SciPy 2026

Engineering enzymes with improved catalytic activity remains a central challenge in biotechnology. In this research, we focus on engineering PETase, a plastic-degrading enzyme, as a testbed for developing a simulation-informed machine learning workflow. We present a Python framework that integrates molecular simulations, docking, and structural analysis with modern machine learning methods to predict enzyme activity from sequence and structure. By combining simulation-derived descriptors—including active-site geometry, electrostatics, stability metrics, dynamics, and docking scores—with sequence embeddings, we generate interpretable predictions that guide rational mutation strategies. While developed for PETase engineering, the workflow is extensible to broader de novo enzyme design efforts.