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. 2016 Sep 16:7:12549.
doi: 10.1038/ncomms12549.

Determining crystal structures through crowdsourcing and coursework

Scott Horowitz  1   2 , Brian Koepnick  3 , Raoul Martin  1   4 , Agnes Tymieniecki  1   2 , Amanda A Winburn  5   6 , Seth Cooper  7 , Jeff Flatten  8 , David S Rogawski  9 , Nicole M Koropatkin  10 , Tsinatkeab T Hailu  1   11 , Neha Jain  1 , Philipp Koldewey  1   2 , Logan S Ahlstrom  1   2 , Matthew R Chapman  1 , Andrew P Sikkema  12 , Meredith A Skiba  12 , Finn P Maloney  13 , Felix R M Beinlich  1   14 ; Foldit Players; University of Michigan students; Zoran Popović  8 , David Baker  3   15   16 , Firas Khatib  17 , James C A Bardwell  1   2
Collaborators, Affiliations

Determining crystal structures through crowdsourcing and coursework

Scott Horowitz et al. Nat Commun. .

Erratum in

  • Corrigendum: Determining crystal structures through crowdsourcing and coursework.
    Horowitz S, Koepnick B, Martin R, Tymieniecki A, Winburn AA, Cooper S, Flatten J, Rogawski DS, Koropatkin NM, Hailu TT, Jain N, Koldewey P, Ahlstrom LS, Chapman MR, Sikkema AP, Skiba MA, Maloney FP, Beinlich FR; Foldit Players; University of Michigan students; Popović Z, Baker D, Khatib F, Bardwell JC. Horowitz S, et al. Nat Commun. 2016 Oct 25;7:13392. doi: 10.1038/ncomms13392. Nat Commun. 2016. PMID: 27779204 Free PMC article. No abstract available.

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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Figures

Figure 1
Figure 1. Snapshots of Foldit players building YPL067C in the Foldit user interface.
For the complete path of the Foldit model building, see Supplementary Movie 1. The starting point for the puzzle (top left) presented the electron density map and the protein sequence to the player. The players then used Trp108 to help anchor the sequence in electron density (top middle) before beginning to fold secondary structure elements (top right through bottom left). After many rounds of modification in Foldit (bottom middle and Supplementary Movie 1), the players arrived at a high-scoring solution in which the ordered regions of electron density were well fit by YPL067C (bottom right). Disordered regions were later pruned.
Figure 2
Figure 2. Model-building competition results.
Comparison of key statistics of the best model from each group after pruning disordered residues from Foldit structures. In all cases, lower values represent better scores. Comparison before pruning disordered residues is shown in Supplementary Fig. 4.
Figure 3
Figure 3. Overall structure of HTC1.
Structural alignment with the top DALI search hit is shown in Supplementary Fig. 7.
Figure 4
Figure 4. HTC1 aggregation inhibition.
HTC1 prevents amyloid formation of RCMLa (a) Aβ1–40 (b) and α-synuclein (c), as measured by thioflavin T (ThT) fluorescence at 490 nm.
See this image and copyright information in PMC

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