An efficient mRNA display protocol yields potent bicyclic peptide inhibitors for FGFR3c: outperforming linear and monocyclic formats in affinity and stability.

Macrocyclization has positioned itself as a powerful method for engineering potent peptide drug candidates. Introducing one or multiple cyclizations is a common strategy to improve properties such as affinity, bioavailability and proteolytic stability. Consequently, methodologies to create large libraries of polycyclic peptides by phage or mRNA display have emerged, allowing the rapid identification of binders to virtually any target. Yet, within those libraries, the performance of linear vs. mono- or bicyclic peptides has rarely been studied. Indeed, a key parameter to perform such a comparison is to use a display protocol and cyclization chemistry that enables the formation of all 3 formats in equal quality and diversity. Here, we developed a simple, efficient and fast mRNA display protocol which meets these criteria and can be used to generate highly diverse libraries of thioether cyclized polycyclic peptides. As a proof of concept, we selected peptides against fibroblast growth factor receptor 3c (FGFR3c) and compared the different formats regarding affinity, specificity, and human plasma stability. The peptides with the best KD's and stability were identified among bicyclic peptide hits, further strengthening the body of evidence pointing at the superiority of this class of molecules and providing functional and selective inhibitors of FGFR3c.

An integrated platform approach enables discovery of potent, selective and ligand-competitive cyclic peptides targeting the GIP receptor.

In any drug discovery effort, the identification of hits for further optimisation is of crucial importance. For peptide therapeutics, display technologies such as mRNA display have emerged as powerful methodologies to identify these desired de novo hit ligands against targets of interest. The diverse peptide libraries are genetically encoded in these technologies, allowing for next-generation sequencing to be used to efficiently identify the binding ligands. Despite the vast datasets that can be generated, current downstream methodologies, however, are limited by low throughput validation processes, including hit prioritisation, peptide synthesis, biochemical and biophysical assays. In this work we report a highly efficient strategy that combines bioinformatic analysis with state-of-the-art high throughput peptide synthesis to identify nanomolar cyclic peptide (CP) ligands of the human glucose-dependent insulinotropic peptide receptor (hGIP-R). Furthermore, our workflow is able to discriminate between functional and remote binding non-functional ligands. Efficient structure-activity relationship analysis (SAR) combined with advanced in silico structural studies allow deduction of a thorough and holistic binding model which informs further chemical optimisation, including efficient half-life extension. We report the identification and design of the first de novo, GIP-competitive, incretin receptor family-selective CPs, which exhibit an in vivo half-life up to 10.7 h in rats. The workflow should be generally applicable to any selection target, improving and accelerating hit identification, validation, characterisation, and prioritisation for therapeutic development.