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Multiplayer Online Game Helps Predict Protein Structures

By LabMedica International staff writers
Posted on 17 Aug 2010
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A multiplayer online game engages nonscientist gamers that compete and collaborate, interacting to solve complex protein-structure prediction problems.

Researchers at the University of Washington (Seattle, USA), Carnegie Mellon University (Pittsburgh, PA, USA), and the University of North Carolina (Chapel Hill, USA) developed the game, called Foldit, which has an extensive motivation and reward structure with a score system, player statuses, ranks, and forums. Different players have different strengths, and by having team competitions, those strengths are combined to get the best results. Over 57,000 players contribute or have contributed in the past, using direct manipulation tools and user-friendly versions of algorithms to promote gameplay. The researchers found that human three-dimensional (3D) structural problem solving were capable of overcoming difficult prediction problems where computer algorithms could become stuck. Conversely, the computer performed better when starting from a simple linear chain of amino acids.

The algorithms are based on the University of Washington Rosetta@home (please see related links below) project structure methodology; players compete and collaborate to optimize the computed energy. Top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The study describing Foldit was published in the August 5, 2010, issue of Nature.

"The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games could provide a powerful new approach to solving computationally-limited scientific problems,” concluded lead author Seth Cooper, a Ph.D. candidate at the University of Washington, and colleagues.

Related Links:
University of Washington
Carnegie Mellon University
University of North Carolina
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