We have recently completed a full re-architecturing of the Rosetta molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy to use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as Rosetta3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This document describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.

People exert significant amounts of problem solving effort playing computer games. Simple image- and text-recognition tasks have been successfully crowd-sourced through gamesi, ii, iii, but it is not clear if more complex scientific problems can be similarly 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 methodologyiv, while they compete and collaborate to optimize the computed energy. We show that top Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only 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.

The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Despite its broad modeling capabilities, Rosetta remains consistently among leading software suites when compared to other methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of over 60 laboratories, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss methods developed in the last five years in Rosetta, involving the latest protocols for structure prediction; protein-protein and protein-small molecule docking; protein structure and interface design; loop modeling; the incorporation of various types of experimental data; modeling of peptides, antibodies and proteins in the immune system, nucleic acids, non-standard chemistries, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the software. Rosetta is available at www.rosettacommons.org.

Foldit is a multiplayer online game in which players collaborate and compete to create accurate protein structure models. For specific hard problems, Foldit player solutions can in some cases outperform state-of-the-art computational methods. However, very little is known about how collaborative gameplay produces these results and whether Foldit player strategies can be formalized and structured so that they can be used by computers. To determine whether high performing player strategies could be collectively codified, we augmented the Foldit gameplay mechanics with tools for players to encode their folding strategies as "recipes" and to share their recipes with other players, who are able to further modify and redistribute them. Here we describe the rapid social evolution of player-developed folding algorithms that took place in the year following the introduction of these tools. Players developed over 5,400 different recipes, both by creating new algorithms and by modifying and recombining successful recipes developed by other players. The most successful recipes rapidly spread through the Foldit player population, and two of the recipes became particularly dominant. Examination of the algorithms encoded in these two recipes revealed a striking similarity to an unpublished algorithm developed by scientists over the same period. Benchmark calculations show that the new algorithm independently discovered by scientists and by Foldit players outperforms previously published methods. Thus, online scientific game frameworks have the potential not only to solve hard scientific problems, but also to discover and formalize effective new strategies and algorithms.citizen science | crowd-sourcing | optimization | structure prediction | strategy C itizen science is an approach to leveraging natural human abilities for scientific purposes. Most such efforts involve visual tasks such as tagging images or locating image features (1-3). In contrast, Foldit is a multiplayer online scientific discovery game, in which players become highly skilled at creating accurate protein structure models through extended game play (4, 5). Foldit recruits online gamers to optimize the computed Rosetta energy using human spatial problem-solving skills. Players manipulate protein structures with a palette of interactive tools and manipulations. Through their interactive exploration Foldit players also utilize user-friendly versions of algorithms from the Rosetta structure prediction methodology (6) such as wiggle (gradient-based energy minimization) and shake (combinatorial side chain rotamer packing). The potential of gamers to solve more complex scientific problems was recently highlighted by the solution of a long-standing protein structure determination problem by Foldit players (7).One of the key strengths of game-based human problem exploration is the human ability to search over the space of possible strategies and adapt those strategies to the type of problem and stage of problem solving (5). The variability of tactics and strategies s...

We present a real-time crowd model based on continuum dynamics. In our model, a dynamic potential field simultaneously integrates global navigation with moving obstacles such as other people, efficiently solving for the motion of large crowds without the need for explicit collision avoidance. Simulations created with our system run at interactive rates, demonstrate smooth flow under a variety of conditions, and naturally exhibit emergent phenomena that have been observed in real crowds.

Following the failure of a wide range of attempts to solve the crystal structure of M-PMV retroviral protease by molecular replacement, we challenged players of the protein folding game Foldit to produce accurate models of the protein. Remarkably, Foldit players were able to generate models of sufficient quality for successful molecular replacement and subsequent structure determination. The refined structure provides new insights for the design of antiretroviral drugs.

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Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have been 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 with 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 which encode them in silico. 146 Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed in E. coli with good solubility 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 contributing 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.