B. M. Wang, N. Chiang, H. M. Ekas, D. M. Brown, G. Dildine, T. J. Lucci, S. Feng, V. Bly, J.F. Gaillard, J. B. Lucks, A. S. Karim, D. Shukla, M. C. Jewett. Nature Communications (2025). doi: 10.1038/s41467-025-66964-6
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This study presents a multi-objective, machine learning–guided cell-free engineering workflow to optimize allosteric transcription factor (aTF)-based biosensors beyond the limits of traditional directed evolution. Using this approach, we engineered the aTF PbrR to sensitively and selectively detect lead at U.S. EPA action levels in freeze-dried cell-free reactions, enabling point-of-use water diagnostics down to ~5.7 ppb.
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