The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins’ performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue–residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings—residue pairs with critical dynamical interactions that have been preserved during evolution—DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.

Pancreatic K_ATP channel trafficking defects underlie congenital hyperinsulinism (CHI) cases unresponsive to the K_ATP channel opener diazoxide, the mainstay medical therapy for CHI. Current clinically used K_ATP channel inhibitors have been shown to act as pharmacochaperones and restore surface expression of trafficking mutants; however, their therapeutic utility for K_ATP trafficking-impaired CHI is hindered by high affinity binding, which limits functional recovery of rescued channels. Recent structural studies of K_ATP channels employing cryo-electron microscopy (cryoEM) have revealed a promiscuous pocket where several known K_ATP pharmacochaperones bind. The structural knowledge provides a framework for discovering K_ATP channel pharmacochaperones with desired reversible inhibitory effects to permit functional recovery of rescued channels. Using an AI-based virtual screening technology AtomNet followed by functional validation, we identified a novel compound, termed Aekatperone, which exhibits chaperoning effects on K_ATP channel trafficking mutations. Aekatperone reversibly inhibits K_ATP channel activity with a half-maximal inhibitory concentration (IC₅₀) ~9 μM. Mutant channels rescued to the cell surface by Aekatperone showed functional recovery upon washout of the compound. CryoEM structure of K_ATP bound to Aekatperone revealed distinct binding features compared to known high affinity inhibitor pharmacochaperones. Our findings unveil a K_ATP pharmacochaperone enabling functional recovery of rescued channels as a promising therapeutic for CHI caused by K_ATP trafficking defects.