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Modular Input-Output Biosensor Design Using De Novo Protein Switches
James Wang1, Julie Yi-Hsuan Chen1, Chenggang Xi1
1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, United States.
Abstract:
Protein-based biosensors offer unique advantages over conventional analytical methods by enabling real-time detection of target analytes with minimal sample preparation. However, efficiently coupling molecular recognition to a reliable output signal remains a key challenge in biosensor design. Here, we present a plug-and-play strategy using the de novo switch platform, LOCKR, which enables direct transduction of a binding event into a defined signal output. The LOCKR architecture supports modular reconfiguration: the recognition domain can be swapped to detect a desired analyte, while the reporter module can be interchanged to tune the output format. We expand the range of LOCKR-compatible readouts beyond split luciferase to include ratiometric Förster-type resonance energy transfer and β-lactamase-based colorimetry. By integrating computationally designed high-affinity binders as interchangeable recognition elements, we demonstrate sensitive detection of glucagon, neuropeptide Y, and peptide YY with limits of detection in the picomolar range. Together with the expanding landscape of de novo designed binders and reporters, the LOCKR platform bridges the gap between molecular recognition and signal generation, enabling versatile biosensor development tailored for user-defined applications.
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