With the rapid improvement of cryo-electron microscopy (cryo-EM) resolution, new computational tools are needed to assist and improve upon atomic model building and refinement options. This communication demonstrates that microscopists can now collaborate with the players of the computer game Foldit to generate high-quality de novo structural models. This development could greatly speed the generation of excellent cryo-EM structures when used in addition to current methods.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson–Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now—starting from an extended polypeptide chain—generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem. Proteins designed de novo by players of the online protein-folding game Foldit can be expressed in Escherichia coli and adopt the designed structure in solution.

The computer game Foldit is currently widely used as a biology and biochemistry teaching aid. Herein, we introduce a new feature of Foldit called "custom contests" that allows educators to create puzzles that fit their curriculum. The effectiveness of the custom contests is demonstrated by the use of five distinct custom contests in an upper-level biochemistry class. The new custom contest feature can be implemented in classes ranging from middle school to graduate school to enable educators to best complement their current curriculum. © 2019 International Union of Biochemistry and Molecular Biology, 47(2): 133-139, 2019.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Summary : Foldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command‐line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real‐time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template‐based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter. Availability and Implementation: Foldit Standalone is available for download at https://fold.it/standalone, under the Rosetta license, which is free for academic and non‐profit users. It is implemented in cross‐platform C ++ and binary executables are available for Windows, macOS and Linux. Contact: scooper@ccs.neu.edu

Nature Communications 7: Article number:12549 (2016); Published 16 September 2016; Updated 25 October 2016 The original version of this Article contained an error in the spelling of a member of the University of Michigan students Consortium, Sam Lee, which was incorrectly given as Sam Lewe. This hasnow been corrected in both the PDF and HTML versions of the Article.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. Proteins 2014; 82:1850–1868. © 2014 Wiley Periodicals, Inc.