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Author

Andrew D. Ellington

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Open access Jul 2026

Using enantioselective biosensors to evolve asymmetric biocatalysts.

Biocatalysts are prized for their enantioselectivity, but slow chromatographic separations required to measure enantiomeric excess bottleneck their development. To overcome this limitation, we evolve enantioselective transcription factors (eTFs) that convert enzyme-catalyzed enantiomer concentrations into programmable gene expression outputs, focusing on imine reductases. Here, using a massively parallel reporter assay, we measure dose-response curves for over 300,000 transcription factor variants in response to an imine precursor and chiral amine products. We quantify the sensitivity, selectivity and dynamic range across variants generated by random, site-saturation and shuffling mutagenesis, isolating variants with exceptional specificity. High-resolution structures of evolved eTFs elucidate how steric effects enforce enantioselectivity, while charge interactions distinguish the imine from the amines. Using two eTFs, we create an ultrahigh-throughput chiral screen to evolve an imine reductase with inverted enantioselectivity. To support generalizability and speed, we design a genetic circuit that enables TF generation within weeks. Our methods enable rapid measurement of asymmetric reactions, supporting innovation in chemical manufacturing.

Simon d’Oelsnitz, Wantae Kim, Nicole N Zhao et al. · 6 citations
Open access Aug 2026

Combining Machine Learning and Directed Evolution for Optimization of a Monooxygenase

A systematic comparison of zero-shot ML models is provided and an iterative framework for integrating machine learning with directed evolution to accelerate enzyme engineering is established to accelerate enzyme engineering.

Daniel Gutierrez, Isa Madrigal Harrison, Aaron L. Feller et al. · 0 citations
Open access Aug 2026

Learning from human and chemical languages to predict biological function

PubCheF-1, a deep learning model that predicts literature-derived biological function directly from chemical structure, establishes that machine learning-based prediction of biological function derived from the language of scientific literature allows the identification of bioactive molecules at high hit rates, thereby accelerating therapeutic discovery.

Clayton W. Kosonocky, Nikol Kadeřábková, Kangsan Kim et al. · 0 citations