groups with which to share their solutions through the server, allowing them to work together to find even better solutions than they could working alone. Whenone player shares a solution by uploading it to the server, other players in the same group are able to see it and download it. The social aspect of the game is supported by in-game chat, a website with forums, and a player-created wiki. At the close of a puzzle, the solution data is aggregated, and presented to the scientists for analysis.
The game is designed to be flexible, and the client allows automatic updating so that we can continually evolve the gameplay. The puzzle posting cycle and automatic updates allow us to respond to not only player feedback, but also to scientists’ analysis, as we introduce and refine gameplay elements.
Figure 3.2 Overview of architecture for scientific discovery games. The biochemistry team provides structure prediction and design problems for the server. These problems become puzzles and are sent to each player’s client. Players collaborate and compete to solve these problems and upload their solutions to the server, where they are aggregated and sent back to the biochemistry team for analysis. This analysis can then be used to improve the design of the game and puzzles. (Figure from Cooper et al. [2010b])
Foldit is built on top of the Rosetta molecular modeling suite which has proven useful at a wide variety of protein modeling tasks [Rohl et al. 2004, Bradley et al. 2005, Qian et al. 2007, Kuhlman et al. 2003]. The suite contains an energy function which captures the interaction energies between protein elements, as well as a set of structural optimization subroutines. For protein structure prediction, structures closer to the native structure will have a lower energy than structures further away from it. Foldit uses this state-of-the-art energy function to compute player’s scores, and also takes advantage of the optimization routines Rosetta makes available.
3.3.2 Coevolution Strategy
In order to arrive at the current state of Foldit, we took an coevolution approach to the game’s design. Given the complexity of this undertaking, werealized that it was unlikely that all our initial decisions would be the best. There are three major groups relevant to our approach: (1) the scientists whose problems the game is meant to help solve; (2) the players; and (3) the game development team. The development team must incorporate feedback from the players to make sure the game is understandable and fun, and from the scientists to make sure that the results produced will be useful to them. Anoverview of the interactions between these three groups is given in Figure 3.3.
Figure 3.3 Overview of the interactions between the three iterative design groups. (Figure from Cooper et al. [2010b])
During the game’s initial development, the development team and scientists must work together closely to determine an initial direction. This involves defining what problems to approach, what the fundamental gameplay mechanics needed are, and what the desired results are. Once possible games have been prototyped, player feedback can begin to be incorporated. Early playtesting helps to uncover what elements of the problem are fun and which can be most confusing and difficult to understand. This can help to both focus the gameplay and narrow the scope of the game to where players will most likely be able to contribute.
After making the game available to the public, a large amount of data and feedback can become available to help improve the game. As in a traditional game, data on gameplay can be gathered from players for an objective analysis of what players are doing, and feedback from the player community is extremely useful in determining new features. However, in a scientific discovery game, as scientists post puzzles and player solutions are analyzed, this analysis must then be incorporated in the design of the game, progressing towards ever better results.
Following this pattern, Foldit has evolved significantly since its initial release. A timeline of significant events in the evolution of the game are given in Figure 3.4.
Figure 3.4 Selected events from the game’s evolution over time. The timeline is shown on the top. Screenshots are included from before release (bottom left) and the current version (bottom right). (Figure from Cooper et al. [2010b])
3.3.3 Categorization as a Game
Although it relies heavily on simulation and visualization, Foldit can be classified as a game, as it possesses the qualities of a game set forth by Schell [Morgan Kaufmann]. Here we list the qualities and how Foldit embodies each.
1.Games are entered willfully: We do not require players to play Foldit.
2.Games have goals: Foldit’s goal is to find the best scoring structure.
3.Games have conflict: Foldit has conflict with both the protein itself, trying to find a better score, and with other players, trying to outrank them.
4.Games have rules: The rules of Foldit are given by the scoring function, available moves, global point structure, and so forth.
5.Games can be won and lost: Each puzzle has a ranking, which could be broken down into “winners” and “losers”.
6.Games are interactive: Foldit allows players to interactively reshape a protein and gives them immediate feedback.
7.Games have challenge: Similar to conflict, Foldit’s challenge arises from achieving higher scores and competing with other players.
8.Games create their own internal value: Foldit’s global points have value for ranking within the game.
9.Games engage players: Foldit keeps players engaged in manipulating protein structures.
10.Games are closed, formal systems: Foldit’s rules define the pieces of the system and how they work together.
3.4 Game Design Challenges
3.4.1 Visualizations
While a user is playing Foldit, several visualizations are available. These help the player determine when they are or aren’t doing well, and show which areas of the protein they could improve and what is wrong with them, so the player can think about how to fix any problems. Figure 3.5 shows a screenshot of the game’s main screen. We intend for the game to look like a game and not necessarily a scientific illustration. While scientific illustration techniques are useful for scientists, they may not be for our purposes, and may in fact be intimidating for non-scientists. Many of the visualizations have options, or can be turned off and on by the player. They include the following.
The protein. The protein itself is rendered in a cartoon-like style. This style is abstract and does not show the exact positions of all the atoms in the protein. The helices, sheets, and loops appear differently along the backbone, and sidechains are rendered very simply. The protein is colored by the score of each residue.
Clashes. These are red flashing spiky balls. They appear where two atoms are too close together, which will severely reduce the score.
