{"doi":"10.1101/2025.11.21.689740","title":"CCK* (Convex Closure K*): A Suite of Algorithms for De Novo L- and D-peptide Design","abstract":"Abstract The computational design of L-peptides and their mirror-image counterparts, D-peptides, is an active area in drug design. Peptide therapeutics 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 underpin the deep learning methods widely used in L-peptide design. Consequently, a robust framework capable of designing both L- and D-peptides should integrate data-driven inference with 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 both chiral spaces. Convex Closure K* (CCK*) is a suite of chirality-agnostic algorithms: SCOPE, MONTAGE, and ARISE. SCOPE uses geometry as a proxy for chemical energetics, computing convex hull representations of rotameric states to rapidly generate multi-sequence protein contact maps. MONTAGE employs geometric hashing in conjunction with the K* algorithm to generate and rank backbone scaffolds according to their suitability for sequence design. ARISE is a K*-based sequence design algorithm that performs iterative residue assignment in an undirected graph to design high-affinity peptide sequences. We apply the full CCK* suite to six de novo design tasks, benchmarking chirality-preserving and chirality-inverting designs in both homochiral and heterochiral complexes.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":582866,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1452745,"name":"Allen C. McBride","orcid":"0000-0002-6332-5457","position":1,"is_corresponding":false},{"id":413197,"name":"Bruce R. Donald","orcid":"0000-0001-6884-4398","position":2,"is_corresponding":false},{"id":1255906,"name":"Henry Childs","orcid":"0009-0000-7657-9926","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:58:59.653747Z","pmid":"41332631","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}