Increased Diels-Alderase Activity through Foldit Player Guided Backbone Remodeling

Department of Biochemistry, University of Washington, Seattle, Washington, USA.
Nature Biotechnology (Impact Factor: 41.51). 01/2012; 30(2):190-2. DOI: 10.1038/nbt.2109
Source: PubMed


Computational enzyme design holds promise for the production of renewable fuels, drugs and chemicals. De novo enzyme design has generated catalysts for several reactions, but with lower catalytic efficiencies than naturally occurring enzymes. Here we report the use of game-driven crowdsourcing to enhance the activity of a computationally designed enzyme through the functional remodeling of its structure. Players of the online game Foldit were challenged to remodel the backbone of a computationally designed bimolecular Diels-Alderase to enable additional interactions with substrates. Several iterations of design and characterization generated a 24-residue helix-turn-helix motif, including a 13-residue insertion, that increased enzyme activity >18-fold. X-ray crystallography showed that the large insertion adopts a helix-turn-helix structure positioned as in the Foldit model. These results demonstrate that human creativity can extend beyond the macroscopic challenges encountered in everyday life to molecular-scale design problems.

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    • "GAME PLAY: DISSEMINATING THE RESULTS BY INSCRIPTION The classic Latourian inscription produced by projects such as Foldit, of course, is a scholarly research article that takes its place among others, a potentially powerful actant in a larger network that produces and reproduces academic power, position, and expertise. At the time of this writing, Foldit's designers have published seven major articles, seven inscriptions, in journals with interdisciplinary influence, some of which have great influence: Nature, Nature: Biotechnology, the Proceedings of the National Academy of Sciences (PNAS), and Nature: Structural & Molecular Biology (Eiben et al., 2012; Khatib et al., 2011; Khatib et al., 2011; Cooper et al., 2011) as well as being featured in a presentation at a digital gaming studies conference (Cooper et al., 2011). The primary authors of these publications are members of the Baker Lab. "
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    ABSTRACT: Protein folding is an important area of research in bioinformatics and molecular biology. The process and product of protein folding concerns how proteins achieve their functional state. A particularly difficult area of protein folding is protein structure prediction. There are many possible ways a protein can fold, and this makes prediction difficult, even with the aid of computational approaches. Protein folding prediction requires significant human attention. Foldit, an online science game, provides an innovative approach to the problem by enlisting human beings to solve puzzles that correlate with protein folding possibilities. Such work aligns broadly with emerging trends in citizen science, where non-experts are enlisted for productive alliances. We examine Foldit, commonly looked at as a dynamic community, and suggest such communities actually have potential to be relatively static and to reproduce and maintain a set of power relations. We make this argument by combining perspectives from Rhetorical Genre Studies and Actor-Network Theory.
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    • "This approach especially benefits scientists who might not have enough funding or time to create data, or in cases where the amounts of data are too large to be analyzed by researchers alone. Galaxy Zoo and FoldIt [17], [18] are two of the best known examples. Galaxy Zoo enables amateur astronomers to walk through telescopic images to categorize the shown objects at a rate which could not have been matched by the efforts of professional astronomers. "
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    ABSTRACT: Genome-Wide Association Studies are widely used to correlate phenotypic traits with genetic variants. These studies usually compare the genetic variation between two groups to single out certain Single Nucleotide Polymorphisms (SNPs) that are linked to a phenotypic variation in one of the groups. However, it is necessary to have a large enough sample size to find statistically significant correlations. Direct-To-Consumer (DTC) genetic testing can supply additional data: DTC-companies offer the analysis of a large amount of SNPs for an individual at low cost without the need to consult a physician or geneticist. Over 100,000 people have already been genotyped through Direct-To-Consumer genetic testing companies. However, this data is not public for a variety of reasons and thus cannot be used in research. It seems reasonable to create a central open data repository for such data. Here we present the web platform openSNP, an open database which allows participants of Direct-To-Consumer genetic testing to publish their genetic data at no cost along with phenotypic information. Through this crowdsourced effort of collecting genetic and phenotypic information, openSNP has become a resource for a wide area of studies, including Genome-Wide Association Studies. openSNP is hosted at, and the code is released under MIT-license at
    PLoS ONE 03/2014; 9(3):e89204. DOI:10.1371/journal.pone.0089204 · 3.23 Impact Factor
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    • "[1] In the biological sciences it has shown great potential in the determination of protein folding structure which has limited feasibility with conventional computational approaches. [2] In healthcare, crowdsourcing has been used in drug discovery, analysis of imaging, clinical diagnosis and to improve service efficiency [3]–[6]. "
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    ABSTRACT: Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis.
    PLoS ONE 08/2013; 8(8):e71154. DOI:10.1371/journal.pone.0071154 · 3.23 Impact Factor
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