References
-
Libis, V., Delépine, B. & Faulon, J.-L. Sensing new chemicals with bacterial transcription factors. Curr. Opin. Microbiol. 33, 105–112 (2016).
-
Ding, N., Zhou, S. & Deng, Y. Transcription-factor-based biosensor engineering for applications in synthetic biology. ACS Synth. Biol. 10, 911–922 (2021).
-
De Paepe, B., Peters, G., Coussement, P., Maertens, J. & De Mey, M. Tailor-made transcriptional biosensors for optimizing microbial cell factories. J. Ind. Microbiol. Biotechnol. 44, 623–645 (2017).
-
Englund, E. et al. Biosensor guided polyketide synthases engineering for optimization of domain exchange boundaries. Nat. Commun. 14, 4871 (2023).
-
Thavarajah, W. et al. A primer on emerging field-deployable synthetic biology tools for global water quality monitoring. NPJ Clean. Water 3, 18 (2020).
-
Silverman, A. D., Akova, U., Alam, K. K., Jewett, M. C. & Lucks, J. B. Design and optimization of a cell-free atrazine biosensor. ACS Synth. Biol. 9, 671–677 (2020).
-
Corbisier, P. et al. Whole cell- and protein-based biosensors for the detection of bioavailable heavy metals in environmental samples. Anal. Chim. Acta 387, 235–244 (1999).
-
Thavarajah, W. et al. Point-of-use detection of environmental fluoride via a cell-free riboswitch-based biosensor. ACS Synth. Biol. 9, 10–18 (2019).
-
Jung, J. K. et al. Cell-free biosensors for rapid detection of water contaminants. Nat. Biotechnol. 38, 1451–1459 (2020).
-
Cao, J. et al. Harnessing a previously unidentified capability of bacterial allosteric transcription factors for sensing diverse small molecules in vitro. Sci. Adv. 4, eaau4602 (2018).
-
Grazon, C. et al. A progesterone biosensor derived from microbial screening. Nat. Commun. 11, 1276 (2020).
-
Taylor, N. D. et al. Engineering an allosteric transcription factor to respond to new ligands. Nat. Methods 13, 177–183 (2015).
-
Voyvodic, P. L. et al. Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors. Nat. Commun. 10, 1697 (2019).
-
Pardee, K. et al. Paper-based synthetic gene networks. Cell 159, 940–954 (2014).
-
Pardee, K. et al. Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell 165, 1255–1266 (2016).
-
Nguyen, P. Q. et al. Wearable materials with embedded synthetic biology sensors for biomolecule detection. Nat. Biotechnol. 39, 1366–1374 (2021).
-
Hossain, G. S., Saini, M., Miyake, R., Ling, H. & Chang, M. W. Genetic biosensor design for natural product biosynthesis in microorganisms. Trends Biotechnol. 38, 797–810 (2020).
-
Landry, B. P., Palanki, R., Dyulgyarov, N., Hartsough, L. A. & Tabor, J. J. Phosphatase activity tunes two-component system sensor detection threshold. Nat. Commun. 9, 1433 (2018).
-
Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2018).
-
Chemla, Y. et al. Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02622-y (2025).
-
Wen, K. Y. et al. A cell-free biosensor for detecting quorum sensing molecules in P. aeruginosa-infected respiratory samples. ACS Synth. Biol. 6, 2293–2301 (2017).
-
Boyd, M. A., Thavarajah, W., Lucks, J. B. & Kamat, N. P. Robust and tunable performance of a cell-free biosensor encapsulated in lipid vesicles. Sci. Adv. 9, eadd6605 (2023).
-
Gambill, L., Staubus, A., Mo, K. W., Ameruoso, A. & Chappell, J. A split ribozyme that links detection of a native RNA to orthogonal protein outputs. Nat. Commun. 14, 543 (2023).
-
McSweeney, M. A. et al. A modular cell-free protein biosensor platform using split T7 RNA polymerase. Sci. Adv. 11, eado6280 (2025).
