(653c) Design of Peptide Affinity Ligands for Tetraspanins for Exosome-Based Early Cancer Detection

Extracellular vesicles (EVs), including exosomes, are nanometer-scale vesicles (30–150 nm) secreted by cells into bodily fluids. They carry proteins, nucleic acids, and lipids reflective of their cells of origin and play critical roles in intercellular communication. In cancer diagnostics, exosomes are increasingly recognized as non-invasive reservoirs of tumor-specific biomarkers, enabling early detection and longitudinal monitoring of disease progression. Among these biomarkers, tetraspanin proteins such as CD81, CD9, and CD63 are highly enriched on exosomal membranes and are widely used as targets for EV capture and characterization. However, conventional antibody-based assays for detecting these surface markers often suffer from limitations including cross-reactivity, batch variability, and inconsistent binding affinity—challenges that impede the robustness and reproducibility of clinical and high-throughput EV-based diagnostics.

To overcome these limitations, we present an approach to develop high-affinity synthetic peptide ligands that selectively bind to exosomal tetraspanins. Our design pipeline integrates computational and experimental methodologies. We employ Peptide Binding Design (PepBD), a Monte Carlo-based algorithm developed in our lab, to iteratively identify peptide sequences optimized for binding to the extracellular domains of CD81 and CD9. Top candidates, ranked by PepBD scores, are further refined through explicit-solvent molecular dynamics simulations to assess binding stability and interface complementarity. Experimentally, we validate lead peptides using fluorescence ELISA and single-EV imaging, demonstrating that one designed peptide binds CD81 with an affinity comparable to that of a commercial antibody (mean fluorescence intensity correlation, R² = 0.998).

Building upon this platform, we are combining our newly developed tetraspanin-targeting peptides with previously engineered peptide probes for EpCAM—a transmembrane glycoprotein and early-stage cancer biomarker. This combinatorial targeting strategy allows for selective identification of exosomes originating from cancer-derived cells, offering enhanced specificity and sensitivity for early cancer detection.

Our approach represents a scalable modular framework for engineering peptide ligands that target clinically relevant surface proteins with high specificity. By integrating computational design with experimental validation, this work establishes a next-generation platform for molecular diagnostics, targeted detection, and precision biosensing in oncology and beyond.