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April 17, 2026Nature Biotechnology2 citationsOpen Access

Artificial allosteric protein switches with machine-learning-designed receptors

ZGZhong GuoOSOleh SmutokGLGyu Rie Lee

Key Points

  • The study aims to create artificial allosteric proteins using machine-learning designed receptors.
  • Developed minimal ligand-binding domains as receptors in single-component allosteric switches.
  • Utilized hydrogen/deuterium exchange mass spectrometry for analysis.
  • Employed 19F nuclear magnetic resonance for examining ligand binding effects.
  • Engineered synthetic allosteric switches with artificial receptor and reporter domains.
  • Successful demonstration of colorimetric, luminescent, and electrochemical biosensors.
  • Increased catalytic activity in the reporter domain following ligand binding.
  • Created fully synthetic allosteric switches for practical applications.
  • Engineered E. coli cells exhibited steroid-dependent antibiotic resistance.

Abstract

Abstract Protein allostery underlies most information and energy processing in biology and the development of artificial allosteric proteins is a key objective of synthetic biology and biotechnology. We show that machine-learning-engineered minimal ligand-binding domains act as efficient receptors in single-component allosteric switches, despite lacking global conformational change. Such colorimetric, luminescent and electrochemical biosensors of small molecules, peptides and proteins can be compiled into intramolecular YES and AND logic gates. Furthermore, we report fully synthetic allosteric switches composed of artificial receptor and reporter domains. Hydrogen/deuterium exchange mass spectrometry and 19 F nuclear magnetic resonance analyses suggest that ligand binding reduces the conformation entropy of the system, increasing the catalytic activity of the reporter domain. The potential practical utility of this approach is demonstrated by engineering Escherichia coli cells with steroid-dependent antibiotic resistance and by developing bioelectronic devices capable of quantifying steroid hormones.

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Cite This Study

Guo et al. (2026) studied this question.

synapsesocial.com/papers/69e1cf375cdc762e9d858328https://doi.org/10.1038/s41587-026-03081-9
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