Experiment results for coronavirus spike binders
The experimental results are in for Foldit player’s 99 binders against the coronavirus spike protein! If you’ve been following along, you know this experiment was planned for earlier this summer, but got held up by some technical problems with our DNA supplier. Well, we found a workaround, got new materials, and ran the binding experiment to test whether any of the 99 Foldit designs bind to the SARS-CoV-2 spike protein.
Unfortunately, we did not see appreciable binding from any of the 99 Foldit designs. Below we’ll walk through the details of the experiment, and we’ll also discuss some exciting news about a successful binder designed by IPD scientists.
Our binding experiment uses two techniques called yeast display and fluorescence activated cell sorting (FACS). You can read more about those techniques in a previous blog post.
In short, we put custom DNA into 100,000s of yeast cells, which then display our protein designs on their surface. After mixing our yeast with fluorescent target protein, we can quickly sort through the yeast cells and pick out those that bind to the target.
Figure 1. (A) Schematic of FACS experiment and (B) example scatter plot of fluorescence from a FACS sort. Each point is a yeast cell, with green fluorescence (expression) on the x-axis, and red fluorescence (binding) on the y-axis. Points in the top right corner represent cells with both red and green fluorescence, indicating good expression and binding.
After each sort, we sequence the DNA of just the collected cells (e.g. the cells that showed expression and binding signal). These DNA sequences can be mapped back to the protein designs that were displayed on the yeast cells.
We count how many times we read each design in the sequencing data. A design with a high number of sequencing counts means that a lot of yeast cells displaying this design were collected, and indicates a successful binder.
Below is a preview of the data. You can download the data for all 99 designs here.
pdb_id counts1 counts2 counts3* counts4 counts5 counts6 BUNS DDG SASA SC 2008926_c0022 10 0 0 0 0 0 7 -33.546 1314.890 0.661 2008926_c0023 21 1 0 0 0 0 8 -33.030 1391.938 0.663 2008926_c0026 30 13 0 0 0 0 12 -37.822 1621.635 0.584 2008926_c0034 1073 2357 0 0 0 1 12 -44.100 1656.985 0.648 2008926_c0036 3 3 0 0 0 0 9 -46.865 1574.854 0.648 2008926_c0037 590 4026 0 45 52 144 7 -36.222 1633.888 0.569 2008926_c0040 343 323 1 0 0 0 10 -35.853 1568.804 0.645 2008926_c0042 57 199 0 0 0 0 6 -31.511 1407.946 0.490 2008926_c0052 2 0 0 0 0 0 6 -31.936 1445.994 0.555 ...
*Note: There was a sequencing error for sort #3, which is why the counts are mostly zeros in the counts3 column. The counts3 numbers do not represent the actual collected fraction from sort #3, and we should disregard those numbers. Fortunately, since sort #3 was an enrichment sort and we have good data for later sorts, we don’t need those counts to interpret the experiment results.
We used a different sorting schedule here than we did in the previous IL6R experiment. In the IL6R experiment, Foldit designs were pooled with a number of IPD designs and were sorted together at the same time. We screened that entire pool against a range of binding conditions (target concentrations from 0.1 to 1000 nM).
In this spike binder experiment, we were able to purify the starting pool so that it was made up almost entirely of Foldit designs. We also took some extra steps to enrich the starting pool, and we only screened against high concentrations of target after enrichment.
- Enrichment at 1000 nM target
- Enrichment at 1000 nM target
- Enrichment at 1000 nM target
- Binding at 1000 nM target
- Binding at 100 nM target
Instead of going directly from the starting pool into binding sorts at different concentrations of target, we first carried out several rounds of enrichment sorting in order to amplify any potential binders. An enrichment sort is very similar to a binding sort, where we select yeast cells that have both expression and binding signal. The experimental conditions are a little more lenient for binding during an enrichment sort.
The important part of enrichment is that the selected fraction of each enrichment sort provides the input for the following sort. If we do this several times in a row, we can drastically enrich the composition of the pool to favor anything that binds even a little bit. This is a way to increase the presence of any weak binders, and helps to ensure we don’t miss anything that was underrepresented in the starting pool.
