Mirror-Peptidizer: In Silico Mirror-Image Screening Enables De Novo Design of D-Peptide Binders without D-Protein Synthesis

Abstract

D-peptides are attractive therapeutic modalities because they are generally more resistant to proteolysis than their L-counterparts, yet systematic design of target-binding D-peptides remains nontrivial. Here, we report Mirror-Peptidizer, an end-to-end in silico mirror-image screening workflow that generates D-peptide binders without requiring chemical synthesis of D-protein targets. The workflow mirrors an L-protein structure to a virtual D-protein, designs L-peptide backbones in the presence of the mirrored target using a diffusion-based backbone generator, selects sequences with a neural sequence design model, and explores local sequence neighborhoods via Bayesian multi-objective optimization balancing sequence-backbone compatibility and a solubility heuristic. Mirroring the resulting complex yields the corresponding D-peptide predicted to bind the native L-target. Using MDM2, PD-L1, and interleukin-23 receptor (IL-23R) as test cases, we identified D-peptides spanning α-helical, β-rich, and mixed conformations with affinity from 11.9 nM to sub-μM. For the MDM2 system, the 1H-15N HSQC (heteronuclear single quantum coherence) perturbations and protein mutagenesis support the designed interface, and cell-penetrating conjugates show p53-dependent cancer growth inhibition. Similarly, PD-L1-targeting D-peptides potently inhibited PD-1/PD-L1 interactions in competitive binding assays in vitro, and IL-23R-targeting D-peptides inhibited IL-2/IL-12/IL-23-induced interferon-γ production in human peripheral blood mononuclear cells. Mirror-Peptidizer is provided as an open-source implementation to facilitate rapid generation of experimentally testable D-peptide starting points.