Small-molecule binding and sensing with a designed protein family
- Gyu Rie Lee
- Samuel J. Pellock
- Christoffer Norn
- Doug Tischer
- Justas Dauparas
- Ivan Anishchenko
- Jaron A. M. Mercer
- Alex Kang
- Asim K. Bera
- Hannah Nguyen
- Evans Brackenbrough
- Banumathi Sankaran
- Inna Goreshnik
- Dionne Vafeados
- Nicole Roullier
- Hannah L. Han
- Brian Coventry
- Hugh K. Haddox
- David R. Liu
- Andy Hsien-Wei Yeh
- David Baker
2026-03-28
The de novo design of small-molecule–binding proteins holds great promise as a potential tool to develop sensors on-demand for arbitrary small molecules. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six small-molecule targets. Biophysical characterization of the designed binders reveals nanomolar to low micromolar binding affinities and atomic-level design accuracy. Additionally, we use a cortisol binder to design a chemically induced dimerization (CID) system that enables the construction of a biosensor for cortisol detection. The approach described here demonstrates the potential of the NTF2 fold and deep learning-based protein design in sensor development, paving the way for future platforms to design binders and sensors for small molecules across analytical, environmental, and biomedical applications.