Figure 2. Diagram of sort procedure. Each bar represents a pool of cells that undergoes sorting. In sort #1, we collect only cells that show high expression (green fluorescence), and these cells become the input for sort #2. Sorts #2-4 are enrichment sorts which should exponentially increase the presence of any binders in the pool. After enrichment, sorts #5 and #6 screen for cells that show binding signal at different concentrations of target.
For each of the sorts in the figure above, we've also noted the percentage of cells that were collected from the sort. In expression sort #1, we collected cells based only on whether they display any protein on their surface (green fluorescence). In sorts #2-6, we collected cells based on whether they bind to the target (red fluorescence).
If there are any successful binders in the starting pool, their prevalence should increase exponentially during enrichment sorts. After a few rounds of enrichment, successful binders will grow to dominate the pool so that the majority of cells show binding.
Unfortunately, after three rounds of enrichment, we still see that <5% of cells show any binding signal at 1000 nM target concentration. This is a clear sign that nothing in the pool binds significantly at 1000 nM target ("easy" binding conditions).
Figure 3. FACS data for Foldit spike binders. Each point represents a single yeast cell displaying a Foldit binder on its surface. The x-axis is intensity of green fluorescence (how much binder is expressed on the cell surface) and the y-axis is red fluorescence (how much target is bound at the cell surface). If there were any successful binders in the pool, we would expect to see a large population in the top right corner of each plot.
Looking at the sequencing counts, we see that a handful of designs did become more prominent during enrichment and show up consistently in the final binding sorts. This does indicate that these designs tend stick to the target somewhat more than other designs in the pool. However, these low numbers are consistent with what we could expect from unfolded non-specific binding, or very weak binding. It is unlikely these designs are folding and sticking to the target as intended, and we cannot expect to improve them by optimization.
A successful IPD-designed binder
In separate news, scientists at the IPD have successfully designed a binder for the coronavirus spike protein! This result was recently posted as an online preprint (meaning the paper has not yet been peer-reviewed).
Rather than design individual proteins by hand, the IPD scientists used supercomputers to automatically generate millions of designs, then checked whether the designs had good binder metrics. Over 90% of the designs were thrown out because they didn’t meet binder metric criteria. The best designs were then tested for binding using the same kinds of FACS experiments we used to test Foldit designs.
Note that this design strategy is not very efficient and requires heavy computational resources. From the millions of initial designs and the 100,000 that were tested, the researchers found only about 100 designs that showed any binding in the lab.
Afterward, scientists did some additional optimization on the best binders, trying all different mutations at every site on the protein. The final optimized designs can bind to the coronavirus spike extremely tightly--even more tightly than natural antibodies!
Lab tests showed that the binders can stop live virus from infecting human cells in a test tube, but these binders still need to be tested in animals before they can be considered drug candidates for clinical trials.
Figure 4. Coronavirus spike binder designed by IPD scientists. On the left, the designed protein binder LCB1 sits at the receptor binding domain (RBD) of the coronavirus spike protein. On the right, lab tests show that this protein (pink trace) is a potent inhibitor of viral infection in human cell culture. Further tests are needed to determine efficacy and safety in whole organisms.
What does this mean for Foldit?
This binder from IPD scientists is great news, and these results help to outline the future direction of binder design in Foldit.
First, the scientists’ method gives us more confidence in Foldit design tools. The automated design methods use the same score function that is used to calculate your Foldit score. And the researchers selected designs using the same binder metrics we've discussed previously (DDG, SASA, and shape complementarity).
But the strategy of the IPD scientists has some shortcomings. Although these automated methods worked great against the coronavirus spike protein, there are many other binder targets that are poorly suited for this approach.
The automated methods work almost exclusively with small 3-helix bundle designs. Other binder targets have convex shapes that aren’t so compatible with a 3-helix bundle fold, or they have protrusions that require special attention. Some binder targets are covered with polar residues that are extremely difficult to satisfy using automatic design.
Those hard problems, where our algorithms fail, are precisely the problems where we think Foldit can excel. We’re looking forward to challenging Foldit players with those tricky problems, and we can get started once we’ve fully integrated the binder metrics into Foldit (we’re almost there -- we appreciate your patience!).
