People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully ‘crowd-sourced’ through games1,2,3, but it is not clear if more complex scientific problems can be solved with human-directed 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 methodology4, 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.
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We thank D. Salesin, K. Tuite, J. Snyder, D. Suskin, P. Krähenbühl, A. C. Snyder, H. Lü, L. S. Tan, A. Chia, M. Yao, E. Butler, C. Carrico, P. Bradley, I. Davis, D. Kim, R. Das, W. Sheffler, J. Thompson, O. Lange, R. Vernon, B. Correia, D. Anderson, Y. Zhao, S. Herin and B. Bethurum for their help. We would like to thank N. Koga, R. Koga and A. Deacon and the JCSG for providing us with protein structures before their public release. We would also like to acknowledge all of the Foldit players who have made this work possible. Usernames of players whose solutions were used in figures can be found in Supplementary Table 4. This work was supported by NSF grants IIS0811902 and 0906026, DARPA grant N00173-08-1-G025, the DARPA PDP program, the Howard Hughes Medical Institute (D.B.), Microsoft, and an NVIDIA Fellowship. This material is based upon work supported by the National Science Foundation under a grant awarded in 2009.
This file contains Supplementary Text, Supplementary Figures S1-S14 with legends, Supplementary Tables S1-S4, Player Testimonials and References.
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