Designed to Bind

To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The de novo design of proteins capable of binding small molecules to facilitate drug delivery is challenging as it requires the simultaneous optimization of protein sequence and structure, along with ligand conformation in the protein. Now, Fry et al. report an optimization algorithm termed neural iterative selection–expansion (NISE) that overcomes this issue. The strategy relies on two neural networks, one to design protein sequences based on an input backbone, and another to predict the structural complex of input protein and desired ligand. This approach enables the design and refining of protein sequences, simulation of protein–ligand structures and ligand conformations by comparing them to the previous input. The team designed protein binders for exatecan, a clinically approved anticancer drug. After generating single-chain four-helix bundle protein scaffolds and computing ligand conformers, these structures were used as initial inputs for NISE. The algorithm produced a series of candidate designs, from which four were selected based on the extent of ligand burial and the ligand–protein interactions. Protein expression enabled the determination of dissociation constants, with three pairs presenting Kd values.