In the meantime, we’ve created a sandbox (non-scoring) puzzle so you can explore the IPD binder in Foldit. Check out the LCB1 Coronavirus Spike Binder puzzle, and get ready for binder metrics to come back in future puzzles!( Posted by bkoep 70 476 | Mon, 08/31/2020 - 15:57 | 4 comments )
Foldit Education Mode
Although Foldit was originally made for science, we always knew it had potential as a learning tool. Until recently, we haven’t done a lot to help teachers use Foldit in their classrooms. We added Custom Contests so teachers could make their own puzzles, but this still takes a lot of time and energy.
The Foldit team had been talking about making a version of Foldit for education, but when the pandemic hit it became clear students across the world needed more remote learning options. So we accelerated our plans, and today we are proud to announce the release of Education Mode!
Figure 1: The Education Mode version of Wiggle teaches you both how to wiggle, and what it’s actually doing.
Education Mode will be launched as a separate app from the main Foldit game. This may change in the future, but for now, if you want to use Education Mode you need to have it installed separately. The downloads can be found on our new educator’s page here.
Education Mode is available from the standard Foldit installation, and can be accessed from the Main Puzzle Selection dialog where you would normally navigate to the Campaign or Science Puzzles. For details about using Education Mode, see our educator's page.
The core idea of Education Mode is to teach a section of a protein biochemistry class through Foldit. We hope this is helpful not only for students, but for anyone curious about the basic science behind protein biochemistry. Even if you’ve been playing Foldit for a while, check out Education Mode for some bonus science and tutorials!
Figure 2: New Primary Structure Puzzle. This is a protein design puzzle, but the purpose is to help you think about which amino acids fit best where in a protein and why based on the underlying chemistry. You’ll notice that the design wheel has had all of the pictures removed to encourage you to visualize the amino acids.
Education Mode has 29 puzzles in 9 tiers. Many of these puzzles are variants of the campaign puzzles, which are designed to teach Foldit gameplay. However, you’re also likely going to learn some biochemistry along the way! In the typical campaign puzzles, we don’t emphasize the biochemistry learning part of it so that you can get to the game quicker and without having to feel like you’re going through a biochemistry class. In Education Mode, the tips focus on teaching you the biochemistry behind the puzzle while learning to play Foldit along the way.
Figure 3: New Idealizing Structure Angles Puzzle. This puzzle is an evolution of the Structure and Idealize campaign puzzle, but now expanded to relate secondary structure to the Rama map, and how to use it.
The Education Mode puzzles start on atomic interactions (like clashes and hydrogen bonds), then focus on amino acid structure before proceeding through different levels of protein structure (primary, secondary, and tertiary structure). Finally, there are a few puzzles on how proteins actually fold in nature, and a final puzzle on protein binding to DNA.
A new feature that you won’t find in the campaign levels is that on many of the puzzles, you can explore the puzzle before clicking through the tutorial, and then reset the puzzle to start scoring. This is so you can explore and experiment before attempting the puzzle for real. You’ll notice that the education tips have both forward and back buttons, and some of them now have pictures to illustrate more abstract concepts! Like the campaign mode, once you’ve completed a puzzle, it will prompt you to move to the next one, but you can also keep playing the puzzle to see if you can improve your score even more.
Some of these puzzles are intentionally hard. We’ve enabled the Save function so that you can take a break and reload your progress, and we have also made it so that you can skip puzzles, in case you want to move on to another topic.
Figure 4: New Tertiary Structure Puzzle. This puzzle is geared specifically to teach students about the difference between secondary and tertiary structure in proteins.
You might notice that we’ve disabled some popular tools (like Wiggle) in many of the Education Mode puzzles. This is to encourage more hand-folding and critical thinking about your choices as opposed to letting the computer do it for you.
Some tools, like Blueprint, are missing from Education Mode because we are still developing lessons for them. For now, the regular Campaign levels are still the best way to learn these tools.
One last feature that we added into Educational Mode is extra camera controls. By pressing Shift+Home, the camera will rock back and forth. Pressing Alt/Option+Home will set the camera into a spin motion. Press the hotkey again to stop the motion. We hope that these new features can help you better visualize the 3D space of your protein!horowsah 70 1855 | Sat, 08/01/2020 - 12:53 | 0 comments )
The energy landscape optimization paper
This blog post is a walk-through for an upcoming paper, showing how researchers at UW and Harvard developed a new method for protein design. This research relied heavily on the work of Foldit players, who will be listed as authors on the paper. (If you have played a Monomer Design puzzle in Foldit, you can opt in to the author list here).
