The Foldit cryo-EM paper
The latest Foldit research paper, about Cryo-EM Density puzzles, was published today in the journal PLOS Biology! The paper is open-access, meaning that anybody can read and share it for free, from the journal website.
The paper is a formal research article, so it is written in technical language meant for other scientists, and skips over some background info. Below, we cover the main points so that everyone can appreciate this accomplishment by Foldit players!
Electron density in Foldit
The paper is about recent Foldit puzzles in the Electron Density category, where players fold the target protein into a 3D “cloud” of density that maps the shape of the folded protein. The paper reports solutions from Puzzles 1572, 1588, 1598, and 1606.
Foldit Puzzle 1598: Cryo-EM Freestyle with Density
This is not the first time Foldit players have wowed us in an electron density puzzle! Some of you may remember Puzzle 1152: Foldit vs. UMich Electron Density Challenge from back in 2015. In that contest, players built solutions into a high-resolution (1.9 Å) density map from x-ray diffraction experiments. Foldit players outperformed UMich undergraduates, expert crystallographers, and state-of-the-art computer algorithms! Those results were published in a previous paper.
This previous result gave us a clue that electron density might be a sweet spot for Foldit players, so we started to look at other kinds of density maps...
Cryo-electron microscopy (cryo-EM) is another technique for getting density maps and solving protein structures. In a cryo-EM experiment, a sample of protein in solution is spread on a thin metal wafer and quickly cooled to cryogenic temperatures to quench all molecular motion, freezing all of the protein atoms in a sheet of vitreous ice. Then we bombard the frozen sample with a beam of high-energy electrons, which scatter when they collide with the atoms of the protein. A detector measures the electron scattering, and the result is a grainy 2D “micrograph” of the wafer and any proteins on its surface.
Example cryo-EM micrograph of the S. entomophila antifeeding prophage, used to generate the maps for the puzzles in this paper. Used with permission of Ambroise Desfosses and Irina Gutsche (source).
If we collect enough of these raw micrographs (think millions), then we can align all of the individual protein molecules and average them together to get a clearer 2D picture of the protein. Finally, we combine all the 2D images to arrive at a 3D reconstruction of the protein, in the form of a density cloud—very similar to the electron density clouds that we get from x-ray diffraction experiments!
Unlike x-ray diffraction, cryo-EM experiments are fairly easy to set up (no protein crystals needed!). But cryo-EM has been unpopular for protein structure research because it yields a lower-resolution, “blobbier” density cloud than x-ray diffraction. However, that started to change around 2012, when a technological breakthrough gave us improved electron-scattering detectors and higher resolution maps. Since then, cryo-EM has taken off, and the number of new cryo-EM protein structures has been doubling every 2 years (by contrast, new x-ray diffraction structures have plateaued since 2013).
Cryo-EM and Foldit players
Even with the recent improvements, cryo-EM maps are not quite as clear as x-ray diffraction maps. The highest resolution typically achieved by cryo-EM is about 3.0 Å. Since covalently bonded atoms are separated by < 2 Å, that means we still can’t make out the positions of individual atoms simply by looking at the map. Instead, we have to infer the positions of the atoms, using our knowledge of physics and protein structure to find a plausible model that fits the map.
Building a plausible protein structure into a low-resolution map is difficult and prone to errors. If a microscopist focuses too much on fitting the density cloud, they might end up with a strained (high energy) model that is physically unrealistic. On the other hand, a computer algorithm that optimizes energy can have a hard time fitting a model into the density map.
This is where Foldit players come in! We know from previous work that Foldit players are adept at interpreting density maps; and the Foldit score function should help guide players toward plausible, low energy models.
In Puzzles 1572, 1588, 1598, and 1606, we provided Foldit players with cryo-EM maps for four proteins that make up the S. entomophila antifeeding prophage (a complex needle-like structure used by bacteria to inject toxins into a target cell). We then compared Foldit player solutions with those of expert microscopists and a handful of automated algorithms.
Comparison of solutions from different methods. (Top) The top Foldit solution from Puzzle 1588 and the model built by the scientist. They look pretty similar when you look this zoomed out, but looking closer: (Bottom) Subtle deviations in the models can yield significant results. In the bottom-right image, an automated algorithm (magenta) had trouble matching the density, and left some regions of the map completely empty.
Foldit players take gold!
In each of the four puzzles, Foldit player solutions had the best balance of plausibility and fit-to-density! If you’re curious, the scientists came in second, and the algorithms came in last (but there was a lot of variance between different algorithms).
Foldit players achieve plausibility and high fit-to-density for AFP7 (Puzzle 1588). (Left) Microscopists build strained models that have many clashes. (Right) Automatic algorithms like Rosetta and Phenix build models with poor fit-to-density (according to three different measures of map correlation). Foldit players build realistic models with few clashes, and still fit the density with a high map correlation.
We also want to point out that the Foldit rankings were incredibly accurate in these puzzles! As most players are aware, the best-scoring solution in Foldit is not necessarily the most accurate scientifically (because the Foldit score function is not a perfect reflection of reality). This is why we run our scientific analysis on all of the high-scoring solutions, to see what actually looks best against the scientific data: sometimes it’s rank #2, and sometimes it’s rank #20. However, in all four of these cryo-EM puzzles, the #1 top-scoring Foldit solution also had the best scientific evaluation!
This is important because it supports the accuracy of the Foldit score function. Foldit players can have more confidence that when their score goes up, so does the scientific value of their solution. It should also give more confidence to other scientists that might want to collaborate with Foldit players in the future. We hope this is just the beginning for Foldit cryo-EM!
Finally, we want to thank all the Foldit players that participated in these cryo-EM puzzles! Even if you didn’t work directly on the models presented in the paper, your folding helps to drive the competition that leads to high-scoring solutions. We love to see Foldit players continuing to share ideas and set high standards for each other! Some of the Foldit players who worked on the solutions in the paper have written up their folding strategies, which you can read in the paper supplement.( Posted by bkoep 118 1385 | Mon, 11/11/2019 - 19:29 | 6 comments )