Design a protein with the help of the new AlphaFold prediction tool! AlphaFold is an algorithm from the company DeepMind and can predict protein structures with incredible accuracy. We want to know if Foldit players can use feedback from AlphaFold to design new proteins! After you've designed a protein, you can upload your solution to the Foldit server and get back an AlphaFold prediction about how your design will actually fold up. Players should aim for an AlphaFold confidence and similarity of 80% or more. See the blog for more details about the AlphaFold prediction tool.
We are especially interested to see what creative new folds players might come up with. We already know that Foldit players can design simple helix bundles or ferredoxin-like ("surfing hotdog") proteins. We are hoping that AlphaFold will allow players to explore protein designs with all beta sheets, or folds with a recessed binding pocket where a ligand could fit. After this puzzle closes, we'll highlight our favorite designs in the next Lab Report video update on September 1.
In addition to the typical extended chain, this puzzle has four starting structures of designed proteins that you may like to use as a starting point. Reset the puzzle to cycle through the different starting structures. There are no Objectives, but we expect successful designs will still need lots of helices or sheets, with short minimal loops, as well as a closely-packed core of orange hydrophobic residues. AlphaFold predictions will not affect your score. Players may insert and delete residues to a maximum of 120 residues total. This special puzzle will remain online until August 31 at 23:00 GMT.
When running a Remix script on some structures, I'm seeing cases where small changes (at least as far as the FoldIt score and structure is concerned) result in major changes to the AF score. Trying to narrow this down a bit, there's a case where changing a lysine to an asparagine results in a drop of 3 points in Foldit which is basically nothing. However it makes a huge difference as far as AF is concerned: dropping the score from (83.0%/80.7%) to (75.1%/61.3%) while doing what looks like a register shift on the sheet structure.
Is this sort of behaviour to be expected? Scientist-shared solution s6_10302 in case its interesting.
Yes, I believe this is to be expected in some cases. The AF neural network is very complex, and two input sequences that are relatively "similar" can in theory produce wildly divergent outputs. Whether or not this accurately reflects any "truth" about the actual folding propensity of these sequences is difficult to say without further analysis or experimental testing. However, there are certainly examples of proteins where minor mutations are known to result in drastically different folding behaviors!
Remember also that the Foldit score only reflects the energy of your solution in its given structure, and tells you nothing about the energy landscape or the energy of alternative structures.