The paper has not yet gone through peer review, but a pre-print draft of the paper is already available online. (Edit: See final publication by PNAS here.) The paper is written for highly-specialized academics with a scientific background, but we think its content can be appreciated by anyone with an interest in protein folding and design.
Below we discuss some of the background for this research, take a look at the figures, and review the main points of the paper.
What is an energy landscape?
The title of the paper is Protein sequence design by explicit energy landscape optimization. Before we jump in, we will need to make sure we understand the idea of an energy landscape. We’ve discussed energy landscapes previously, so let’s recap:
There are a lot of possible ways that an unfolded protein might fold up (think of all the different knots you can tie with a shoestring). Each of these possible folds has some amount of free energy, which depends on the amount of clashing, voids, H-bonding, etc. The lower the free energy, the more stable the fold.
In an energy landscape, we like to imagine all of these possibilities laid out on a grid, like a map of possible folds. Then we imagine that the depth at each point of the map corresponds to the energy of each fold. There will be deep valleys and wells where we have stable, low energy folds; and there will be hills and peaks where we have unstable, high energy folds. This map is our energy landscape.
An unfolded protein will naturally fold into its most stable structure. This is the structure with lowest free energy (the deepest point in the landscape).
Every protein sequence has a different energy landscape. Most random protein sequences have a featureless landscape with many, many shallow wells of similar depth. These sequences will not have a strong preference for any particular fold, and they will be poorly folded in real life.
On the other hand, a well-designed protein sequence will have an energy landscape with a single deep well. This sequence has an overwhelming preference for the low energy fold at this well, and the sequence will be well-folded in real life.
Normally, we try to approximate the energy landscape of a sequence by folding the sequence into thousands and thousands of different structures, and calculating the energy of each one (details here). Even though this only gives us a partial view of the energy landscape, it is computationally intensive, and it takes some 10,000s of CPU hours to compute. (A big thanks goes to Rosetta@home volunteers for providing this CPU power!)
Because energy landscapes are expensive to compute, most protein design methods focus on just the design structure, and ignore the rest of the landscape. We only try to reduce the free energy of our design, and we cross our fingers that the energy landscape has no other energy wells. This is sometimes effective, but it can lead to an energy landscape that has multiple low-energy wells (which means the protein could fold into an unintended structure).
Ideally, we would like a design method that considers the entire energy landscape, but without requiring thousands and thousands of energy calculations.
Figure 1. Energy landscapes and trRosetta. (A) An energy landscape visualizes the energy (depth) across all different folds, or "conformations." Suppose that we want to design a protein with fold P. Most design methods optimize the free energy of fold P and arrive at sequence B (green). Since these methods are blind to the rest of the energy landscape, sequence B might have a landscape with alternative energy wells. A better design method would consider the entire energy landscape to produce sequence A (blue), which has a single low-energy well. (B) The trRosetta neural network takes an input sequence and makes predictions about how the residues will be oriented in the folded structure. This new work shows that trRosetta predictions serve as a good proxy for the energy landscape. The neural network can optimize the sequence to improve the match between the predictions and the desired structure, molding the landscape to favor our desired structure.
Neural networks and sequence likelihood
trRosetta (transform-restrained Rosetta) is a machine learning program developed after the breakthrough AlphaFold program (details in this blog post). The input for trRosetta is a 1D protein sequence, and the output is the predicted distance and orientation between every pair of residues in the 3D folded protein structure.
Previously, researchers at the Baker Lab showed that these distance and orientation predictions are good for protein structure prediction problems. The orientations help us generate a complete 3D model of the folded protein, which accurately shows how the protein will actually fold.
In the new paper, researchers turn trRosetta on its head to evaluate and design proteins. Rather than use the predictions to generate a structure for the input sequence, they compare the distance and orientation predictions to the intended structure, and calculate the sequence likelihood for that structure.
