Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion.

Authors

Yilun Qi, Guoxun Zhang, Klaus Yserentant, Sonya Lee, Artem Lyubimov, Kyrellos Ibrahim, Giuseppe Cimicata, James S Fraser, Bo Huang, Tanja Kortemme

Published in

bioRxiv : the preprint server for biology. Jul 23, 2026. Epub Jul 23, 2026.

Abstract

In engineering, industrial design principles link geometric form with function. In nature, global protein shape, curvature, and chirality similarly drive critical processes like membrane remodeling and cellular motility. However, explicitly programming these global features remains a challenge for generative AI. Here, we integrate computer-aided design (CAD) blueprints with diffusion models, establishing protein shape as an explicit, programmable layer. We engineered diverse, tunable architectures, including single-chain shapes, scaffolds with unusual twist handedness, and superhelical assemblies, structurally validating 31 of 59 designs via X-ray crystallography or electron microscopy. To demonstrate shape-encoded function, we coupled fluid-flow-optimized helical propellers to an F ₁ -ATPase rotor, yielding a prototype ATP-dependent molecular swimmer. This framework translates industrial design principles to the molecular scale, enabling the programmable engineering of biomimetic architecture with emergent functions.
Integrating CAD blueprints with diffusion-based AI translates industrial design principles to the molecular scale, enabling the explicit programming of protein shapes with emergent functions.

PMID:
42539255
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.

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