5.3.2 Foldit

Foldit is a beautiful open call because it enables non-experts to participate in a way that is fun.

The Netflix Prize, while evocative and clear, does not illustrate the full range of open call projects. For example, in the Netflix Prize most of the serious participants had years of training in statistics and machine learning. But, open call projects can also involve participants who have no formal training, as was illustrated by Foldit, a protein folding game.

Protein folding is the process through which a chain of amino acids takes on its shape. With a better understanding of this process, biologists could design proteins with specific shapes that could be used as medicine. Simplifying quite a bit, proteins tend to move to their lowest energy configuration, a configuration that balances the various pushes and pulls within the protein (Figure 5.7). So, if a researcher wants to predict what shape into which a protein will fold, the solution sounds simple: just try all possible configurations, calculate their energies, and predict that the protein will fold into the lowest energy configuration. Unfortunately, this brute force approach that involves trying all possible configurations is computationally impossible because there are billions and billions of potential configurations. Even with the most powerful computers available today—and in the foreseeable future—brute force is just not going to work. Therefore, biologists have developed many clever algorithms to efficiently search for the lowest energy configuration. But, despite massive amounts of scientific and computational effort, these algorithms are still far from perfect.

Figure 5.7: Protein folding. Image was created and placed into public domain by DrKjaergaard. Source: Wikimedia Commons.

Figure 5.7: Protein folding. Image was created and placed into public domain by “DrKjaergaard”. Source: Wikimedia Commons.

David Baker and his research group at the University of Washington were part of the community of scientists working to develop better computational approaches to protein folding. In order to track what was happening while their algorithms were cranking away, Baker and his group would occasionally watch a screen-saver that visualized the progress of their algorithms. While watching these visualizations, Baker began to wonder whether it would be possible for humans to help in the process, and thus began Foldit, a creative and beautiful open call (Hand 2010).

Foldit turns the process of protein folding into a game that can be played by anyone. From the perspective of the player, Foldit appears to be a puzzle (Figure 5.8). Players are presented with a three-dimensional tangle of protein structure and can perform operations—“tweak”, “wiggle”, “rebuild”—that change its shape. By performing these operations players change the shape of the protein, which in turn increases or decreases their score. Critically, the score is calculated based on the energy-level of the current configuration; lower-energy configurations result in higher scores. In other words, the score helps guide the players as they search for low-energy configurations. This game is only possible because—just like predicting movie ratings in the Netflix Prize—protein folding is also a situation where it is easier to check solutions than generate them.

Figure 5.8: Game screen for Foldit.

Figure 5.8: Game screen for Foldit.

Foldit’s elegant design enables players with little formal knowledge of biochemistry to compete with the best algorithms designed by experts. While most players are not particularly good at the task, there are a few players and small teams of players who are exceptional. In fact, in a head-to-head competition to predict the structure of 10 specific proteins, Foldit players were able to beat state-of-the-art protein folding algorithms five times (Cooper et al. 2010).

Foldit and the Netflix prize are different in many ways, but they both involve open calls for solutions that are easier to check than generate. Now, we will see the same structure in yet another very different setting: patent law. This final example of an open call problem shows that they can also be used in settings that are not obviously amenable to quantification.