OmniPepBuilder: Unified Peptide Binder Design via Systematic Exploration of Binding Landscapes

Peptides offer a versatile modality for modulating protein–protein interactions, but scarce complex structures constrain design across diverse cyclization chemistries. Training-free methods repurpose binding-related knowledge encoded in pretrained structure predictors, yet peptide structure prediction and scoring remain insufficiently benchmarked, and efficient exploration of diverse poses remains challenging on difficult targets. We begin with rigorous structural and binding benchmarks to improve prediction and reassess scoring, finding that confidence and Rosetta scores are more informative in controlled mutation series than in heterogeneous screening. We therefore introduce OmniPepBuilder, a unified, training-free two-stage framework for linear and cyclic peptide binder design. To address limited pose diversity and sampling efficiency, global proposal combines state replay, dynamic structural cell partitioning and revisit penalties for adaptive, systematic exploration of binding basins. Local refinement then uses physics-based scoring to optimize interfaces within selected basins. Computational benchmarks show pronounced gains in pose discovery on difficult targets, while local refinement further improves predicted interactions in already confident complexes. Encouraged by this exploration capability, we extend the framework to multi-target binders and macrocyclic molecular glues, where a single peptide must satisfy multiple interface constraints. The putative designs accommodate distinct target pockets in separate complexes or bridge protein partners in diverse ternary poses, demonstrating efficient exploration under the joint geometric constraints of these demanding applications.