Research

Expanding the synthetic biology toolbox one enzyme at a time…

As emerging technologies increasingly outpace the functional limits of natural enzymes, there is a growing demand for designer enzymes with highly specialized capabilities. The Chaput lab at the University of California, Irvine is working to address this problem by developing polymerases and other DNA modifying enzymes with custom activities for next-generation applications in synthetic biology and molecular medicine.

The central mission of our lab is to expand the synthetic biology toolbox by creating enzymes that enable the replication of alternative genetic polymers (XNAs), while improving the performance and compatibility of existing enzymes.

To achieve this goal, we combine directed evolution with advanced microfluidic technologies that operate in picoliter-scale water-in-oil (w/o) droplets. Our homebuilt droplet sorting instrument can interrogate up to 10⁸ enzyme variants per day while consuming a million-fold less material than traditional plate-based screening assays. This extraordinary throughput enables the discovery of rare, long-range epistatic mutations that remain inaccessible to computational design approaches and lower throughput screening methods.

The enzymes produced by our laboratory unlock a wide range of downstream applications, including diagnostics, therapeutics, affinity reagents, and information storage systems. Representative examples include biologically stable aptamers, allele-specific gene silencing reagents, and highly sensitive diagnostic platforms capable of detecting, genotyping, and quantifying viral pathogens. We have also developed a biologically safe, soft material for low energy, high-density data archiving.

A defining feature of our research is the ability to reconstruct and analyze evolutionary trajectories using complementary structural and biochemical approaches. These studies provide a mechanistic view of the evolutionary forces that drive the emergence of new enzyme functions, illuminating pathways that guide proteins toward productive regions of the fitness landscape. We are using this approach to reveal the extent of structural innovation required to achieve fundamentally new biochemical activities. Over time, such data will inform AI-based enzyme design approaches by providing experimentally validated solutions that remain opaque to current algorithms.

By creating enzymes that expand the chemical space of nucleic acids, our work lays the foundation for molecular systems that transcend the constraints of natural biology.