De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small -molecule -binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity -containing backbone architectures. Here, we designed diverse cavity -containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain -Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root -mean -square deviations (RMSDs) of the Ca atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 A & ring; . We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain -swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data -driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small -moleculebinding function.