To be fair, this modeling is based a lot off of learning from previous crystal structures and the biases those may or may not impose. An x-ray crystal model (or NMR/Cryo-EM structure if you have a very small or very large protein respectively) is still considered the “ground truth”, while these machine learning generated structures are more for hypothesis generation/approximations (that may help in building models from experimental structural data).

Crystal packing needed for x-ray crystallography may rigidify a protein, but at least within the field of structural biology it is not believed to alter the shape of the protein. Likely it just hides the dynamic conformations a protein can occupy - and machine learning methods also suffer from this flaw as they only predict a single structure.

It’s not a copy. Completely different architectures. ESMfold uses a language model while AlphaFold2 does not. ESMfold can predict on a single sequence, and AlphFold2 works best when you give it multiple related sequence in a multiple sequence alignment. ESMfold is also way faster (though not as accurate than AlphaFold2)