ABSTRACT:
The de novo design of artificial enzymes with
customized catalytic functions represents a long-standing challenge in
synthetic biology. Recent breakthroughs in deep learning, particularly the rise of Generative Artificial Intelligence (GAI), have
transformed enzyme design from structure-centric strategies toward
function-oriented paradigms. This review outlines the emerging computational
frameworks that now span the entire design pipeline, including active site
design, backbone generation, inverse folding, and virtual screening. Detailed description of active site, called a theozyme, is
designed to stabilize transition states and can be guided by density functional
theory (DFT) calculations that define the geometry of key catalytic components. Guided by the theozyme, GAI approaches such as diffusion and flow-matching
models enable the generation of protein backbones pre-configured for catalysis.
Inverse folding methods, exemplified by ProteinMPNN and LigandMPNN, further incorporate
atomic-level constraints to optimize sequence–function compatibility. To assess
and optimize catalytic performance, virtual screening platforms such as PLACER
allow evaluation of protein–ligand conformational dynamics under catalytically
relevant conditions. Through representative case studies, we illustrate how
GAI-driven frameworks facilitate the rational creation of artificial enzymes
with architectures distinct from natural homologs, thereby enabling catalytic
activities not observed in nature. With the rapid progress and widespread
adoption of GAI, we anticipate that de novo enzyme design with
customized catalytic functions will soon evolve into a mature and broadly
applicable methodology.
Qi X, Wang D, Shi Z, Liao X, Ma H. Generative Artificial
Intelligence for Function-Driven De Novo Enzyme Design. Synthetic Biology and Engineering2025, 3, 10015. https://doi.org/10.70322/sbe.2025.10015
AMA Style
Qi X, Wang D, Shi Z, Liao X, Ma H. Generative Artificial
Intelligence for Function-Driven De Novo Enzyme Design. Synthetic Biology and Engineering. 2025; 3(3):10015. https://doi.org/10.70322/sbe.2025.10015