This sequence likelihood score tells us whether trRosetta thinks the design sequence is a good match for the design structure. If the intended distances and orientations of the design structure are a close match to the trRosetta predictions, then the sequence likelihood for that structure will be high. If the design structure is a poor match to the predictions, the sequence likelihood will be low.
Predicting energy landscapes
The researchers used sequence likelihood to show that trRosetta can predict useful information about the entire energy landscape of a protein sequence -- not just information about the preferred structure.
To show this, they used a dataset of energy landscapes for >4000 Foldit designs, which have been accumulated from several years of Foldit design puzzles. This dataset represents about 100 million CPU hours of energy landscape calculations! They divided this dataset into favorable and unfavorable energy landscapes.
First, they calculated the sequence likelihood just for the design structure. They found that the sequence likelihood of a design is a good predictor of whether a design has a favorable or unfavorable energy landscape. Importantly, trRosetta sequence likelihood was a much better predictor than just the Rosetta energy (or Foldit score) of the design. Since trRosetta takes just a couple minutes to run, this could cut down the need to run expensive landscape calculations!
Next, the researchers calculated sequence likelihoods for many different structures across the energy landscape of each design. They found that these likelihoods accurately reflect the shape of the landscapes.
For example, when they looked at a favorable energy landscape with a single energy well, they saw that models within the well had a high sequence likelihood, and models outside the well had low likelihood.
They also looked closely at a few special cases, where an energy landscape shows two competing energy wells. One of these wells represents the intended design fold, and the other well represents a decoy fold that is equally stable. We expect that a protein sequence with this kind of energy landscape is equally probable to fold into the design fold or the decoy fold. This is correctly reflected in the sequence likelihood scores, which are reduced for the design fold, and are comparable between design and decoy folds.
Figure 2. trRosetta predicts information about energy landscapes. (A) Histogram of sequence likelihood (left) and Rosetta energy (right) for 4200 Foldit designs. The distribution of favorable landscapes is shown in blue, and unfavorable landscapes in gray. There is significant overlap in the distributions of Rosetta energy, showing that Rosetta energy is a poor predictor of the whole energy landscape. Sequence likelihood is a better predictor, with less overlap between blue and gray distributions. (B) Energy landscape plots for Foldit designs, with color gradient showing the trRosetta sequence likelihood of models across the landscape. At the top, a landscape with a single well has very high sequence likelihood within the well. Below, landscapes with multiple wells have weaker, more dispersed likelihood. Cartoon illustrations show the design and decoy folds X and Y. On the right, example bimodal distributions show the “ambivalency” of trRosetta distance predictions when a landscape has two energy wells.
This is all well and good. We’ve seen that trRosetta is really useful for predicting theoretical energy landscapes, and can help us cut down on computational work. But does it actually reflect physical reality? A more stringent challenge would compare trRosetta against real experimental data from lab testing.
Last year we published the experimental testing results for 145 Foldit player designs. When the researchers checked this data, they found that trRosetta sequence likelihood was a good predictor of success in the lab!
Figure 3A-B. trRosetta predicts experimental testing results. (A) When we look at the testing results for 30,000 IPD-designed proteins, we see that trRosetta sequence likelihood correlates well with folding stability (as approximated by protease resistance). By contrast, Rosetta energy of the design is poorly correlated with this stability measure. (B) Histogram of sequence likelihood (left) and Rosetta energy (right) for 145 experimentally-tested Foldit designs. Successful designs are in blue, and failures in gray. Sequence likelihood is a better predictor and energy alone, with less overlap between the success and failure distributions.
Optimizing the energy landscape
Finally, the researchers put trRosetta to the test, to see if it could actually redesign proteins to have favorable energy landscapes.
From the 4000 Foldit designs, they selected a representative set of 200 models and used trRosetta to redesign their sequences. Remember that, in Foldit, the original designs were made to optimize the energy (the Foldit score) of just the target fold. Now, trRosetta is trying to optimize the entire energy landscape, which encompasses the energies of all possible folds.
The results were surprising: although trRosetta was good for eliminating decoys and coarsely sculpting the energy landscape, the resulting landscapes lacked a sharp, deep energy well that we like to see for a stable, well-folded protein design. Instead, a combination of trRosetta (optimizing the landscape) and traditional design (optimizing the design energy) yielded the best energy landscapes, with a single deep energy well.
