Since this is getting a pretty high rank, maybe I should use it to pitch an interesting experiment to the DeepMind team (if they are reading this). Take random amino acid sequence, maybe bias pulling out of the bag of letters in a reasonable distribution, then pick sequences that AlphaFold2 thinks will collapse into a volume in any way smaller than their expected free chain radius; then start mutating this sequence, selecting for sequences that decrease the chain radius. Sort of a GA descent on chain radius.

My question are as follows:

1. Does AlphaFold ever converge on structures in this fashion at all? As in, is the heuristic able to identify partial folds, or, is foldability somehow encoded either through evolutionary history?

2. If it does, are the structures that it identifies truly compact (measurable through sedimentation centrifugation) and solvable using conventional techniques? I suspect if the first than definitely the second because of sample bias.

3. If it does identify compact structures, are they novel sequences or sequences homologous to existing structures? If the former, then AlphaFold is truly extrapolating. If the latter, then it's only interpolating.

It would be super cool if AlphaFold discovered a "new fold" (for those who are unfamiliar with the jargon, a fold is a family of homologous sequences that create defined structure, novelty is gauged by homology, not the end structure; you can have the same structure created by wildly different sequences). It would be even cooler if AlphaFold discovered a new motif (besides alpha helix, beta sheet, and the known turns/chains). But I think that is rather unlikely.

4. If you change your bag distribution by using a primitive aa set (delete histidines, tryptophans, enrich for leucines and serines) how do your search dynamics change?