Computational design and optimization of novel d ‐peptide TNF α inhibitors

: Compared to small molecule drugs, peptide therapeutics provides greater efficacy, selectivity, and safety. The intrinsic disadvantages of peptides are their sensitivity to proteases. To overcome this, we have developed a general computational strategy for de novo design of protein binding helical d-peptides. A d-helical fragment library was established and used in generating flexible d-helical conformations, which were then used to generate suitable sequences with the required structural and binding properties. Using this strategy, we successfully de novo designed d-helical peptides that bind to tumor necrosis factor-α (TNFα), inhibit TNFα-TNFR1 binding, reduce TNFα activity in cellular assays, and are stable against protease digestion. Our strategy of helical d-peptide design is generally applicable for discovering d-peptide modulators against protein-protein interactions.