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This is the place where we will describe some of the outcomes and results of your folding work, provide a glimpse of future challenges and developments, and in general give you a better sense of where we are and where foldit hopes to go in the future.

Drug Design Update: Tool Talk

Hello everyone!

We are now ready to deploy a series of tools for small molecule drug discovery! Our goal is to release new tools on a rolling basis for the next couple of weeks. Because the tools are still being tested and not guaranteed to be bug free, we have created an “experimental” user group. This user group is open to everyone who wants to test the drug discovery tools; however, we do ask, that you report any bugs that you find or any suggestions that you have for the interface. After all the tools have been tested, we will release the drug discovery tools to Foldit’s main client.

You should expect this build to be experimental. This means that you have a chance of losing your designs. Because of this, it is highly recommend that you keep a separate install outside of your main client. This will ensure that you do not lose your main game puzzle progress.

In order to get access for the experimental group, follow the steps for access to the devprev build. Instead of replacing “main” with “devprev” in the options.txt file, type “experimental”.

"update_group" : "experimental"

The first tool is a very simple tool and is an expansion of the very first puzzle’s tool released for small molecule drug discovery. It is simply named, “Ligand Design Tool”. This tool provides the ability to change the identity of elements, create bonds between atoms, add a predefined set of fragments, delete atoms, and delete bonds. The Ligand Design Tool is available in both the Selection Interface and in the original Foldit interface.

You may want to change your view to the ligand design view. This will let you see the hydrogens on the small molecule, which can be used to extend the ligand by a single atom. You can access this by changing the view options in the advanced options menu.

In addition to releasing the first tool, we have three new tutorials. The tutorials are a work in progress and will be updated when we have some down time. The tutorials are particularly exciting as they follow the progression of a scientific team as they design an inhibitor for the FKB binding protein.

The puzzle that we are using for testing is Dihydrofulate Reductase (DHFR). Read on to get some more information on DHFR!

Finally, here is what to expect in the upcoming weeks. We will have a new blog post for each topic explaining in detail the science behind each tool and the tool's purpose:

Ligand minimization with a new force field, MMFF, and a set of new filters to guide you in designing small molecules, the rule of five filter and a similarity filter. We will have an explanation of all these features later on, but here is a video of the MMFF minimization.

There is also the ligand queueing interface. This tool allows us to give you small molecules that have been pre-identified (through experiments or virtual high throughput screening) that might bind in the target protein. This tool will also allow you to share your small molecule designs between your teams.

Finally, we are also creating a tool that lets you design small molecules like medicinal chemists. This is called reaction based drug design and provides a synthetic pathway for organic chemists to design the small molecule you create.

Additionally, I should be around in chat on Friday (April 29, 2016) around 2PM Eastern to help answer questions! Think of it like an "office hour", where you can drop in and get things answered versus an actual scientist chat.

ligand_design_cheat_sheet.pdf397.93 KB
( Posted by  free_radical 79 1369  |  Tue, 04/26/2016 - 22:14  |  30 comments )

Ramachandran Map

A Ramachandran plot is a way to examine the backbone conformation of each residue in a protein. It was first used by G.N. Ramachandran et al. in 1963 to describe stable arrangements of individual residues of a protein. Today, a Ramachandran plot is frequently used by crystallographers to identify protein models with an unrealistic backbone.

As many of you may recall, each residue of a protein has two rotatable bonds, which we designate φ and ψ. If we take a protein structure and measure the rotations about these bonds (between -180 and 180 degrees), then we can plot each residue with respect to its φ (x-axis) and ψ (y-axis). The result is a Ramachandran plot, where each black point is a residue of the protein:

Certain rotations are more stable than others: white areas of the Rama plot are unstable, and a residue in this space will have a bad backbone score; colored areas of the Rama plot are more stable, and a residue in this space will have a better backbone score.

The stable areas of the Rama Map in Foldit are divided into four regions, called ABEGO regions, and are colored accordingly:

  • Red: Right-handed helix (characteristic of α-helix)
  • Blue: Right-handed strand (characteristic of β-strand)
  • Green: Left-handed helix (uncommon, except for GLY)
  • Yellow: Left-handed strand (very uncommon, except for GLY)

Because the 20 different amino acid types have different properties, each amino acid type has a slightly different Rama profile. For example, most amino acids have a side chain that would clash with the backbone in a left-handed helix, so maps of these residues have only a faint green region. However, glycine has no side chain and can easily adopt a left-handed helix conformation, so its map has a large, intense green region.

Mouse over a point in the Rama Map to see its residue type and number in the upper right corner.
Click on a point to see the specific Rama profile for its amino acid type; this also selects the residue in Selection Mode.
Click and drag a point to change the φ and ψ rotations of a single residue's backbone.

The viewport at the top of the Rama Map will focus on a selected residue, and simply shows the local configuration of the protein backbone around the selected residue. Each residue in the viewport is colored according to the ABEGO region in which it lies. The ABEGO coloring scheme can also be applied to the main Foldit console in the View Options with View->AbegoColor.