Figure 3C-D. Redesigning proteins with trRosetta energy landscape optimization. (C) Example energy landscapes for two redesigned Foldit proteins. Redesign with trRosetta alone produces a landscape with a single shallow well, and Rosetta lowers the energy without favoring a single energy well. Combining both approaches gives a favorable energy landscape with a single deep energy well. (D) The quality of energy landscapes across all 200 redesigned proteins. The colored lines show how many redesigns (y-axis) meet a threshold for energy landscape quality (x-axis; increasingly stringent threshold). Traditional Rosetta redesign (green) is susceptible to low energy decoys, and less than 50% of redesigns pass the lowest threshold; however, Rosetta redesigns that do pass have very deep energy wells and also tend to pass higher thresholds. trRosetta (purple) improves landscapes that fail the low-quality threshold, but cannot achieve deep energy wells that meet a high-quality threshold. A hybrid approach, in magenta, achieves the best of both worlds.
What does this mean for Foldit?
In all Foldit design puzzles so far, we’ve seen that players are very good at optimizing the score of their designs. But the real challenge of protein design is how to account for the rest of the energy landscape, and we still haven’t found a good way to do this in Foldit.
Some players probably remember the 2018 Foldit Partition Tournament, which challenged players to explore the energy landscape of each others’ designs. That showed some promise, but still was time-consuming and low-throughput (we generated only 20 landscapes in 6 weeks).
trRosetta offers a fast alternative for predicting energy landscapes, and we may be able to combine it with normal Foldit scoring. trRosetta might be able to report the sequence likelihood of a Foldit solution, and even suggest mutations to improve its energy landscape.
One disadvantage with machine learning programs like trRosetta is that they are “opaque” and sometimes difficult to make sense of. We can’t really say why trRosetta makes certain suggestions, or ask which design features are causing problems. That could make it difficult to reconcile trRosetta suggestions with Foldit score components like clashing and H-bonding.
Another shortcoming of trRosetta is that it cannot suggest how to refold the protein backbone to improve an energy landscape. Some protein backbones are inherently more difficult to design than others (or even impossible). Finding designable backbones is an important aspect of protein design, and we think that’s a particular strength for Foldit players.
Still, trRosetta is clearly a useful tool for protein design, and we’ll be looking at ways to incorporate trRosetta into Foldit. Maybe players could find new and unexpected ways to use feedback from neural networks!( Posted by bkoep 70 476 | Fri, 07/31/2020 - 21:00 | 8 comments )
Newsletter July 24: A Good Week for Go Science
Dev Josh here with your weekly Foldit update. Congratulations to Go Science! for being the top of all three puzzles this week! Go Science has been an open and active group since 2010. One of the best ways to learn and improve in Foldit is to join a group.
Solutions from This Week's Puzzles
(Disclaimer: This is not scientific feedback; these solutions are not officially endorsed by the Foldit scientists.)
Puzzle 1863: Refinement R1043
I've heard this puzzle was crashing pretty frequently. Thanks for your patience everyone, the devs are hard at work trying to fix these issues!
Puzzle 1864: Symmetric Trimer Design: Limited Interface
To master this puzzle, you needed to limit how big your binding interface was. Notice how the top scores rotated their helical bundles to limit their attachments!
Puzzle 1865: Coronavirus Anti-inflammatory Design 8
Bkoep said there were 15 unsolvable BUNS, but some of the top solutions got them down to 11! Great job on satisfying those BUNS everyone, keep it up!
Want to know more about why we're designing binders from scratch? Check out this forum thread for details on why we're not just using the ACE2 receptor design.
Recipe of the Week
This week's recipe is new but with great potential:
mwm64's UnBun is designed to help you reduce BUNS. This recipe only works on puzzles with the BUNS objective, and I haven't personally tried it out much, but I've heard a few folks are trying it. Plus, if you're looking to get involved with recipe evolving, this simple recipe could be a good way to get some practice with Lua. Given how important the BUNS objective is, we're going to need more recipes like this! So thanks mwm64 for making the first de-BUN-ifier!
