  AI algorithms have proven to be excellent predictors of protein structure,
but whether and how much these algorithms can capture the underlying physics
remains an open question. Here, we aim to test this question using the
Alphafold2 (AF) algorithm: We use AF to predict the subtle structural
deformation induced by single mutations, quantified by strain, and compare with
experimental datasets of corresponding perturbations in folding free energy
$\Delta\Delta G$. Unexpectedly, we find that physical strain alone -- without
any additional data or computation -- correlates almost as well with
$\Delta\Delta G$ as state-of-the-art energy-based and machine-learning
predictors. This indicates that the AF-predicted structures alone encode fine
details about the energy landscape. In particular, the structures encode
significant information on stability, enough to estimate (de-)stabilizing
effects of mutations, thus paving the way for the development of novel,
structure-based stability predictors for protein design and evolution.
