The de novo design of small proteins with beta-barrel topologies has been a challenge for computational design due to the complexity inherent in these folds. In a new study appearing in PNAS, a team led by Baker Lab research scientist David E. Kim describes the successful design and characterization of four different classes of small beta barrels using Rosetta energy-based methods and deep learning approaches.
