WeFold paper on CASP11 has just been accepted!
The paper is titled: "An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12"
and just as with the first WeFold paper about CASP10, Foldit played a very large part in CASP11!
In order to publish these results, however, we must now abide by the new authorship policy that journals have now implemented requiring author names and affiliations for all authors. Previously, we had used "Foldit Players" (or Players, F.) to represent all of you.
Similar to Foldit's previous publication in Nature Communications, for this paper you will all be under the group consortium: "Foldit Players", and anyone who played a CASP11 puzzle has the option to list their complete real name (we cannot use Foldit usernames).
If you played one of the CASP11 puzzles and would like your full real name to be included in the group consortium list for this paper, please follow the directions below in the comments by Saturday June 2nd at 11:59pm GMT
We would like to emphasize that this is completely voluntary, as we will of course also have a statement in the acknowledgements thanking all Foldit players, just not by name.( Posted by beta_helix 73 1765 | Thu, 05/24/2018 - 02:35 | 6 comments )
Foldit's 10 Year Anniversary!
Today marks the 10-year anniversary of Foldit’s launch on May 9, 2008!
In the past decade, Foldit players have advanced protein science by accurately predicting the structure of a viral protein1, by developing an algorithm for protein modeling2, and by redesigning a protein enzyme with improved activity3. Foldit players have shown that they can refine protein models better than sophisticated computer programs4, and that they can interpret electron density maps as well as expert crystallographers5. We have high hopes for the next 10 years of Foldit, and can't wait to see what Foldit players will discover next!
Protein Design in Foldit
Most recently, Foldit players have been designing brand new proteins from scratch. The ability to design proteins is a big milestone for Foldit players, and we’re excited about the new types of problems that we can start to tackle with protein design in Foldit! This achievement has been a long time in the making—below you can review previous blog posts to follow this progress over the last four years. Play the latest design puzzle now!
Nov. 1, 2013 - First batch of Foldit player-designed proteins selected for testing
Mar. 25, 2014 - Improvements in Foldit player-designed proteins
Jun. 18, 2014 - First positive testing results for a Foldit player-designed protein
Feb. 10, 2015 - First alpha/beta Foldit designs selected for testing
Feb. 28, 2017 - Better backbones yield promising alpha/beta designs
Mar. 1, 2017 - Diverse player designs fold up in the wet lab
Apr. 15, 2017 - Protein crystallography of a Foldit player design
May 30, 2017 - X-ray diffraction of a protein crystal
A high-resolution crystal structure (cyan) aligned with the design model (green) shows that this protein folds up just as it was designed by Waya, Galaxie, and Susume. The protein backbone aligns to the design with a Cα RMSD of 1.1 Å, and the sidechains in the protein core pack just as intended.
Small Molecule Design in Foldit
We’re also excited to ramp up small-molecule design in Foldit, allowing Foldit players to create new ligands that could bind to protein targets! Play the latest small-molecule design puzzle now!
New tools allow Foldit players to build small molecules that can bind to protein targets
We'd like to thank all the Foldit players that have contributed to Foldit over the last 10 years! None of this would have been possible without you! Happy folding!
1. Khatib, F. et al. Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nat Struct Mol Biol 18, 1175–1177 (2011).
2. Khatib, F. F. et al. Algorithm discovery by protein folding game players. Proc Natl Acad Sci U S A 108, 18949–18953 (2011).
3. Eiben, C. B. et al. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nature Biotechnology 30, 190–192 (2012).
4. Cooper, S. et al. Predicting protein structures with a multiplayer online game. Nature 466, 756–760 (2010).
5. Horowitz, S. et al. Determining crystal structures through crowdsourcing and coursework. Nat Commun 7, 12549 (2016).
New Update in 'Experimental' Update Group!
As many of you are probably aware, Rosetta is the "science engine" behind Foldit. It's been about two years since we last updated the version of Rosetta that Foldit uses under the hood. Since then, there have been many developments and improvements in Rosetta, and we thought it's time to update again.
Here's just a sampling of some of the improvements that come with the updated Rosetta:
* Fixes to some of the random crashes on MacOS
* Fixes to electron density-related crashes on Linux
* Fixes to symmetry-related crashes on all platforms
* Better support for non-protein residues, such as RNA, carbohydrates, lipids and non-canonical amino acids
* Support for modeling membrane proteins
* Improved detection of native-like hydrogen bonding networks
But perhaps the biggest change that comes with the update is improvements to scoring. There's been a *lot* of work put into the Rosetta scoring function recently, and just about every portion of scoring has been re-evaluated and re-optimized. (For those who want a nitty-gritty breakdown of the changes, a comprehensive overview has been published, along with details on how things were optimized.)
Here's a demonstration of the improvement. When looking at the ability to discriminate native-like proteins from non-native-like ones, for many proteins the new scoring function is able to do a much better job than the older scoring function:
Here each red point represents a structure prediction run for a different protein. The discrimination ability of the two scoring functions are plotted, using a metric where 0.0 represents no discrimination between native-like and non-native, and 1.0 represents ideal discrimination. The diagonal line represents no difference between the two scoring functions, and any points above the diagonal line represent proteins where the new scoring function does a better job than the old one.
This also is reflected in the score-versus-rmsd "funnel" plots for the predictions, where the new scoring function does a better job of eliminating false minima (blue) than the older score function does. (In these plots, better scoring structures are lower on the y-axis, and more native-like structures are further to the left. Eliminating false minima means a selection of top-scoring structures is less likely to include non-native-like ones.)
