They can't possibly know that. What they know is that their guesses are very significantly better than the previous best and that they could do this for the widest range in history. Now, verifying the guess for a single (of the hundreds of millions in the db) protein is up to two years of expensive project. Inevitably some will show discrepancies. These will be fed to regression learning, giving us a new generation of even better guesses at some point in the future. That's what I believe to be standard operating practice.
A more important question is: is today's db good enough to be a breakthrough for something useful, e.g. pharma or agriculture? I have no intuition here, but the reporting claims it will be.
Proteins don't exist as crystals in a vacuum, that's just how humans solved the structure. Many of the non-globular proteins were solved using sequence manipulation or other tricks to get them to crystallize. Virtually all proteins exist to have their structures interact dynamically with the environment.
Google is simply supplying a list of what it presumes to be low RMSD models based on their tooling, for some sequences they found, and the tooling is based itself on data mostly from X-ray studies that may or may not have errors. Heck, we've barely even sequenced most of the DNA on this planet, and with methods like alternative splicing the transcriptome and hence proteome has to be many orders of magnitude larger than what we have knowledge of.
But sure, Google has solved the structure of the "protein universe", whatever that is.
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Yes, the idea of a 'protein universe' seems like it should at least encompass 'fold space'.
For example, WR Taylor : https://pubmed.ncbi.nlm.nih.gov/11948354/
I think the rough estimate was that there were around 1000 folds - depending on how fine-grained you want to go.
Absolutely agree, though, that a lot of proteins are hard to crystalise (i understand) due to being trans-membrane or just the difficulty of getting the right parameters for the experiment.