Player of the Week
A quick shout-out this week to malphis, a friendly newcomer who joined a couple of months ago and has been really active in chat. Malphis has also been super helpful submitting bug reports to help the devs track down issues. Thanks!
Art of the Week
Looking for some more protein beauty? Check out this beautiful proteins blog! It's got a ton of real proteins that are naturally amazingly beautiful.
Today’s Master Folding Tips
Beginner: Before trying to wiggle your designed protein into the perfect shape, give it a mutate first! This will help the protein pack together better and give you a cleaner structure to work with. You can also mutate by hand: for example, although all of your amino acids start as isoleucine, it's actually better to set your loops to asparagine to start with.
Intermediate: Have you learned how to use the Rama map yet? We're working on a few new guides that should help make it easier to learn, but in the meantime Susume has two guides on how to use the Rama map to fix un-ideal loops and even copy a loop
Expert: Are you planning your design before you make it? Before you start drafting, spend a few minutes thinking about what your design will look like. How long will each helix and sheet be? Will you try to make pi stacks? What part of the protein will bind at the interface, and how will that give it shape complementarity? Once you're ready, use Loci's AA Edit and SS Edit to enter your design and give it a quick early/midgame rinse. Then hand it off to a novice member of your group to evolve and try another design!
Have a tip to share with the community? Reply with your wisdom, or post on our Forums!
Until next time, happy folding!( Posted by agcohn821 70 1108 | Wed, 07/29/2020 - 18:53 | 0 comments )
Foldit Newsletter July 17: Bonjour Encore Triple Hélice
Dev Josh here with your weekly Foldit update.
3 Solutions from This Week's Puzzle
(Disclaimer: This is not scientific feedback; these solutions are not officially endorsed by the Foldit scientists.)
Puzzle 1861: Symmetric Trimer Design: Buried Unsats
This solution took a less common approach to the triple helix meta. I'll let you decide for yourself whether you think it scored well or not. What do you think of it? Let us know in the Discord!
Puzzle 1862: Coronavirus Round 13
An extra special congratulations goes to clark92 for being top rank for this puzzle! This up-and-coming folder only started folding at the end of February, and already they've taken the leaderboards by storm!
These solutions come from some of our beginner folders! Can you tell what they could do better?
As a reminder, here are some helpful tips from bkoep on designing a good binder!
Want to get your top solution featured in the weekly newsletter? Click the "Share with Scientists" button in the "Open/Share Solution" menu and your solution might get featured! Don't forget to fill out our username sharing form if you'd like your username to be shown with your solutions!
Recipe of the Week
Player of the Week
If you're still on the intro puzzles, nspc also has a video on beating Hydrophobic Disaster.
I think I speak for everyone when I say merci beaucoup! Nspc (pc on Discord) is a beginner folder who has been learning fast by being really active in the chat. Say hi next time you see them around!
Art of the Week
Here's some art from 1861: a cool-looking triangle and a crazy ball of... I don't even know what... Thanks for sharing!
Today’s Master Folding Tips
Despite how common they are, I really recommend trying a helix bundle like the ones you've seen from the top-scoring solutions! Helices are easier to make than loops or sheets, so practicing on helix bundles is a great way to get a higher rank and practice the basics before trying something tricky and advanced like long loops or a sheet structure.
Are you paying attention to which structures your AA structure preferences. AAs prefer to be in? It's not a hard-and-fast rule, but check out the wiki for AA structure preferences. I find this especially helpful for getting started by mutating my isoleucines away into something more suitable for the structure I'm designing, like asparagines for loops, valines for sheets, and MALEK for helices.
How many structural motifs can you name? Most of you know pi stacking, some of you even know about beta hairpins. But do you know about ST turns, Greek keys, and Omega loops? What about sequence motifs?
Having these concepts in your toolkit will give you more conceptual legos from natural proteins to think about when designing. There's plenty of research out there on common patterns, and if you're looking for expert tips, then you're ready to dig into real literature. Good luck, and let us know what you find on the
Want to give your group a shoutout in the next newsletter? Reply with a blurb about what your group is and why new players should join, and your group might get featured in the next newsletter!
Until next time! Happy folding!( Posted by agcohn821 70 1108 | Fri, 07/24/2020 - 03:47 | 0 comments )