Justin Kashi
Hi there! I did my Bachelor in Bioengineering at McGill University (2019-2023), after which I did my Master's thesis in Synthetic Biology & Systems Biology in the Ignea Lab at McGill University (2023-2025) where I studied transcriptomics and metabolomics in Tacca plant species. I fell in love with foundational protein language models and modeling enzymes functions using structural and sequence information. After participating in the Align Bio 2025 PETase protein engineering tournament in Fall 2025 with my teammates, we developed a protein engineering framework using our methodology which we are presenting at the SciPy 2026 conference!
Session
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.