-
Lubkowicz, D. et al. Reprogramming probiotic Lactobacillus reuteri as a biosensor for Saphylococcus aureus derived AIP-I detection. ACS Synth. Biol. 7, 1229–1237 (2018).
-
Leander, M., Yuan, Y., Meger, A., Cui, Q. & Raman, S. Functional plasticity and evolutionary adaptation of allosteric regulation. Proc. Natl. Acad. Sci. USA 117, 25445–25454 (2020).
-
Süel, G. M. et al. Evolutionarily conserved networks of residues mediate allosteric communication in proteins. Nat. Struct. Biol. 10, 59–69 (2002).
-
Nishikawa, K. K. et al. Highly multiplexed design of an allosteric transcription factor to sense new ligands. Nat. Commun. 15, 10001 (2024).
-
d’Oelsnitz, S. et al. Using fungible biosensors to evolve improved alkaloid biosyntheses. Nat. Chem. Biol. 18, 981–989 (2022).
-
F. M. Machado, L., Currin, A. & Dixon, N. Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes. J. Biol. Eng. 13, 91 (2019).
-
Yang, J. et al. Active learning-assisted directed evolution. Nat. Commun. 16, 714 (2025).
-
Vidal, L. S., Isalan, M., Heap, J. T. & Ledesma-Amaro, R. A primer to directed evolution: current methodologies and future directions. RSC Chem. Biol. 4, 271–291 (2023).
-
Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl. Acad. Sci. USA 116, 8852–8858 (2019).
-
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M. & Church, G. M. Low-N protein engineering with data-efficient deep learning. Nat. Methods 18, 389–396 (2021).
-
Qiu, Y., Hu, J. & Wei, G.-W. Cluster learning-assisted directed evolution. Nat. Comput. Sci. 1, 809–818 (2021).
-
Zhang, Q. et al. Integrating protein language models and automatic biofoundry for enhanced protein evolution. Nat. Commun. 16, 1553 (2025).
-
Huang, C. et al. Application of directed evolution and machine learning to enhance the diastereoselectivity of ketoreductase for dihydrotetrabenazine synthesis. JACS Au 4, 2547–2556 (2024).
-
Lobzaev, E., Herrera, M. A., Kasprzyk, M. & Stracquadanio, G. Protein engineering using variational free energy approximation. Nat. Commun. 15, 10447 (2024).
-
Hie, B., Bryson, B. D. & Berger, B. Leveraging uncertainty in machine learning accelerates biological discovery and design. Cell Syst. 11, 461–477 (2020).
-
Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).
-
Chen, B. et al. xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins. Nat. Methods 22, 1028–1039 (2025).
-
Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348 (2022).
-
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: Exploring the boundaries of protein language models. Cell Syst. 14, 968–978 (2023).
-
Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).
-
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
-
Hie, B. L. et al. Efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. 42, 275–283 (2023).
-
Yang, J., Li, F.-Z. & Arnold, F. H. Opportunities and challenges for machine learning-assisted enzyme engineering. ACS Cent. Sci. 10, 226–241 (2024).
-
Saito, Y. et al. Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration. ACS Catal. 11, 14615–14624 (2021).
-
Landwehr, G. M. et al. Accelerated enzyme engineering by machine-learning guided cell-free expression. Nat. Commun. 16, 865 (2025).
-
Ding, K. et al. Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering. Nat. Commun. 15, 6392 (2024).
-
Kim, G. B., Gao, Y., Palsson, B. O. & Lee, S. Y. DeepTFactor: A deep learning-based tool for the prediction of transcription factors. Proc. Natl. Acad. Sci. USA 118, e2021171118 (2021).
-
Zeng, W., Dou, Y., Pan, L., Xu, L. & Peng, S. Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein. Nat. Commun. 15, 7838 (2024).
-
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).
-
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
-
Sumida, K. H. et al. Improving protein expression, stability, and function with proteinMPNN. J. Am. Chem. Soc. 146, 2054-2061 (2024).
