S.-J. Chen et al.
Opinion: Protein folds vs. protein folding: Differing questions, different challenges
Proc. Natl. Acad. Sci. USA, 2023, 120 e2214423119
Link to online article

Incipit
Protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction (1-3). By combining physical and geometric constraint - and especially patterns extracted from the Protein Data Bank (4)- these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds.
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But this is not folding prediction. Patterns extracted from proteins in the Protein Data Bank (PDB) provide a ready "parts list", circumventing the folding process entirely. These patterns are "fully baked." That is, a pattern extracted from a solved structure in the PDB is fully preorganized; any physical-chemical organizing interactions have already been realized during folding. The situation is analogous to interpreting a movie by fast-forwarding to the final scene without first watching the previous two hours; we know how it ends, but we don't know why.