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<p>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.</p>

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Small-molecule binding and sensing with a designed protein family

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月28日

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  • 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

摘要

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.