-
Khersonsky, O. et al. Automated design of efficient and functionally diverse enzyme repertoires. Mol. Cell 72, 178–186 (2018).
-
Widatalla, T., Rafailov, R. & Hie, B. Aligning protein generative models with experimental fitness via Direct Preference Optimization. Preprint at https://doi.org/10.1101/2024.05.20.595026 (2024).
-
Padmakumar, V., Pang, R. Y., He, H. & Parikh, A. P. Extrapolative Controlled Sequence Generation via Iterative Refinement. Preprint at https://doi.org/10.48550/arXiv.2303.04562 (2023).
-
Yang, Z. et al. Does negative sampling matter? a review with insights into its theory and applications. IEEE Trans. Pattern Anal. Mach. Intell. 46, 5692–5711 (2024).
-
Carlson, E. D., Gan, R., Hodgman, C. E. & Jewett, M. C. Cell-free protein synthesis: Applications come of age. Biotechnol. Adv. 30, 1185–1194 (2012).
-
Silverman, A. D., Karim, A. S. & Jewett, M. C. Cell-free gene expression: an expanded repertoire of applications. Nat. Rev. Genet. 21, 151–170 (2019).
-
Hunt, A. C. et al. Cell-free gene expression: methods and applications. Chem. Rev. 125, 91–149 (2024).
-
Ekas, H. M. et al. An automated cell-free workflow for transcription factor engineering. ACS Synth. Biol. 13, 3389–3399 (2024).
-
Ekas, H. M. et al. Engineering a PbrR-based biosensor for cell-free detection of lead at the legal limit. ACS Synth. Biol. 13, 3003–3012 (2024).
-
Monchy, S. B. et al. Plasmids pMOL28 and pMOL30 of Cupriavidus metallidurans are specialized in the maximal viable response to heavy metals. J. Bacteriol. 189, 7417–7425 (2007).
-
Jarvis, P. & Fawell, J. Lead in drinking water – An ongoing public health concern? Curr. Opin. Environ. Sci. Health 20, 100239 (2021).
-
Zietz, B. P., Laß, J., Suchenwirth, R. & Dunkelberg, H. Lead in drinking water as a public health challenge. Environ. Health Perspect. 118, a154–a155 (2010).
-
WHO. Lead poisoning, https://www.who.int/news-room/fact-sheets/detail/lead-poisoning-and-health (2024).
-
EPA. Lead Service Lines, https://www.epa.gov/ground-water-and-drinking-water/lead-service-lines (2025).
-
Borremans, B., Hobman, J. L., Provoost, A., Brown, N. L. & Lelie, D. vd Cloning and Functional Analysis of the pbr Lead Resistance Determinant of Ralstonia metallidurans CH34. J. Bacteriol. 183, 5651–5658 (2001).
-
EPA. Basic Information about Lead in Drinking Water, https://www.epa.gov/ground-water-and-drinking-water/basic-information-about-lead-drinking-water (2025).
-
Jia, X., Ma, Y., Bu, R., Zhao, T. & Wu, K. Directed evolution of a transcription factor PbrR to improve lead selectivity and reduce zinc interference through dual selection. AMB Express 10, 67 (2020).
-
EPA. Drinking Water Regulations and Contaminants, https://www.epa.gov/sdwa/drinking-water-regulations-and-contaminants (2025).
-
Liu, X. et al. Design of a transcriptional biosensor for the portable, on-demand detection of cyanuric acid. ACS Synth. Biol. 9, 84–94 (2019).
-
Thavarajah, W. et al. The accuracy and usability of point-of-use fluoride biosensors in rural Kenya. npj Clean. Water 6, 5 (2023).
-
Maret, W. & Li, Y. Coordination dynamics of Zinc in Proteins. Chem. Rev. 109, 4682–4707 (2009).
-
Cangelosi, V., Ruckthong, L. & Pecoraro, V. L. Lead: Its Effects on Environment and Health Ch.10 (De Gruyter, Berlin, 2017).
