CCK * (Convex Closure K *): A Suite of Algorithms for the De Novo Design of L- and D-peptide Binders

The computational design of L-peptides and their mirror-image counterparts, D-peptides, is a valuable area in drug design. L- and D-peptides offer exceptional structural diversity and high binding specificity, while D-peptides additionally confer critical advantages such as proteolytic resistance. Progress in de novo D-peptide design has been hindered by the absence of evolutionary context and limited structural data, both of which are essential for the deep learning methods that dominate L-peptide design. Consequently, any framework capable of designing both L- and D-peptides must not only be grounded in data-driven inference but also first-principles, physics-based modeling. Here, we introduce a unified computational framework that supports de novo design of both L- and D-peptides, thereby expanding the accessible design space across the full mirrored proteome. Convex Closure K * (CCK *) is a suite of chiral-invariant algorithms: scope, montage, and arise. scope, using geometry as a proxy for chemical energetics, computes convex hull representations of rotameric states to rapidly generate multi-sequence protein contacts maps. montage employs geometric hashing in conjunction with the K * algorithm to generate and rank backbone scaffolds by their suitability for sequence design. arise is a sequence design algorithm that uses an iterative residue search approach in an undirected graph to discover high-affinity peptide sequences. We apply the full CCK * suite to six de novo design tasks, benchmarking both homochiral and heterochiral binding on chirality-preserving and chirality-inverting designs.