2026-07-15 –, University Hall
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.
Polyethylene terephthalate (PET) plastic degradation has emerged as a major environmental challenge. The discovery of PETase, originally identified in Ideonella sakaiensis, opened new possibilities for enzymatic plastic recycling. However, improving PETase stability, activity, and substrate specificity remains an open problem in protein engineering.
In this presentation, we introduce a modular Python workflow designed specifically to engineer improved PETase variants. The workflow integrates molecular modeling tools—including Rosetta, FoldX, and AMBER molecular dynamics simulations—with docking and modern machine learning frameworks (scikit-learn and PyTorch). Rather than relying purely on sequence-based ML, We incorporate simulation-informed descriptors such as:
- Electrostatic potential and catalytic residue environment
- Stability and ΔΔG predictions
- Molecular dynamics–derived flexibility metrics
- Docking scores with PET oligomers
These simulation-derived features are combined with sequence embeddings to predict enzyme activity in an interpretable manner. This enables rational mutation prioritization rather than black-box screening.
Key components include:
1. Data Pipelines
Standardized processing of sequence variants, simulation outputs, structural descriptors, and
docking results in an automated and reproducible workflow.
2. Simulation-Informed Feature Engineering
Integration of structural, dynamic, and energetic descriptors with learned sequence embeddings.
3. Machine Learning Modeling
Cross-validation, uncertainty estimation, and careful evaluation to ensure robust predictive
performance.
4. Interpretability for Engineering
Feature attribution methods to identify which structural or dynamic properties most strongly
influence predicted activity — directly informing mutation strategies.
We demonstrate the workflow by engineering PETase variants with predicted improvements in catalytic efficiency and stability. By integrating docking of PET oligomers, molecular dynamics simulations, and ML prediction, we show how simulation-informed features improve predictive performance compared to sequence-only baselines.
This PETase-focused approach illustrates how tightly integrating physics-based simulations with machine learning enables actionable design decisions.
While PETase is the immediate application, the framework generalizes to other enzyme families, offering a reproducible and extensible foundation for computational protein engineering.
What Attendees Gain
- A concrete PETase engineering case study
- A reproducible Python-based workflow integrating simulations and ML
- Practical strategies for combining docking, MD, and ML
- Methods for interpretable prediction and rational mutation design
- An extensible framework adaptable to other enzyme systems
Sai Sanjana Prakash is an R&D scientist with training in Computer Science and Biomedical Engineering from Georgia Tech. She is passionate about tackling challenges at the intersection of computation, science, and engineering. Her work and interests span the foundations of intelligence, protein design and engineering, and the development of computational tools to advance scientific discovery. Driven by a deep curiosity for complex systems, Sanju focuses on translating insights into innovative, practical solutions.
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!