Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry
- 1California Institute of Technology
Abstract
Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C-H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.
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BibTeX
@misc{li2026designevolution,
title = {{Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry}},
author = {Z.-Q. Li and R. K. Zhang and P.-P. Xie and J. Kelly and Y. Zhang and Z.-Y. Qin and T. Lambert and O. Mukhopadhyay and D. Hanley and A. E. Danson and J. Abramson and S. Singh and Z. Wu and P. Kohli and P. Liu and J. Wang and F. H. Arnold},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.754751},
url = {https://www.biorxiv.org/content/10.64898/2026.10.01.754751v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry
AU - Z.-Q. Li
AU - R. K. Zhang
AU - P.-P. Xie
AU - J. Kelly
AU - Y. Zhang
AU - Z.-Y. Qin
AU - T. Lambert
AU - O. Mukhopadhyay
AU - D. Hanley
AU - A. E. Danson
AU - J. Abramson
AU - S. Singh
AU - Z. Wu
AU - P. Kohli
AU - P. Liu
AU - J. Wang
AU - F. H. Arnold
PY - 2026
DA - 2026/10/01
PB - bioRxiv
DO - 10.64898/2026.10.01.754751
UR - https://www.biorxiv.org/content/10.64898/2026.10.01.754751v1
AB - Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C-H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.
ER -