-
Aravind, L., Anantharaman, V., Balaji, S., Babu, M. M. & Iyer, L. M. The many faces of the helix-turn-helix domain: Transcription regulation and beyond. FEMS Microbiol. Rev. 29, 231–262 (2005).
-
Hastings, R., Aditham, A. K., DelRosso, N., Suzuki, P. H. & Fordyce, P. M. Mutations to transcription factor MAX allosterically increase DNA selectivity by altering folding and binding pathways. Nat. Commun. 16, 636 (2025).
-
Warfel, K. F. et al. A low-cost, thermostable, cell-free protein synthesis platform for on-demand production of conjugate vaccines. ACS Synth. Biol. 12, 95–107 (2022).
-
Stark, J. C. et al. On-demand biomanufacturing of protective conjugate vaccines. Sci. Adv. 7, eabe9444 (2021).
-
Pardee, K. et al. Portable, on-demand biomolecular manufacturing. Cell 167, 248–259.e212 (2016).
-
Collins, M. et al. A frugal CRISPR kit for equitable and accessible education in gene editing and synthetic biology. Nat. Commun. 15, 6563 (2024).
-
Jung, J. K. et al. At-home, cell-free synthetic biology education modules for transcriptional regulation and environmental water quality monitoring. ACS Synth. Biol. 12, 2909–2921 (2023).
-
Stark, J. C. et al. BioBits™ Bright: A fluorescent synthetic biology education kit. Sci. Adv. 4, eaat5107 (2018).
-
Huang, A. et al. BioBits™ Explorer: A modular synthetic biology education kit. Sci. Adv. 4, eaat5105 (2018).
-
Elnaggar, A. et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127 (2022).
-
Post, M. A Call for Clarity in Reporting BLEU Scores. Proc. Third Conf. Mach. Transl. 1, 186–191 (2018).
-
Hu, E. J. et al. LoRA: Low-Rank Adaptation of Large Language Models. Preprint at https://doi.org/10.48550/arXiv.2106.09685 (2021).
-
Silverman, A. D., Kelley-Loughnane, N., Lucks, J. B. & Jewett, M. C. Deconstructing cell-free extract preparation for in vitro activation of transcriptional genetic circuitry. ACS Synth. Biol. 8, 403–414 (2018).
-
Stark, J. C. et al. Rapid biosynthesis of glycoprotein therapeutics and vaccines from freeze-dried bacterial cell lysates. Nat. Protoc. 18, 2374–2398 (2023).
-
Kwon, Y.-C. & Jewett, M. C. High-throughput preparation methods of crude extract for robust cell-free protein synthesis. Sci. Rep. 5, 8663 (2015).
-
Jewett, M. C. & Swartz, J. R. Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnol. Bioeng. 86, 19–26 (2004).
-
Jewett, M. C. & Swartz, J. R. Substrate replenishment extends protein synthesis with an in vitro translation system designed to mimic the cytoplasm. Biotechnol. Bioeng. 87, 465–471 (2004).
-
Jewett, M. C., Calhoun, K. A., Voloshin, A., Wuu, J. J. & Swartz, J. R. An integrated cell-free metabolic platform for protein production and synthetic biology. Mol. Syst. Biol. 4, 51–59 (2008).
-
E. P. A. Method 200.8: Determination of Trace Elements in Waters and Wastes by Inductively Coupled Plasma-Mass Spectrometry. (Cincinnati, OH, 1994).
-
Yeghicheyan, D. et al. Collaborative determination of trace element mass fractions and isotope ratios in AQUA-1 drinking water certified reference material. Anal. Bioanal. Chem. 413, 4959–4978 (2021).
-
Meng, E. C. et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci. 32, e4792 (2023).
-
Discovery, C. et al. Chai-1: Decoding the molecular interactions of life. Preprint at https://doi.org/10.1101/2024.10.10.615955 (2024).
-
Wang, B. M. et al. Active learning-guided optimization of cell-free biosensors for lead testing in drinking water. Zenodo. https://doi.org/10.5281/zenodo.17351710 (2025).
