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"These results suggest not that the critical problems of protein-structure prediction are solved," says Baker, "but rather that accurate modeling now appears to be an achievable goal." To take it to the next level of accuracy, he says, will require still more computing power and better understanding of how linear sequences of amino acids transform into fully functional folded proteins.
Some kinds of proteins resist prediction more than others, however. "It's very difficult right now to accurately calculate interactions involving charged atoms," Baker says. "These are often in places like the active sites of enzymes, so this is a critical problem to solve. But more computing power will definitely help us search these landscapes better."
Even before Rosetta is refined to the point that it can accurately predict the structures of large proteins, it can be used to create altogether new proteins (see "Researchers Design and Build First Artificial Protein").
"There's no reason to rely strictly on what nature has provided through evolution," says Baker. "For example, we are interested in designing novel enzymes that catalyze reactions not catalyzed by naturally occurring proteins, and new endonucleases—proteins that can cleave DNA at a specific place—which could be useful in controlling pathogens. And we are very excited about our work using computational design methods to try to design a vaccine for HIV. You can imagine that the perfect vaccine might be a very stable, carefully designed protein that would guide the immune system to the Achilles heel of the virus, and that you could make in large amounts and ship all over the world." 
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