Small-molecule binding and sensing with a designed protein family

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.

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Publication Details

Journal
Nature Communications
Published
2026-03-28
DOI
https://doi.org/10.1038/s41467-026-70953-8
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
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article

Small-molecule binding and sensing with a designed protein family

Hannah L. Han, Justas Dauparas, Asim K. Bera, Doug Tischer et al.
Nature Communications
Protein Degradation and Inhibitors
article

Small-molecule binding and sensing with a designed protein family

Hannah L. Han, Justas Dauparas, Asim K. Bera, Doug Tischer, Dionne Vafeados, Jaron A. M. Mercer, Christoffer Norn, Banumathi Sankaran, Ivan Anishchenko, Gyu Rie Lee, Hannah Nguyen, Hsien‐Wei Yeh, Nicole Roullier, Inna Goreshnik, Hugh K. Haddox, David R. Liu, Evans Brackenbrough, David Baker, Samuel J. Pellock, Brian Coventry, Alex Kang
article en

Abstract

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.

Nature Communications
Broad Institute (US), Howard Hughes Medical Institute (US), Harvard University (US), Lawrence Berkeley National Laboratory (US), Korea Advanced Institute of Science and Technology (KR), University of California, Santa Cruz (US), University of Washington (US), PDL BioPharma (United States) (US)
Life in Land
Openalex Percentile: Top 4%
Protein Degradation and Inhibitors
DOIOpen Access PDFOpenAlex
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