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Gamers Beat Algorithms At Finding Protein Structures

Posted by samzenpus on from the tetris-trained dept.

jamie writes "Researchers have turned the biochemical challenge of figuring out protein folding structures into a computer game. The best players can beat a computerized algorithm by rapidly recognizing problems that the computer can't fix. From the article: 'By tracing the actions of the best players, the authors were able to figure out how the humans' excellent pattern recognition abilities gave them an edge over the computer. For example, people were very good about detecting a hydrophobic amino acid when it stuck out from the protein's surface, instead of being buried internally, and they were willing to rearrange the structure's internals in order to tuck the offending amino acid back inside. Those sorts of extensive rearrangements were beyond Rosetta's abilities, since the energy changes involved in the transitions are so large.'"

9 of 80 comments (clear)

  1. Great idea but seems tough to gamify problems by SuperKendall · · Score: 5, Interesting

    I thought Foldit was actually a pretty fun game and a great idea when it came out, and now that I'm reminded of it I'll have to go back and play some more. It's fantastic to have validation that humans are still excellent pattern recognition engines compared even to very modern algorithms and powerful computers.

    But to extend the idea more generally, seems rather hard. Foldit had the great insight to take you to an algorithmically close starting place and let you complete the final adjustments - in that way the algorithm itself is as much a part of the team as the detail or adjustment members they were talking about.

    I wonder how many other ideas can be so easily brought to a place close enough that a human can recognize patterns enough to be of use in a final solution. I look forward to seeing what astronomers come up with...

    --
    "There is more worth loving than we have strength to love." - Brian Jay Stanley
    1. Re:Great idea but seems tough to gamify problems by blahplusplus · · Score: 5, Interesting

      "e. It's fantastic to have validation that humans are still excellent pattern recognition engines compared even to very modern algorithms and powerful computers."

      Computers primary advantage is speed, for all our pattern recognition capability are mathematical capability is pretty limited besides modern computers. I think it's our ability to stitch or see things as wholes instead of millions of unconnected parts that gives us an advantage - we can recognize things like context that speed up the process significantly whether we are consciously aware or unconsciously recognizing context.

    2. Re:Great idea but seems tough to gamify problems by fraktalek · · Score: 4, Informative

      Foldit had the great insight to take you to an algorithmically close starting place and let you complete the final adjustments - in that way the algorithm itself is as much a part of the team as the detail or adjustment members they were talking about.

      This kind of problem solving was suggested already by Stanislaw Lem in his book Summa Technologiae as a kind of "augmented intelligence" as opposed to purely human or purely artifical intelligence.

  2. Re:In 10 years... by mongoose(!no) · · Score: 4, Insightful

    How is that any different than what we all do now?

  3. Re:Gamers? no Nerds? yes by CastrTroy · · Score: 4, Interesting

    The really funny part is that somebody programmed a bot to play the game, and it's doing better than the researcher's algorithm.

    --

    Anthropic principle: We see the universe the way it is because if it were different we would not be here to see it.
  4. Re:I've played a bit by the+gnat · · Score: 5, Informative

    forget about Foldit! Just download Folding@Home and let your CPU/GPU do it for you!

    FoldIt and Folding@Home are doing completely different things. FoldIt (or more specifically, the Rosetta software underneath it) is attempting to guess the final structure of novel protein sequences, using a variety of clever tricks such as mining the database of known structures for peptide motifs. It contains energy functions to evaluate candidate structures, but it is not simulating physical processes, and it tells you nothing about how the linear chain of amino acids forms the 3D structure. Folding@Home is used to study the process of protein folding, where the end result is already known; it isn't useful as a structure prediction tool. Both programs require a massive amount of computing power, but for very different reasons. Both are very useful, but there is almost no overlap in their practical applications. (And it should go without saying that while they can both be an excellent complement to experimental studies, neither can replace them.)

  5. Re:In 10 years... by Nadaka · · Score: 4, Funny

    I am pretty sure I already do this.

    On the plus side, if I can just plug in, it could reduce the eye strain from staring at a monitor all day.

  6. Re:Congrats, you might already be a Nature co-auth by Xoc-S · · Score: 4, Funny

    Does that give me a an Erdos number?

  7. Re:Congrats, you might already be a Nature co-auth by Zerth · · Score: 4, Informative

    If the Andrew Leaver-Fay in this paper is the same one who cowrote "Faster placement of hydrogens in protein structures by dynamic programming" with Jack Scott Snoeyink, then yes. It gives a 4.

    Best shot since I missed out on getting a 2 on Ebay.