| Title |
Determining crystal structures through crowdsourcing and coursework
|
|---|---|
| Published in |
Nature Communications, September 2016
|
| DOI | 10.1038/ncomms12549 |
| Pubmed ID | |
| Authors |
Scott Horowitz, Brian Koepnick, Raoul Martin, Agnes Tymieniecki, Amanda A. Winburn, Seth Cooper, Jeff Flatten, David S. Rogawski, Nicole M. Koropatkin, Tsinatkeab T. Hailu, Neha Jain, Philipp Koldewey, Logan S. Ahlstrom, Matthew R. Chapman, Andrew P. Sikkema, Meredith A. Skiba, Finn P. Maloney, Felix R. M. Beinlich, Zoran Popović, David Baker, Firas Khatib, James C. A. Bardwell |
| Abstract |
We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 21% |
| United Kingdom | 8 | 11% |
| Australia | 3 | 4% |
| Denmark | 3 | 4% |
| Canada | 3 | 4% |
| Japan | 2 | 3% |
| Austria | 2 | 3% |
| Spain | 2 | 3% |
| Sweden | 1 | 1% |
| Other | 5 | 7% |
| Unknown | 29 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 40 | 55% |
| Scientists | 29 | 40% |
| Science communicators (journalists, bloggers, editors) | 3 | 4% |
| Unknown | 1 | 1% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| United States | 2 | 2% |
| Ireland | 1 | <1% |
| Unknown | 127 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 36 | 27% |
| Researcher | 25 | 19% |
| Student > Bachelor | 14 | 11% |
| Other | 9 | 7% |
| Student > Doctoral Student | 9 | 7% |
| Other | 22 | 17% |
| Unknown | 18 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 28 | 21% |
| Agricultural and Biological Sciences | 23 | 17% |
| Chemistry | 13 | 10% |
| Computer Science | 11 | 8% |
| Medicine and Dentistry | 7 | 5% |
| Other | 26 | 20% |
| Unknown | 25 | 19% |