Seminar
01/07/2026
Multiscale Computational Enzymology for Nucleic Acid Enzyme Design

12.00pm, Seminar Room

Prof. Darrin M. York

(Rutgers University, United States of America)

Nucleic acid enzymes, including ribozymes, DNAzymes, and XNAzymes, catalyze diverse chemical reactions and have emerged as powerful platforms for biotechnology, therapeutics, and synthetic biology. Studies of naturally occurring ribozymes have revealed fundamental catalytic strategies based on conformational dynamics, metal-ion recruitment, and hydrogen-bonding networks, while engineered RNA and DNA enzymes have expanded the range of accessible reactions and applications. At the same time, synthetic xeno-nucleic acids (XNAs) offer opportunities to create next-generation nucleic acid enzymes with enhanced stability, bioavailability, nuclease resistance, and activity in cellular environments.

This talk examines recent advances in our understanding of nucleic acid catalysis through application of a comprehensive computational enzymology approach that combines multiscale quantum and machine learning models and advanced molecular simulation techniques. Natural endonucleolytic ribozymes and engineered RNA and DNA enzymes that catalyze RNA cleavage are first examined. A common L-platform/L-scaffold framework emerges as a unifying structural and mechanistic blueprint for both “G+A” and “G+M” ribozyme classes, including the hairpin, twister, Varkud satellite, hammerhead, pistol, and 8-17dz systems. The framework also provides insight into other catalytic RNAs, including HDV and the alkyl-transfer ribozymes MTR1, SAR, and SAMURI. Finally, the methods explore the mechanism of the self-splicing group I intron ribozyme from Tetrahymena thermophillia. We propose and computationally test design modifications that we predict will enable the accommodation of C(–1) in a crucial wobble-like pair at physiological pH. This design would provide a potential strategy for expanding the sequence space accessible to programmable trans-splicing ribozymes in RNA editing applications. Together, these findings provide a predictive understanding of catalytic mechanisms and establish transferable design principles that can guide the rational engineering of new RNA, DNA, and XNA enzymes.