Ideal Loops

When designing a protein, there are usually a number of different loop backbones that can connect α-helices and β-strands. However, we've found that certain types of loops occur frequently in native proteins, and that these "ideal" loops can be distinguished by ABEGO patterns. For example, the most common way to connect two β-strands is by a short hairpin, with two residues in left-handed helix (green) conformation.

The Foldit Rama Map includes a gallery of ideal loops, located in the drop menus in the upper right corner. Each drop menu displays a handful of ideal loops that can be used to connect some combination of α-helices and β-strands. These are provided as a reference for Foldit players, and we encourage players to try to incorporate these loop structures in their designs. Within each drop menu, the most common loops are listed at the top, but a less common loop may be preferred depending on the precise layout of α-helices and β-sheets in a design!

The Rama Map will be available to use in selected design puzzles. It can be accessed from the Actions menu in the Original Interface; or from the Main menu in Selection Interface. Try out the new Rama Map in the latest design puzzle!

( Posted by  bkoep 79 1027  |  Wed, 03/16/2016 - 01:53  |  10 comments )

Sheets and Barrels

One of our players recently asked an interesting question in the Forum, about structural components that differentiate beta-sheets and beta-barrels. We posed this question to the Baker Lab's beta-barrel specialist Anastassia Vorobieva, and here's what she had to say...

Question, by brow42:
We recently had a design puzzle that preferred sheets. Some players made a sheet sandwich and some made beta barrel. We all made hydrophobic cores. But what structural component in real proteins lead to one or the other?

Answer, by bkoep:
I'm not the expert on this, but I can tell you what I do know. Perhaps I can track down another Baker lab scientist to follow up...

In many beta barrels, there are key positions that adopt irregular backbone conformations to reshape the beta sheet. Some positions adopt a "beta bulge," in which an extra residue is inserted between two residues of a beta strand. In the primary sequence, this residue would interrupt the normal pattern of alternating polar and nonpolar residues. There are also "glycine kink" positions, in which a glycine residue deforms the beta sheet by adopting a conformation unfavorable for other amino acids.

In beta sandwich proteins (and in many other structures with beta sheets), the "edge strands" of a beta sheet are often sprinkled with polar residues on the core-facing side of the sheet (which is normally nonpolar). Sometimes these are residues like TYR, which has a hydrophobic region that can contribute to core packing, as well as a polar atom that can extend out into solvent to make hydrogen bonds.

Complete answer, by Anastassia Vorobieva, PhD:
As bkoep pointed out, the presence of glycines in the middle of the beta-sheet (which is rare in beta-sandwiches), the position of the bulges and the presence of edge polar residues are good discrimination criteria between beta-sandwiches and beta-barrels.


However, there is no easy answer and we still have no clear idea of how these structural elements interact with each other. For example, beta-bulges are present in both beta-sandwiches and beta-barrels. Only their position matters. And some beta-barrels have polar residues in their core, especially those that bind small molecules. And to make everything even more confusing, some beta-barrels are able to close without the presence of glycines in the sheet!

To get a little bit more into details, beta-strands like to have a right twist. In other words, the side chains and the hydrogen bonds tend to rotate clockwise along a beta-strand.


This individual strands twist results in the "fan-shaped" beta-sheet mentioned by Susume. However, twist can be constraint in strands located in the middle of a sheet as such strands have to interact with the neighbor strands that have their own twist. In beta-barrels, the curvature necessary to close the barrel is hardly compatible with the individual twist of the beta-strands. As a result, there are some key positions in the barrel where the strand just can't continue to twist to the right and simultaneously interact with the two neighbor. There are several strategies in native proteins to "reset" the twist in such regions:
- Placing one glycine, which is the only residue that can twist to the left.
- Placing a bulge, which forces the right twist at the expense of the hydrogen bonds sometimes.
- Reduce the number of inter-strand hydrogen bonds. In the barrels that are able to close without glycines the strands typically interact with a larger offset.

To design beta-barrel proteins de novo, we are currently working on strategies to predict the key regions in the sheet were the twist will become a problem.

For your models in Foldit, here are some ideas to find twist problems:
- The side chains and the hydrogen bonds rotate to the right along the strand.
- If the twists of two neighbor strands are not coordinated, the side-chains of two interacting residues will tend to bend towards each-other. When two neighbor strands twists are well coordinated, the side-chains are parallel to each-other.
- The bending of the side-chains towards each-other is likely to cause several problems in the structure. These side-chains are likely to clash with each-other and the local torsion of the backbone to be unfavorable. As a consequence, the Foldit score will probably be negatively affected if one tries to force closure of a sheet that is more likely to fold into an open sandwich.

To summarize, the presence of glycines and polar edge-residues are good discriminators between barrels and sandwiches. If they are not sufficient, look for clashes and score problems that would indicate that you are trying to force the closure of a sheet that does not meant to be closed.

Regarding the loop length mentioned above, it should not have an influence in a properly twisting sheet, as the twist of the turn is compatible with the overall twist of the strand. However, spvincent is absolutely right in the case if the twist of the strands is not properly adjusted. Then the constraints building up by that unappropriated twist will be especially high in the loop region and a longer loop will help.

