AlphaFold predictions of fold-switched conformations are driven by structure memorization

Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate…

• NI
  National Institutes of Health

  Award: LM202011
• NH
  National Heart, Lung, and Blood Institute
• UN
  U.S. National Library of Medicine

  Award: LM202011