Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

Mutationally sensitive residues, where substitutions cause large stability changes, should also be dynamically rigid, because both properties arise from tight local packing and extensive contact networks. We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted {Delta}{Delta}G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across [~]2,000 natural proteins, [~]400 de novo designs, and 759 NMR-characterized proteins. Robustness predicts dynamics well (median within-protein |{rho}| {approx} 0.6), approaching the AlphaFold2 pLDDT confidence score while providing complementary information: robustness explains additional variance beyond pLDDT on every dataset, with the largest gains on designed proteins. The correlation is equally strong on de novo designs that lack any evolutionary history, pointing to a biophysical effect rather than a proxy for sequence conservation. A multiple regression model using a full 20-dimensional {Delta}{Delta}G profile per residue further outperforms all scalar summaries, showing that the identity of the substituted amino acid encodes dynamical information not captured by single-number predictors. Case studies on individual proteins, including the Zika virus capsid where pLDDT fails almost entirely, show that robustness maps physically meaningful dynamical patterns onto three-dimensional structures. Mutational robustness is thus a physically interpretable probe of the local fitness landscape that complements structural confidence scores for predicting protein flexibility.