( Posted by  bkoep 79 1027  |  Tue, 03/08/2016 - 22:13  |  2 comments )

Snowflake 2015/6 Puzzle Results!

First, let's recognize the best of the bunch: spvincent, 01010011111, actiasluna, smilingone, franse and BCAA!

Now let's take a look at the designs that caught our eye when we went through them earlier this week:
This design, via spvincent was selected by jflat06!

01010011111 created a design that caught the attention of Seth Cooper during our deliberations.

free_radical had a number of great words to say about actiasluna's design.

smilingone! This is the design of yours that got my attention and I called dibs on it pretty fast, in case you wanted to know. (inky's pick)

bkoep was fond of the design from franse, so he claimed that as a favorite.

We also have a student working with us decide this design from BCAA was his favorite.

That wraps it up for this challenge - but we really appreciate all the effort put into the designs and hope you enjoyed the extra challenges from this year's "Snowflakes, Revisted".

( Posted by inkycatz 79 2078  |  Thu, 01/14/2016 - 22:14  |  4 comments )

Devprev Drug Design Update

Hi everyone,

I want to thank you for taking the time to test the first drug design puzzle. It is very nice to be part of a community that is so enthusiastic and helpful in tracking down bugs, reporting them, and improving science. I know that it can be extremely frustrating testing things that are broken or that do not perform in logical ways. Thank you for sticking it through and reporting the bugs and feature requests.

The idea behind releasing the puzzle to devprev without any information was to get a handle on what needed to be done to improve the drug design module without the constraint of a specific problem being solved. In the future, this will not be the case and a detailed description of the puzzle and the disease you are working on will be given.

This puzzle only featured one small aspect of the drug design tools and modes. In this puzzle, you were allowed to only change the atom identity to a Carbon or Nitrogen. By limiting the tools, we were able to identify specific problems with drug design mode (see below for summary). This is not what the future is of drug design. Adding large and small fragments, extending the small molecule by specific atoms, designing through synthetic rules, and queuing ligands are all tools that were not available. To let everyone get a peek at what is to come, jflat has created another sub group that has all the tools available. We are currently testing this build, but look for a blog post in the future where we will give you instructions on how to obtain it.

Here is a summary of the bugs/features that were reported.

We have tutorials! We just have not released the tutorials. The tutorials take you through a scientific paper where rational drug design was used to create an inhibitor for the FK506 binding protein. The tutorial puzzles will be in the new experimental drug design group that is being set up.

Ligand grids
There were a lot of bugs/features about the ligand grids. A lot of people wanted the color of the grids to be changed. Is it better to have a simple color change is needed or the ability to change the color on the fly is needed?

The ligand grids were put in place to show contact points where you can design acceptors/donors atoms or fragments. Right now, the grids only show acceptors, donors, or repulsive scores. The future of the grids is to include information about structure activity relationships (SAR) of small molecules. SARs help identify regions of the small molecule or protein that like specific features, like hydrophobic/polar atoms, bulky/small fragments, etc. SARs are unique to each protein, so the ligand grids will change.

Finally, there were numerous bugs with the grids. The grids would disappear/appear randomly, or move, or not show when toggled and the slider was moved. These bugs will be fixed!

Ligand jumping out of pocket
I believe that I have tracked down the problem for this. Interestingly, I believe it is how I set up the ligand center filter, which was supposed to keep the ligand in the binding pocket. You have to love irony.

Where to begin? The idea for limiting wiggle, which is what the puzzle was supposed to do, is that drug design is usually done with a fixed backbone with no wiggles/shakes. While this is the general rule of thumb, there are optimization steps done to the backbone and sidechains throughout the design process. The problem with this specific puzzle, and why we wanted to limit wiggle/shake, is that the binding pocket was extracted from a larger protein. There were cutpoints at the end of each helix. Through internal testing, the protein would explode each time I wiggled the protein. I thought that I had fixed the issue, but it looks like there were some problems left.

For the future, the current plan is we will not limit wiggle or shaking. One of the goals of the Foldit community is to develop new strategies to fold proteins. For drug design, we want to keep in alignment with those goals and allow free thought and movement of the protein. New puzzles will not have these constraints associated with them (unless they are really needed).

One thing not shown in this puzzle is on the fly rotamer development. This means, each time you modify the ligand, a whole set of new rotamers are generated. This feature is enabled in the new experimental group.

Scoring is notoriously difficult for small molecule drug design. For this puzzle, we did not make any modifications to the score function to make it easy to distinguish which small molecule was the best. There is a new filter that we have developed that better recapitulates experimental values, but it was not included in the puzzle. Speaking of filters, there will be a whole lot of them introduced in the future!

We look forward to your additional feedback!

( Posted by  free_radical 79 1369  |  Fri, 12/04/2015 - 17:49  |  0 comments )
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Developed by: UW Center for Game Science, UW Institute for Protein Design, Northeastern University, Vanderbilt University Meiler Lab, UC Davis
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