In this work, we developed a novel pipeline for engineering de novo miniproteins to target the Interleukin-4 receptor alpha (IL4Ra), a crucial receptor in Th2-mediated inflammatory diseases currently dominated by monoclonal antibodies. To achieve this, we used a scaffolding strategy to extract key cytokine helices and used RFdiffusion to design the connecting loops. Initial binders with sub-micromolar affinities were then optimized using molecular dynamics and phage display libraries for affinity maturation. Ultimately, this combined generative AI and directed evolution approach yielded high-affinity miniprotein binders, proving that diffusion models can successfully replicate complex cytokine interfaces for next-generation therapeutics.

Transcription factors and other intracellular protein-protein interactions remain challenging therapeutic targets. This presentation describes an integrated live-cell discovery platform combining genetically encoded peptide libraries, intracellular cyclisation, functional selection and scalable hit validation. By constraining peptides during selection, the workflow enriches cell-tolerated, biostable antagonists that disrupt transcription factor-DNA and protein-protein interactions. The approach links library design, screening, sequencing-based prioritization, biophysical characterization and cellular validation to accelerate discovery of functional, cell-active constrained peptides.

Intrinsically disordered regions regulate signaling and often drive disease, yet their flexibility and lack of stable pockets make them difficult to target. We developed deep-learning based computational approaches to design de novo proteins that recognize flexible sequences, post-translationally modified epitopes, and aggregation-prone regions. These binders showed specificity and enabled applications in oncology, immunology, virology, and neurodegeneration, including CAR-T targeting, MYC inhibition, HBx suppression, and Tau degradation. We also designed sequence-specific metalloproteases that cleave disease-relevant regions of TDP-43. Together, this work establishes a platform for converting dynamic molecular states into selective, therapeutically actionable targets across the proteome.

By integrating approximately 200,000 mass spectrometry datasets with ribosome profiling and immunopeptidomics resources, we identified more than 4,000 translated products from 7,264 non-canonical open reading frames (ncORFs), termed peptideins. Functional validation and evolutionary analyses demonstrate their roles in cell essentiality and medulloblastoma survival, revealing a previously hidden class of biologically relevant peptides that expands the human proteome and offers new opportunities for peptide discovery and therapeutic development.

We introduce a novel workflow for the discovery of protein-binding macrocyclic peptides from small libraries. Using this technology, we have identified a highly selective allosteric ligand for Protein Tyrosine Phosphatase 1B (PTP1B), an “undruggable” target in Type II diabetes. We also discuss how these ligands can be elaborated into protein degraders. Identification and characterization of a PTP1B allosteric ligand.

The development of targeted therapeutics requires balancing the exceptional specificity of antibodies with the manufacturability, tissue penetration, and chemical versatility of peptides. We have developed a peptide engineering platform that converts phenotypic screen-selected domain antibodies into structurally constrained cyclic peptides that preserve antibody-derived binding while enabling modular therapeutic conjugation. This presentation will describe our integrated discovery workflow, including phenotypic phage display, computational CDR3 analysis, cyclic peptide engineering, and functional validation. We will discuss how this platform enables the scalable development of compact, high-affinity targeting ligands for precision drug delivery and next-generation therapeutic applications.

The USP family of DUBs regulate Ub signaling using a common catalytic-domain fold. The dynamic nature of their catalytic-domain is important for controlling USP function, with inter- and intramolecular interactions influencing hydrolysis. This conformational flexibility, in combination with the high sequence conservation of the USP active site, has made it challenging to readily identify potent and selective inhibitors for individual USPs. Here, we demonstrate how a naive, macrocycle-mRNA display selection rapidly yielded high-affinity binders to USP7 that specifically inhibit the DUB in the nanomolar range. Structural analysis reveals distinct binding modes for several peptides, engaging hotspots previously identified by small-molecules.