Nature, volume 466, issue 7307, pages 756-760
Predicting protein structures with a multiplayer online game
Seth Cooper
1
,
Firas Khatib
2
,
Adrien Treuille
1, 3
,
Janos Barbero
1
,
Jeehyung Lee
3
,
Michael Beenen
1
,
Andrew Leaver-Fay
2, 4
,
David Baker
2, 5
,
Zoran Popović
1
,
Foldit Players
2
4
Present address: Department of Biochemistry, University of North Carolina, CB 7260, Chapel Hill, North Carolina 27599, USA.,
|
Publication type: Journal Article
Publication date: 2010-08-03
PubMed ID:
20686574
Multidisciplinary
Abstract
A natural polypeptide chain can fold into a native protein in microseconds, but predicting such stable three-dimensional structure from any given amino-acid sequence and first physical principles remains a formidable computational challenge. Aiming to recruit human visual and strategic powers to the task, Seth Cooper, David Baker and colleagues turned their 'Rosetta' structure-prediction algorithm into an online multiplayer game called Foldit, in which thousands of non-scientists competed and collaborated to produce a rich set of new algorithms and search strategies for protein structure refinement. The work shows that even computationally complex scientific problems can be effectively crowd-sourced using interactive multiplayer games. Predicting the structure of a folded protein from first principles for any given amino-acid sequence remains a formidable computational challenge. To recruit human abilities to the task, these authors turned their Rosetta structure prediction algorithm into an online multiplayer game in which thousands of non-scientists competed and collaborated to produce new algorithms and search strategies for protein structure refinement. This shows that computationally complex problems can be effectively 'crowd-sourced' through interactive multiplayer games. 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.
Top-30
Citations by journals
5
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30
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PLoS ONE
27 publications, 2.97%
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Lecture Notes in Computer Science
26 publications, 2.86%
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ACS Symposium Series
14 publications, 1.54%
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Proceedings of the National Academy of Sciences of the United States of America
13 publications, 1.43%
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Nature Biotechnology
9 publications, 0.99%
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Nature
9 publications, 0.99%
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Journal of Molecular Biology
7 publications, 0.77%
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Proceedings of the ACM on Human-Computer Interaction
6 publications, 0.66%
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Scientific Reports
6 publications, 0.66%
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PLoS Computational Biology
6 publications, 0.66%
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Methods in Molecular Biology
6 publications, 0.66%
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Bioinformatics
6 publications, 0.66%
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5 publications, 0.55%
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Computers in Human Behavior
5 publications, 0.55%
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SSRN Electronic Journal
5 publications, 0.55%
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Computational and Structural Biotechnology Journal
5 publications, 0.55%
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Proteins: Structure, Function and Genetics
5 publications, 0.55%
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F1000Research
4 publications, 0.44%
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Applied Sciences (Switzerland)
4 publications, 0.44%
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Drug Discovery Today
4 publications, 0.44%
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Biochemistry and Molecular Biology Education
4 publications, 0.44%
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Journal of Chemical Education
4 publications, 0.44%
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IEEE Access
4 publications, 0.44%
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Science
4 publications, 0.44%
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Journal of Microbiology and Biology Education
4 publications, 0.44%
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Journal of Chemical Physics
3 publications, 0.33%
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PeerJ
3 publications, 0.33%
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Nature Communications
3 publications, 0.33%
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Nature Methods
3 publications, 0.33%
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Citations by publishers
20
40
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160
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Association for Computing Machinery (ACM)
146 publications, 16.06%
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Springer Nature
134 publications, 14.74%
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Elsevier
97 publications, 10.67%
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Wiley
54 publications, 5.94%
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IEEE
48 publications, 5.28%
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Public Library of Science (PLoS)
37 publications, 4.07%
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American Chemical Society (ACS)
34 publications, 3.74%
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Taylor & Francis
23 publications, 2.53%
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Oxford University Press
22 publications, 2.42%
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Multidisciplinary Digital Publishing Institute (MDPI)
19 publications, 2.09%
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Cold Spring Harbor Laboratory
18 publications, 1.98%
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IGI Global
14 publications, 1.54%
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Proceedings of the National Academy of Sciences (PNAS)
13 publications, 1.43%
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Frontiers Media S.A.
12 publications, 1.32%
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Royal Society of Chemistry (RSC)
11 publications, 1.21%
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Cambridge University Press
9 publications, 0.99%
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Emerald
8 publications, 0.88%
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SAGE
8 publications, 0.88%
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Wolters Kluwer Health
7 publications, 0.77%
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American Association for the Advancement of Science (AAAS)
7 publications, 0.77%
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JMIR Publications
6 publications, 0.66%
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IOP Publishing
6 publications, 0.66%
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Mary Ann Liebert
5 publications, 0.55%
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Social Science Electronic Publishing
5 publications, 0.55%
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Walter de Gruyter
5 publications, 0.55%
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American Society for Microbiology
5 publications, 0.55%
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Annual Reviews
5 publications, 0.55%
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American Institute of Physics (AIP)
4 publications, 0.44%
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F1000 Research
4 publications, 0.44%
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160
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Cooper S. et al. Predicting protein structures with a multiplayer online game // Nature. 2010. Vol. 466. No. 7307. pp. 756-760.
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Cooper S., Khatib F., Treuille A., Barbero J., Lee J., Beenen M., Leaver-Fay A., Baker D., Popović Z., Players F. Predicting protein structures with a multiplayer online game // Nature. 2010. Vol. 466. No. 7307. pp. 756-760.
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TY - JOUR
DO - 10.1038/nature09304
UR - https://www.nature.com/articles/nature09304
TI - Predicting protein structures with a multiplayer online game
T2 - Nature
AU - Popović, Zoran
AU - Cooper, Seth
AU - Khatib, Firas
AU - Treuille, Adrien
AU - Barbero, Janos
AU - Lee, Jeehyung
AU - Beenen, Michael
AU - Leaver-Fay, Andrew
AU - Baker, David
AU - Players, Foldit
PY - 2010
DA - 2010/08/03 00:00:00
PB - Springer Nature
SP - 756-760
IS - 7307
VL - 466
PMID - 20686574
SN - 0028-0836
SN - 1476-4687
ER -
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@article{2010_Cooper,
author = {Zoran Popović and Seth Cooper and Firas Khatib and Adrien Treuille and Janos Barbero and Jeehyung Lee and Michael Beenen and Andrew Leaver-Fay and David Baker and Foldit Players},
title = {Predicting protein structures with a multiplayer online game},
journal = {Nature},
year = {2010},
volume = {466},
publisher = {Springer Nature},
month = {aug},
url = {https://www.nature.com/articles/nature09304},
number = {7307},
pages = {756--760},
doi = {10.1038/nature09304}
}
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Cooper, Seth, et al. “Predicting protein structures with a multiplayer online game.” Nature, vol. 466, no. 7307, Aug. 2010, pp. 756-760. https://www.nature.com/articles/nature09304.