• 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.
    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.