People exert large amounts of problem-solving effort playing computer games. Simple image-and text-recognition tasks have been successfully 'crowd-sourced' through games 1-3 , but it is not clear if more complex scientific problems can be solved with humandirected computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology 4 , while they compete and collaborate to optimize the computed energy. We show that 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 integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems. Although it has been known for over 40 years that the threedimensional structures of proteins are determined by their amino acid sequences 5 , protein structure prediction remains a largely unsolved problem for all but the smallest protein domains. The state-of-the-art Rosetta structure prediction methodology, for example, is limited primarily by conformational sampling; the native structure almost always has lower energy than any non-native conformation, but the free energy landscape that must be searched is extremely large-even small proteins have on the order of 1,000 degrees of freedom-and rugged due to unfavourable atom-atom repulsion that can dominate the energy even quite close to the native state. To search this landscape, Rosetta uses a combination of stochastic and deterministic algorithms: rebuilding all or a portion of the chain from fragments; random perturbation to a subset of the backbone torsion angles; combinatorial optimization of protein side-chain conformations; gradient-based energy minimization; and energy-dependent acceptance or rejection of structure changes 6-8 . We hypothesized that human spatial reasoning could improve both the sampling of conformational space and the determination of when to pursue suboptimal conformations if the stochastic elements of the search were replaced with human decision making while retaining the deterministic Rosetta algorithms as user tools. We developed a multiplayer online game, Foldit, with the goal of producing accurate protein structure models through gameplay ( Fig.
Foldit REF is an online multiplayer game whose players interact with protein structures while they compete and collaborate to optimize the computed energy.
Predicting protein structures with a multiplayer online game
Nature
Nature
Medicine Computer Science Biology