemic versions share the same neuraminidase subtype, N1, the protein is an ideal drug target.
Most molecular imaging or modeling focuses on determining the arrangement of atoms in a molecule’s crystal structure–a lengthy, energy-intensive process that provides a precise way to capture the molecule’s shape but only in one conformation, frozen at a single moment in time. In contrast, the new “relaxed complex” method models the virus protein molecule in a state that provides a better understanding of how the protein behaves and even revealing conformations that rarely occur.
Biochemist Andrew McCammon and undergraduate lab member Daniel Dadon used a sophisticated computer program to simulate all possible conformations–27 in all–of the H1N1 virus’s flexible neuraminidase protein. Rather than forcing the protein into a single crystal structure’s conformation, “[we] got a movie of how the protein would behave in nature,” Dadon says. “It’s like frames from a film, rather than a single photograph.”
Dadon aligned each of those 27 neuraminidase conformations and found that all of them had a binding site that remained unchanged, a single spot that could act as a prime inhibitor target. The researchers then looked at a library of drugs already approved by the FDA. After breaking molecular models of the drugs down into small fragments, they ran them through a colossal search algorithm in order to find those molecules with the highest affinity for the neuraminidase binding site.
“If you start with compounds that are FDA-approved, it may be a faster way to find good drug leads,” says Rommie Amaro, who specializes in pharmaceutical and computer sciences at the University of California at Irvine. “There’s a long process to get a drug reviewed, and the molecules have to be metabolically okay for people to ingest. So instead of starting with random leads from a chemical library, if you start with compounds that are FDA-approved, you could already have the more harmful compounds weeded out.”
The process gave Dadon 15 hits, all with a higher binding affinity for H1N1 than any of the antivirals already approved for use against flu. Because all 15 of those compounds had a single substructure in common, Dadon looked for molecules already being produced by chemical and drug companies that contained that substructure. He found six of them, and all are currently being tested against H1N1 by collaborators in Australia.
The flu virus mutations known to resist antiviral drugs appear to occur in an altogether different binding site than the one Dadon and his colleagues discovered. Such a distinction is important, especially as reported cases of H1N1 resistance to Tamiflu are on the rise. If any of the researchers’ six molecules prove successful, the resulting drug could provide a second line of attack to be used where current antivirals fail.
McCammon used an earlier version of the same method to discover novel inhibitors for a key HIV enzyme. But the technique is still relatively obscure in the drug-discovery world. “The relaxed complex method is not widely used because of the amount of computing time that it requires,” says Wilfred Li, a bioinformatics specialist at UCSD’s San Diego Supercomputing Center.
Li also notes that such studies are quite computationally expensive. “This study really stresses the need for a better computing infrastructure so more proteins can be studied in this fashion. These techniques can be used to develop new, interesting, and more potent inhibitors.”