Man vs. machine: protein folding
Protein structure prediction is an important problem in order to develop new treatments against diseases. The structure determines how the protein works to a large extent and thus being able to predict the structure is essential in order to develop new drugs (see fold.it):
Protein structure prediction: As described above, knowing the structure of a protein is key to understanding how it works and to targeting it with drugs. A small proteins can consist of 100 amino acids, while some human proteins can be huge (1000 amino acids). The number of different ways even a small protein can fold is astronomical because there are so many degrees of freedom. Figuring out which of the many, many possible structures is the best one is regarded as one of the hardest problems in biology today and current methods take a lot of money and time, even for computers. Foldit attempts to predict the structure of a protein by taking advantage of humans’ puzzle-solving intuitions and having people play competitively to fold the best proteins.
Unfortunately, from a computational point of view, structure prediction is a tough problem. The problem has a significant 3D geometrical component which makes it accessible for humans though. Exploiting this fact is what fold.it aims for. It is a game that has humans fold proteins presented as puzzles. The goals of fold.it are:
For protein structure prediction, the eventual goal is to have human folders work on proteins that do not have a known structure. This would require first attracting the attention of scientists and biotech companies and convincing them that the process is effective. Another goal is to take folding strategies that human players have come up with while playing the game, and automate these strategies to make protein-prediction software more effective. These two goals are more or less independent and either or both may happen.
The more interesting goal for Foldit, perhaps, is not in protein prediction but protein design. Designing new proteins may be more directly practical than protein prediction, as the problem you must solve as a protein designer is basically an engineering problem (protein engineering), whether you are trying to disable a virus or scrub carbon dioxide from the atmosphere. It’s also a relatively new field compared to protein prediction. There aren’t a lot of automated approaches to protein design, so Foldit’s human folders will have less competition from the machines.
Wired run an interesting article a few weeks ago about fold.it and its performance in the CASP (Critical Assessment of Techniques for Protein Structure Prediction) competition where the rankings of the human folded proteins were pretty competitive. Exploiting human intelligence for certain computations is not completely new. Captchas were probably one of the first applications that exploited the human pattern recognition capabilities in order to differentiate between humans and machines. Amazon is running a whole service “MTurk” which provides an infrastructure to utilize “idling Internet users”. The new aspect here might be the design problem mentioned above which aims for constructing proteins with specific properties by leveraging not only human pattern recognition capabilities but also creativity.