No, it's not a miracle; everything it does works because the information to make those predictions is a collection of latent variables and DM found good ways to convert from sequence space into an embedding that approximates those latent variables.
From what I can tell it still depends heavily on having a good sequence and structure template (or templates). It tells us little to nothing about the specific details of the folding process. To me the only part that seems miraculous is that it seems like we can predict novel structures (previously unknown conformations) using small fragments of templates rather than entire protein domains.
> To me the only part that seems miraculous is that it seems like we can predict novel structures (previously unknown conformations) using small fragments of templates rather than entire protein domains.
Just to add a bit of context: Rosetta’s de novo methods, which had the highest success rates of template-free structure prediction before the arrival of ML-based protocols, use a similar approach. Picking small fragments from protein structure databases reduces the conformational sampling space a lot.
Yes, it was miraculous when they did it, too (a quarter century ago: https://pubmed.ncbi.nlm.nih.gov/10526365/). I believe they called it ab-initio, which led to a lot of complaints because ab-initio means something entirely different (I've published with David Baker,but on a different topic).
From what I can tell it still depends heavily on having a good sequence and structure template (or templates). It tells us little to nothing about the specific details of the folding process. To me the only part that seems miraculous is that it seems like we can predict novel structures (previously unknown conformations) using small fragments of templates rather than entire protein domains.