AI turns routine protein measurements into maps of stability
A standard experiment can contain far more biology than its standard analysis reveals.
Hydrogen-exchange mass spectrometry is widely used to see which regions of a protein become more or less exposed. But those measurements contain something much richer: the local energetic stability of the protein, amino acid by amino acid.
PFNet learns to recover that hidden information. The clever part is that it does not need a huge experimental training set.
The authors generate synthetic experiments from the underlying physics, add realistic experimental imperfections, and train the model to work backwards from the measurements to the hidden protein energetics.
What previously took tens of hours can now be done in seconds.
Applied to the SARS-CoV-2 spike protein, that extra resolution reveals not just where ACE2 binds, but how binding redistributes stability across the protein, stabilizing some regions while destabilizing others.
AI can increase the resolution of an experiment without changing the experiment itself.
Lu et al., Nature Communications (2026), CC BY 4.0.
doi.org/10.1038/s41467-026-7…