The β-catenin:TCF interaction is the central node of the Wnt/β-catenin signaling pathway and has resisted decades of direct drug discovery effort. Helicons are stabilized helical peptides engineered to penetrate cells and bind flat intracellular protein surfaces that are inaccessible to small molecules and biologics. Zolucatetide, a Helicon discovered on the Parabilis platform, directly inhibits this interaction. We present the biochemical, cell-based, and in vivo data supporting direct β-catenin binding and on-target pathway inhibition and activity.

In Alzheimer’s disease and other tauopathies, pathology spreads throughout the brain via neuron-to-neuron transmission of misfolded tau.  In this project, we develop stapled peptide ligands for low-density lipoprotein receptor-related protein 1 (LRP1), a primary mediator of tau uptake in neurons, and show that these peptides have favorable pharmacokinetic properties and block LRP1-mediated tau uptake.

Oral peptide formulations are currently of considerable interest spurred by recent approvals including semaglutide (Wegovy), enlicitide (Lipfendra), and icotrokinra (Icotyde). Notably, inclusion of a permeation enhancer (PE) in the Wegovy and Lipfendra formulations underscore the need to consider an enabling formulation strategy for enhanced bioavailability. Herein, we will discuss PE chemistry, formulation considerations, and the potential for PE-peptide interactions.

Advances in peptide therapeutics depend on the interplay between molecular design and delivery technology. This talk will explore innovations across subcutaneous, oral, and emerging routes of administration, highlighting how peptide engineering and formulation design can work together to overcome delivery barriers and expand therapeutic possibilities.

Intelligent machines are emerging as a new kind of scientific instrument for exploring the molecular sequences in which life is written. In this talk, I will describe our efforts to build AI systems for peptide discovery and design, uncovering antimicrobial molecules across the tree of life and creating new peptides with therapeutic potential. By integrating computational mining, standardized experimental datasets, and laboratory validation, we aim to learn generalizable principles connecting sequence to function. These efforts couple computation and experimentation in a continuous cycle, offering new ways to read, test, and ultimately rewrite the molecular information of life to develop medicines.

Peptides offer diverse and unique properties as both biomaterial components and bioactives that can enhance healing in the diabetic wound environment. This talk will highlight three unique mechanisms by which we have employed peptides in engineered materials for diabetic wound healing – as extracellular matrix-inspired structural components, cell interface points, and bioactives. These strategies collectively offer potential to improve treatment both by enhancing human repair and modulating microbial growth and infection.

Therapeutic peptides are promising yet limited by rapid degradation and poor cellular delivery. We engineer programmable nanostructures, including nanoparticle "backpacks" and DNA-based architectures, that stabilize peptides and direct them to specific immune cells with spatially controlled presentation of immunomodulatory cargos. By coupling rational nanoparticle design (geometry, surface chemistry, and aspect ratio) with peptide positioning and processing, we illustrate how to transform short-lived free peptides into potent, tunable immunotherapeutics capable of driving durable antitumor immunity and tissue-resident responses.

Development of a unimolecular quintuple agonist which outperforms GLP-1R:GIPR co-agonism and semaglutide to further decrease body weight, food intake, and hyperglycemia.

The Chatterjee Lab at the University of Pennsylvania develops new generative models to design functional biologics. Our work has centered on language models that de novo design peptides to bind and modulate undruggable disease targets. To enable therapeutic translation, we have developed discrete diffusion models to generate peptides that are "pareto-optimal" across key ADMET properties. Recently, we have extended these frameworks further to discrete flow matching models that generate and refine highly specific binders under competing therapeutic objectives, and have extended these frameworks to peptide-drug conjugates, peptides that control target conformational dynamics, and "escape-proof" antiviral peptides.