PetaScale

Plastic pollution affects consumers, municipalities, and industries worldwide, yet no existing technology can efficiently degrade plastics under the diverse temperatures, pH levels, and environmental conditions found in real‑world waste streams. Current PETase engineering relies on slow, low‑throughput assays and limited datasets, preventing the development of robust enzymes that can be deployed at scale. This project addresses that gap by building a high‑throughput wet‑lab pipeline capable of testing hundreds of enzyme variants, generating the large datasets needed to train novel machine‑learning architectures for enzyme prediction and optimization.
Complementary in silico directed evolution will accelerate the discovery of high‑performance variants tailored to specific industrial environments. The final deliverable is a complete biodegradation solution: a predictive computational model, optimized enzymes, and hardware such as bioreactors or filtration modules for integration into recycling facilities, wastewater treatment plants, and environmental cleanup operations.
Beyond PETases, the platform is designed to generalize to any protein family, enabling a long‑term business model centered on scalable enzyme engineering. With mentorship from the RNA Lab, the project is positioned to generate both scientific impact and commercially viable biotechnology.