This project investigates ribozymes, catalytic RNA molecules that link sequence, structure, and chemical function. We combine computational analysis with wet lab experimentation to understand how ribozymes emerge, how they work at the molecular level, and how their activities can be optimized for both fundamental and applied goals.

On the computational side, we integrate RNA secondary and tertiary structure modeling, sequence landscape analysis, and statistical learning to predict active motifs, interpret selection trajectories, and guide the design of improved variants. By iterating between prediction and experiment, we aim to produce mechanistic insight into RNA catalysis while developing ribozymes as engineerable components for biotechnology, including molecular sensing, programmable regulation, and new catalytic functions.

On the experimental side, we build and characterize ribozyme systems using in vitro transcription, targeted mutagenesis, and quantitative kinetic assays to measure reaction rates, substrate scope, and metal ion dependence. We use high throughput selection and sequencing to track functional enrichment across large variant pools, enabling genotype to phenotype maps that reveal which motifs are essential, which positions tolerate change, and how epistatic interactions shape catalytic performance. We also probe ribozyme robustness under different physicochemical regimes, including changes in temperature, hydration state, and ionic composition, to connect function to realistic environmental constraints.