Abstract LB003: Integrated computational design and cDNA display selection enable cyclic peptide radiopharmaceutical ligand discovery

Abstract Despite their small size, cyclic peptides offer a desirable balance of binding affinity and biological specificity, making them attractive scaffolds for radiopharmaceuticals and peptide-based theranostics. However, identifying novel cyclic peptide ligands suitable for positron emission tomography (PET) imaging or radiotherapeutic payload delivery remains a challenge due to limited design and screening strategies. To address this need, we developed an integrated pipeline that combines computational design of macrocyclic peptides with high-throughput library screening via cDNA display. Our computational workflow begins by querying structural databases for proteins containing a selected epitope motif as a substructure. We then extract interacting tertiary motifs (TERMs) from these proteins as design seeds that reflect natural protein shape complementarity and interface features. Using these TERMs, we generate diverse peptide backbone ensembles with Protpardelle, a generative model trained on molecular dynamics simulations of thioether-closed cyclic peptides through a unique bias exchange metadynamics capability. Candidate scaffolds are next sequence-designed using ProteinMPNN and Rosetta, followed by full site-saturation mutagenesis to systematically explore advantageous mutations for affinity optimization. Subsequent cDNA display selections and next-generation sequencing (NGS) enable quantitative identification and ranking of enriched, high-affinity binders. We have successfully applied this platform to design cyclic peptides targeting cell-surface proteins, including immune checkpoint regulators and pan-cancer markers and here will present preliminary data across the B7 family of targets. These targets are overexpressed across solid tumors and tumor-associated macrophages in breast, ovarian, and endometrial cancers and have emerged as high-value therapeutic antigens. While antibody-drug conjugates (ADCs) against B7-H3/B7-H4 are in clinical development, their large molecular size and slow clearance can limit tumor penetration and contribute to dose-limiting toxicity. In contrast, compact cyclic peptides exhibit rapid tumor uptake and clearance, properties well-suited for PET imaging and targeted radionuclide therapy. By combining motif-guided computational design with selection-based screening readout by NGS, our framework accelerates the discovery of high-affinity cyclic peptides ready for synthesis and radiolabeling, thereby advancing peptide-based theranostics toward clinical translation. Citation Format: Josephine L. Fieger, Braxton V. Bell. Integrated computational design and cDNA display selection enable cyclic peptide radiopharmaceutical ligand discovery abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (8Suppl): Abstract nr LB003.