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Biotech Supercomputing Science

Supercomputer Sets Protein-Folding Record 63

Nicros writes with this snippet from Nature News: "A specially designed supercomputer named Anton has simulated changes in a protein's three-dimensional structure over a period of a millisecond — a time-scale more than a hundred-fold greater than the previous record. ... The simulations revealed how the proteins changed as they folded, unfolded and folded again. 'The agreement with experimental data is amazing,' says Chandra Verma, a computational structural biologist at the Bioinformatics Institute of the Agency for Science, Technology and Research in Singapore. Simulating the basic pancreatic trypsin inhibitor over the course of a millisecond took Anton about 100 days — roughly as long as computers spent toiling over previous simulations that only spanned 10 microseconds."
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Supercomputer Sets Protein-Folding Record

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  • by ikkonoishi ( 674762 ) on Sunday October 17, 2010 @06:09AM (#33922874) Journal

    Here this should give you more information. []

    I think the article was alright though. It told what was going on, and why it could be important. It wasn't written for a computer nerd demographic so the exact specs weren't really relevant.

  • by anguirus.x ( 1463871 ) on Sunday October 17, 2010 @06:52AM (#33922992)
    The best way is to just compare them to the actual structure which is known from x-ray crystallography and NMR studies. They could do isotopic replacement studies to get ideas about which hydrogrens or nitrogens or carbons were kinetically involved in the folding process and see whether the same atoms were important in the simulation. If they're the same it could just be a coincidence, or it could be an indication that the folding itself is accurate on the computer, not just the final structure. This is truly amazing, but we've still got a long ways to go. MOAR COMPUTERS PRZ!
  • by the gnat ( 153162 ) on Sunday October 17, 2010 @10:30AM (#33923794)

    Nature and Science are not for hard science.

    The actual research articles are hard science - this was just a news story for a general audience. The official publication of the results [] in Science magazine appears to be a pretty serious piece of work, and it's significant enough that the editors allowed them to make it reasonably long instead of a (severely compressed) three- or four-page summary article like most of what they publish. There are lots of valid criticisms of those two journals, starting with their length requirements, but they're not Scientific American, and publishing in one of these is practically a prerequisite for getting a faculty position in biosciences at a major research university.

  • by the gnat ( 153162 ) on Sunday October 17, 2010 @10:43AM (#33923856)

    That's a little unfair to Folding@Home. Shaw has a lot of resources to pour into this project - he's lured faculty members away from universities to work for him instead and has the equivalent of several large labs worth of advanced researchers. He also has an immensely larger budget than most non-profit labs, and he's self-employed so he doesn't have to answer to granting agencies or tenure committees. I think what he's doing is great but he's really one of the only people who could have pulled this off. It's difficult to know what approach will work best in advance, and both Shaw and Vijay Pande have been very innovative in approaching the problem from completely different angles.

    By the way, this approach has been tried before with less stellar results - I'm thinking of the MD-GRAPE project in Japan. You're also assuming that every problem is equally well suited towards custom ASICs, but actually, molecular dynamics is far easier to do this with than many other methods. For instance, Rosetta (Rosetta@Home and Fold.It) is doing structure prediction, not folding, using a mostly statistics-based energy function and Monte Carlo sampling, and this isn't something you can trivially offload to a specialized chip. In that case, distributed computing is by far the most efficient solution.

  • by ElektronSpinRezonans ( 1397787 ) on Sunday October 17, 2010 @11:09AM (#33923990)
    This was not an ab initio, calculation. It's all atom MD, which itself is an approximation. People have done ab initio calculations on 10-15 residue peptides, but that's about all you can do with current computational power.

    I believe the article is published in Science not because of its computer utilization (i.e. using a bad-ass super computer), but because of its biological relevance. They managed to characterize not only the peptides conformations, but also their mutant's behavior in silico.
  • by Samantha Wright ( 1324923 ) on Sunday October 17, 2010 @11:23AM (#33924050) Homepage Journal
    No, Anton simulated one millisecond over the course of a hundred days. The previous recordholder took roughly the same time to do a hundredth of the work. (This was probably the RIKEN MDGRAPE-3, but again, documentation is le sparse.)
  • by Anonymous Coward on Sunday October 17, 2010 @12:55PM (#33924600)

    Here ya go, whinger:

    David E. Shaw, et al "Anton, A Special-Purpose Machine for Molecular Dynamics Simulation," Communications of the ACM, vol. 51, no. 7, 2008, pp. 91–97.

    Jeffrey S. Kuskin, et al "Incorporating Flexibility in Anton, a Specialized Machine for Molecular Dynamics Simulation," Proceedings of the 14th Annual International Symposium on High-Performance Computer Architecture (HPCA '08), Salt Lake City, Utah, February 16–20, 2008.

    Richard H. Larson, et al "High-Throughput Pairwise Point Interactions in Anton, a Specialized Machine for Molecular Dynamics Simulation," Proceedings of the 14th Annual International Symposium on High-Performance Computer Architecture (HPCA '08), Salt Lake City, Utah, February 16–20, 2008.

  • Here (Score:1, Informative)

    by Anonymous Coward on Sunday October 17, 2010 @02:00PM (#33925076)

    I didn't RTFA since I've already heavily researched these guys. D.E Shaw is the kind of billionaire I would be.

    Summary: The actual atomic interaction equations are simulated very fast. Distributing the results of a local interaction to the rest of the simulation quickly, is hard.,%20a%20Parallel%20Machine%20with%20Heterogeneous%20Multicore%20ASICs.pdf [] []

How can you work when the system's so crowded?