Complex enzyme active site scaffolding by iteratively detuned catalytic guidance
- 1Institute for Protein Design, University of Washington, Seattle, WA 98105, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, US
Abstract
De novo enzyme design requires sequences whose structures reproduce prescribed catalytic geometries. This remains challenging for active sites that couple multiple catalytic residues, cofactors and substrates. Here we introduce the Catalytic Guidance Engine (CaGE), which optimizes designed sequences to satisfy complex sets of functional constraints by alternating constraint guided structure prediction trajectories with sequence design, gradually fading the strength of the constraint information over cycles to nudge the designed sequence towards satisfying all the constraints. We train RF3 to accept atom-pair distances and other design conditions, and use classifier-free guidance to fade their influence over multiple design cycles. We also develop training-free motif guidance protocols which operate at inference time by directly adjusting selected atoms towards the design target geometry during diffusion sampling. CaGE improves catalytic-motif geometry across a diverse enzyme benchmark and yields sequences that recover complex target active sites in independent AF3 predictions. We experimentally demonstrate the power of CaGE by designing active enzymes with complex catalytic sites: a nonheme-iron design positions four active-site residues around an iron centre and a transition-state ligand, and a PLP-dependent design positions four catalytic residues around a PLP cofactor with multiple functional groups. Active site substitutions and a 2.7[A] crystal structure support the designed mechanisms and structures. Thus, CaGE provides a route to functional de novo enzymes with complex active sites.
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BibTeX
@misc{qian2026complex,
title = {{Complex enzyme active site scaffolding by iteratively detuned catalytic guidance}},
author = {T. Qian and W. Wang and S. Gao and A. Muraishi and B. Zhao and D. N. An and F. Xue and M. Kazman and J. Mendoza and H. Nguyen and A. Bera and A. Kang and A. Yang and W. Dai and J. Liang and Y. Wang and X. Huang and F. DiMaio and D. Baker},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.02.756307},
url = {https://www.biorxiv.org/content/10.64898/2026.10.02.756307v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Complex enzyme active site scaffolding by iteratively detuned catalytic guidance
AU - T. Qian
AU - W. Wang
AU - S. Gao
AU - A. Muraishi
AU - B. Zhao
AU - D. N. An
AU - F. Xue
AU - M. Kazman
AU - J. Mendoza
AU - H. Nguyen
AU - A. Bera
AU - A. Kang
AU - A. Yang
AU - W. Dai
AU - J. Liang
AU - Y. Wang
AU - X. Huang
AU - F. DiMaio
AU - D. Baker
PY - 2026
DA - 2026/10/02
PB - bioRxiv
DO - 10.64898/2026.10.02.756307
UR - https://www.biorxiv.org/content/10.64898/2026.10.02.756307v1
AB - De novo enzyme design requires sequences whose structures reproduce prescribed catalytic geometries. This remains challenging for active sites that couple multiple catalytic residues, cofactors and substrates. Here we introduce the Catalytic Guidance Engine (CaGE), which optimizes designed sequences to satisfy complex sets of functional constraints by alternating constraint guided structure prediction trajectories with sequence design, gradually fading the strength of the constraint information over cycles to nudge the designed sequence towards satisfying all the constraints. We train RF3 to accept atom-pair distances and other design conditions, and use classifier-free guidance to fade their influence over multiple design cycles. We also develop training-free motif guidance protocols which operate at inference time by directly adjusting selected atoms towards the design target geometry during diffusion sampling. CaGE improves catalytic-motif geometry across a diverse enzyme benchmark and yields sequences that recover complex target active sites in independent AF3 predictions. We experimentally demonstrate the power of CaGE by designing active enzymes with complex catalytic sites: a nonheme-iron design positions four active-site residues around an iron centre and a transition-state ligand, and a PLP-dependent design positions four catalytic residues around a PLP cofactor with multiple functional groups. Active site substitutions and a 2.7[A] crystal structure support the designed mechanisms and structures. Thus, CaGE provides a route to functional de novo enzymes with complex active sites.
ER -