Protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction (1–3). By combining physical and geometric constraints—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. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database (5)(https://alphafold. ebi. ac. uk/). This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”(6). Clearly, deep-learning AI represents a major advance in protein fold prediction.

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