Everything between the BRCT and RING domains of BRCA1 is an intrinsically unstructured region which DeepMind correctly predicts, https://pubmed.ncbi.nlm.nih.gov/15571721/
Another famous one would be R-domain of CFTR, which was not resolved in experimental structure determination, and AlphaFold models correctly show disorder there. Nothing to be done in those cases except perform molecular simulation or other experiments to assess dynamic ensembles, https://alphafold.ebi.ac.uk/entry/P13569
A curious non-biologist here: how valuable are these low confidence predictions for biologists? In other words, is it hard to predict but easy to check situation as with, say, prime numbers in mathematics?
The medium-confidence predictions are great for grounding or sourcing intuition. If you're trying to divide up a protein for an experiment and you have to choose where to divy it up - you'd like to use even a bad prediction to help weight an otherwise completely random approach. AND there are great methods to help with this, but they're often custom, time-consuming, and out-of-field for most. So being able to very quickly spot-check using a uniform state-of-the art, for any arbitrary protein, makes it actually pretty useful for certain kinds of pre-experimental guidance.
Re:AlphaFold Protein Structure Database
by devindotcom on Thursday July 22, 2021 @01:12PM
(#27921547)
Some are valuable for the reasons the other person responding noted, but some of the low confidence predictions may also be high confidence predictions of a disordered class of protein that doesn't have a standard rest state. So it's useful work one way or the other.
Maybe I was wrong for expecting the impossible here, but I was excited to see this specific structure and it appears that there is still work to do. Nevertheless, kudos to Deepmind on their amazing achievement and contributions to the field!