We have been investigating the membrane permeability of large cyclic peptide scaffold model systems. In our efforts to apply the insights gained from these systems to find macrocycles that are both permeable and bioactive, we have designed mRNA-display and DNA-encoded libraries with enhanced permeability, using simple design criteria such as avoiding charged groups and hydrogen bond donors in the side chains, and limiting backbone NH groups via N-methylation. We now report lead compounds from these libraries selected against two intracellular proteins, their permeabilities, as well as their biochemical and cellular activities.

The development of macrocyclic peptides (MPs) able to access intracellular protein targets is of keen interest. Here we describe new types of MPs in which the ring is closed through formation of a moiety in which a permanent positive charged is embedded within a hydrophobic heterocyclic ring system. We show that this strategy increases the passive membrane permeability of any MP, often dramatically. We also describe the synthesis and screening of combinatorial libraries of these novel MPs.

Macrocyclic peptides promise access to intracellular “undruggable” targets but suffer poor permeability. We developed a microfluidic, liposome-based permeation screen using click chemistry and a 19.6K-member thioether-cyclized DNA-encoded macrocycle library. Using DOPC liposomes, we identified, resynthesized, and plate-validated permeant hits, demonstrating scalable, permeability-driven selection. Next, we’ll diversify scaffolds and deploy bacterial and mammalian-derived membranes to better mimic barriers, sharpening structure–permeability insight in the bRo5 space and enabling predictive PK modeling.

Peptidic conjugates offer a promising strategy for targeting intracellular proteins, yet limited cell permeability remains a major barrier. This work introduces strategies to enhance and validate the cellular uptake of moderately permeable peptides. By integrating an innovative assay system with structure–permeability design principles beyond PAMPA, MDCK, and Caco-2 models, the study provides insights to advance peptide-based modalities for intracellular drug discovery.

The design of potent peptide mimetics and macrocyclic peptides could benefit from additional constraining and/or covalent targeting strategies. To increase the affinity and residence time of those ligands, we probed the introduction of additional constraining motifs, the dimerization of peptide mimetics, and the  introduction of covalent strategies targeting His, Lys or Tyr residues. I will share our recent experiences with those approaches against various drug targets.

Cyclic peptide conjugation is a powerful method for re-engineering classic chemotherapeutics to achieve higher efficacy and reduced side effects. I present our work on conjugating a cyclic peptide, [(WR)4WK]ßA, to epirubicin. The resulting conjugate was tested across multiple cancer cell lines, including triple-negative breast cancer and multidrug-resistant uterine sarcoma, with heart cells serving as a control for toxicity. The conjugate showed enhanced tumor uptake and potency while significantly reducing cardiotoxicity.

Although there are several examples of macrocyclic peptides showing that they are promising drug candidates to inhibit protein-protein interactions, developing orally available macrocyclic peptides are still a challenge. Developing oral macrocyclic peptides is a two-step process that is required to be optimized simultaneously due to their complex 3D solution conformations: optimization of affinity/activity and oral bioavailability. This talk reviews how to utilize mRNA display platform to develop oral macrocyclic peptides.

My lab is working on the long-standing goal of developing cell membrane–permeable peptides for modulating intracellular targets and for oral delivery. We have developed nanoscale synthesis methods to generate and screen tens of thousands of sub-kDa synthetic peptides. My talk will highlight this platform and examples of successful ligand discovery, including membrane-permeable protein-protein inhibitors (unpublished) and orally available cyclic peptides.

Computational design of structured cyclic peptides has been successful using physics-based models, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning networks have shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. Application of a cyclic offset to the AlphaFold2 network and RFdiffusion, results in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function.

Third-generation co-folding models struggle with predicting structures of macrocycles which contain non-canonical amino acids, thus their application is limited to hit-finding. Vilya goes beyond this limitation to realize the power of such models in a much broader chemical space. I will describe how we build such models, describe their capabilities in structure- and property-prediction, and show vignettes of how they can be used throughout the discovery process.

In this presentation, we introduce how Koliber’s machine-learning technology, integrated with Robust Diagnostics' peptide-array technology, overcomes these limitations. We demonstrate that large libraries are unnecessary, as Koliber’s machine learning can optimize initial hits to achieve improved binding affinity. We also present visualization techniques for detecting binding modes, offering new insights into peptide-array applications for therapeutic peptide discovery.

Cyclic peptides have long been considered attractive as a drug modality due to their medium size and combining the advantages of small molecules and biologics. However, membrane permeability remains a significant challenge. Recently, physics-based Generative AI has emerged as a promising technology to design cyclic peptides with specific properties in mind. Here we focus on applying this method to design de novo cyclic peptides with drug-like oral bioavailability.