The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Algorithm discovery by protein folding game players
Open accessAbstract
Foldit is a multiplayer online game in which players collaborate and compete to create accurate protein structure models. For specific hard problems, Foldit player solutions can in some cases outperform state-of-the-art computational methods. However, very little is known about how collaborative gameplay produces these results and whether Foldit player strategies can be formalized and structured so that they can be used by computers. To determine whether high performing player strategies could be collectively codified, we augmented the Foldit gameplay mechanics with tools for players to encode their folding strategies as “recipes” and to share their recipes with other players, who are able to further modify and redistribute them. Here we describe the rapid social evolution of player-developed folding algorithms that took place in the year following the introduction of these tools. Players developed over 5,400 different recipes, both by creating new algorithms and by modifying and recombining successful recipes developed by other players. The most successful recipes rapidly spread through the Foldit player population, and two of the recipes became particularly dominant. Examination of the algorithms encoded in these two recipes revealed a striking similarity to an unpublished algorithm developed by scientists over the same period. Benchmark calculations show that the new algorithm independently discovered by scientists and by Foldit players outperforms previously published methods. Thus, online scientific game frameworks have the potential not only to solve hard scientific problems, but also to discover and formalize effective new strategies and algorithms.
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Cites
Predicting protein structures with a multiplayer online game
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368
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Cited by
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Self-citationMacromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888
Self-citationBenchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335
Gamification and Serious Games for Personalized Health
Simon, M. (2012) · Studies in Health Technology and Informatics · doi:10.3233/978-1-61499-069-7-85 · Cited by 301
Metadata
- Authors
- Khatib, F., Cooper, S., Tyka, M. D., Xu, K., Makedon, I., Popović, Z., Baker, D., & Players, F.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-11-07
- Volume
- 108
- Issue
- 47
- Pages
- 18949-18953
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2043284163
- Cited by
- 582
- References
- 1
- FWCI
- 15.40
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Khatib, F., Cooper, S., Tyka, M. D., Xu, K., Makedon, I., Popović, Z., Baker, D., & Players, F. (2011). Algorithm discovery by protein folding game players. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1115898108
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