This improvement isn't limited only to protein structure prediction. The new scoring function shows discrimination improvements in a wide range of protein prediction problems, including protein-protein and protein-small-molecule interaction predictions.
There is a slight drawback to these improvements, though. The new scoring function is slower than the current one. (In our tests, it averages about 30% slower.) We don't anticipate this being noticeable in general interactive use, but it may affect things like long-running shakes and wiggles. Most affected will be scripts which use a set number of iterations of shake and wiggle - these will run for longer, and if you've optimized the number of iterations for the current scoring function, the optimal number of iterations may have changed in the new one.
We're excited about these score function improvements, though, and think the better results are worth the slowdown. You might spend a bit more time working on a single structure, but you should hopefully spend much less time working on "bad" or "scientifically uninteresting" structures.
So, if you're feeling adventurous, please help us out by testing the updated version. To do this, switch your update group to `experimental`. In addition to testing how the slowdown affects scripts, we also want to make sure no bugs have slipped in on how the various tools behave. -- Note that, due to the difference in scoring, the puzzles available with `experimental` are not the same as with the `main` and `devprev` clients. If you want to play the regular Foldit puzzles, you'll need to switch your update group back to 'main'. None of the 'experimental' puzzles will count towards your website rank, but they will help us work out any issues prior to releasing it to the general public!( Posted by rmoretti 73 840 | Thu, 02/08/2018 - 21:42 | 3 comments )
Aflatoxin: a cancer-causing glue
In our previous blog post, we announced our Aflatoxin Challenge – a new series of Foldit puzzles designed to tackle a common poison known as aflatoxin. This week, Baker Lab scientist ianh offers a more detailed picture of the chemistry behind aflatoxin's harmful effects.
Most people on Earth consume aflatoxins every day. Aflatoxins are compounds produced by certain fungi that can grow in or on almost all grains and groundnuts. Aflatoxins are known hepatocarcinogens, meaning they cause liver cancer in high doses. Liver cancer is the third leading cause of cancer death globally, with 83% of cases occurring in East Asia and sub-Saharan Africa where aflatoxin exposure is highest.
What makes aflatoxin so toxic?
Surprisingly, aflatoxin itself isn’t toxic. Once ingested, our body uses its normal metabolic processes to try to break it down. It turns out a metabolic product of aflatoxin – not aflatoxin itself – is harmful, and the chemistry behind its toxicity is both frightening and familiar.
One of the metabolic enzymes that acts on aflatoxin is CYP3A4, a vitally import liver enzyme tasked with breaking down different complex molecules. Its normal targets are molecules produced by our own bodies, like the sex hormones testosterone and estrogen, but CYP3A4 can also safely chew up some of the complex chemicals we put in our body, such as caffeine (found in coffee and tea) and lidocaine (a local anesthetic commonly used by dentists).
When CYP3A4 metabolizes a chemical it changes that molecule’s structure in some way. When it acts on aflatoxin, it adds a chemical feature – chemists call this new feature an epoxide group.
Epoxide groups are highly reactive, meaning they are unstable on their own and like to bond with other nearby chemicals. This extreme reactivity can be harnessed for many practical applications, including adhesion, electrical insulation, and industrial manufacturing. Some of the strongest glues ever made – epoxy glues – are based on epoxide chemistry.
Once CYP3A4 converts aflatoxin into epoxy-aflatoxin, the compound doesn’t wait around for long. It quickly reacts with other chemicals in our cells, especially amines.
One of the best places to find amines in our cells in our DNA. Each of the four letters of DNA – A, T, G, and C – is an amine. Epoxy-aflatoxin reacts especially strongly with guanosine (G), forming a nearly-unbreakable bond. This permanently damages DNA.
DNA damage is problematic, not only because it interferes with the natural processes of the damaged cell, but because this damage can be passed on to descendants when the damaged cell replicates. In this way, a single error in a single cell may be amplified over time to affect a large population of cells.
The liver is especially prone to this cascade because it (1) expresses high levels of the CYP3A4 protein that metabolizes aflatoxin, and (2) because liver cells reproduce at faster rates than cells of other organs, allowing DNA mutations to accumulate and propagate much more quickly.
Human DNA includes a gene that encodes for the tumor-suppressing protein p53. The normal role of the p53 protein is to police DNA damage in the cell. It recognizes DNA damage and can initiate DNA repair processes or, in extreme cases, induce cell death. Without functional p53 protein, DNA damage runs rampant in a cell. Dysfunctional p53 is strongly associated with many forms of cancer in humans.
It seems that one particular guanosine in the p53 gene is especially susceptible to aflatoxin damage. Damage to this guanosine is propagated to descendant cells as a G -> T mutation in the p53 gene; the G -> T mutation in the p53 gene results in a ARG -> SER mutation in the p53 protein. The p53 protein falls victim to the very type of DNA damage it is meant to avert! This mutated version of p53 is exceedingly common in liver cancer patients.
In summary: aflatoxin causes DNA damage in the liver, resulting in a population of cells with defective p53 and prompting tumor growth.
Help us design a protein to break down this toxic compound by playing the latest Foldit puzzle, 1445: Aflatoxin Challenge: Round 2 with Insertions!( Posted by ianh 73 1765 | Tue, 10/31/2017 - 17:26 | 1 comment )