Make science into a game, and you’ll not only entertain thousands of people, but may also solve some of the toughest problems in your field.
Algorithm discovery by protein folding game players Firas Khatiba, Seth Cooperb, Michael D. Tykaa, Kefan Xub, Ilya Makedonb,Zoran Popovićb, David Bakera,c,1, and Foldit Players 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 Examination of the algorithms encoded in these two recipes revealed a striking similarity to an unpublished algorithm developed by scientists over the same period.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.
The shape of proteins is a really useful thing to know. If you are in drug research, it can help you find molecules that can manipulate a protein to kill a pathogen or repair a fault in a patient’s body. If you study evolution, it can help you find deep relationships among proteins that sequence similarities no longer preserve, and understand how their intricate workings evolved.
Traditionally, there have been two good ways of determining the structure of a protein. One is by purifying and crystallising it, and shooting X-rays at the crystals. Obviously, that only works with proteins that can be purified and crystallised, which is not all of them. The other is homology-based prediction – basically comparison with a related protein of known structure, which obviously requires a similar enough protein with a known structure.
Predicting the structure of a protein from its amino acid sequence is fiendishly difficult, but that’s what the folks behind Rosetta and Foldit are trying to do. Nowadays, isolating and sequencing the gene that codes for your protein of interest is relatively easy (sequencing the protein itself is tougher). If all you had to do to accurately know its structure was to plug the sequence into a program and wait a few minutes, that would make the life of many people much easier.
(I briefly played Foldit, but I admit I jut got lost and frustrated and gave up. However, I encourage everyone with an actual attention span to give it a go and see if it’s for you.)