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Will more efficient protein folding program advance nanotechnology?

A few weeks ago we noted a claimed paradigm shift in understanding the scientific problem of protein folding. While waiting to learn if this claim has been confirmed or challenged, we found another item at Physorg.com that reports a promising new technique from MIT’s Computer Science and Artificial Intelligence Laboratory for modeling protein folding. As with the previous advance we will wait to see if increased understanding of the folding of natural, evolved proteins will aid efforts to design proteins from scratch to fold in a predetermined way as a path toward engineering components of advanced molecular machine systems. From an article written by Larry Hardesty of the MIT News Office “Understanding proteins: New model of protein folding helps researchers handle flood of genomic data“:

…In a series of recent papers, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have demonstrated a promising new technique for modeling such protein folding. While not as accurate as some existing techniques, it is much more computationally efficient. Sophisticated, atom-by-atom simulations that run on hundreds of thousands of computers might take months to model a few milliseconds of protein folding. The researchers’ new technique can model the same process in minutes on a single laptop.

Speed is of the essence as the amount of unprocessed genomic data proliferates. “There’s the Broad 1,000 Genomes project, there’s X many species that have been sequenced now, and the sequence data is just vastly outpacing the speed with which you could apply some of these other techniques,” says Charles O’Donnell, a PhD student in the Department of Electrical Engineering and Computer Science who helped develop the new approach. “If you want to make sense of all this high-throughput data that’s coming from this great biotech innovation, then you need something quick.”

Other “quick” methods of simulating protein folding exist, but the MIT researchers’ appears to be more accurate. There is still much we don’t know about the actual structure of proteins, O’Donnell cautions, so that makes assessing the quality of computational methods difficult. But at the 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) in July, the MIT researchers will present a paper demonstrating that for a class of proteins known as amyloids, their technique’s predictions match the currently available data with 81 percent accuracy, whereas high-efficiency techniques previously managed 42 percent at best.

Computational modeling of protein folding has been an active research area for decades, but “it hasn’t been entirely clear whether it was going to be useful or not,” says Susan Lindquist, an MIT professor of biology, recent recipient of the National Medal of Science, and, along with CSAIL’s Bonnie Berger and Srini Devadas, one of O’Donnell’s faculty advisors. “I think that this paper helps realize that goal.” …

“Protein folding continues to be wide-open problem with desperate need of more rigorous mathematical, statistical and computer-science approaches,” says Sorin Istrail, a professor of computer science at Brown University who specializes in computational biology. What distinguishes the MIT researchers’ work, he says, is its “rigorously mathematical results.” “The world needs to do what Bonnie and Charlie are doing,” Istrail says, “taking one aspect of the problem and building rigorous methods for that particular component.”

For those who would like to learn more about these new computational approaches to protein folding to see if they might be useful for protein design work for advanced nanotechnology, many of the papers can be downloaded from O’Donnell’s web site, and a number of public domain programs for protein structure prediction are available from Berger’s web site.

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