Huh, I would've said exactly the opposite. The feature space of the variations of amino acid sequences is so big that I wouldn't bet on breakthroughs derived from understanding. Most recent advances seem to focus on how specific sequence motifs interact with each other, which are generally only applicable to certain kinds of proteins, but not protein folding as a whole.
My prediction would be that next breakthroughs that push the field over the usability limit are derived from more general machine learning advances.
What it can be is a force multiplier for every progress in our understanding of why those sequences are transcribed into those structures.
But I have to admit that, with years of head-on, the research has not gotten there so I might be wrong.
Methods from the domain where already not truly model-based (compared to things like physical equations). It is mostly observations on existing proteins coupled with a form of gradient descent so, for this particular application, things have not degraded. (I am aware that there is more to it, this is just a fast summary)
But to be honest, it is far from a solved problem and I expect more breakthroughs from deep understanding and modeling than ML.