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Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring
Summary
Researchers built a computational pipeline—combining protein simulation, synthetic biology, and machine learning—to design a biosensor capable of detecting micro- and nanoplastics in the environment. The resulting electrochemical sensor reduced measurement error by over 92% compared to unmodified devices and overcame the challenge of variability between individual sensors. Faster, cheaper, and more reliable detection tools are essential for understanding how widely microplastics have spread and for enforcing future pollution limits.
The intelligent discovery of novel biosensors is often hampered by slow, trial‐and‐error experimental cycles. To overcome this gap, we introduce and validate a computationally‐guided discovery pipeline that synergizes molecular simulation, synthetic biology, electrochemical engineering, and machine learning guided analysis for the rational design of high‐performance sensors. We demonstrate the power of this approach by tackling the urgent challenge of detecting micro‐ and nanoplastics (MNPs), which are potential harmful agents, causing respiratory, cardiovascular, and oncological disorders. Our pipeline begins with computational screening, using molecular dynamics simulations to evaluate the thermal stability and binding affinity of a candidate protein for recognition element, the carbohydrate‐binding module of Bacillus anthracis ( Ba CBM2). Building upon the positive computational results, the protein was synthesized and integrated onto a custom‐fabricated electrochemical platform. The computationally‐informed protein was experimentally verified to have a superior analytical performance when covalently tethered to gold surfaces of on‐chip electrodes, which acted as label‐free electrochemical biosensors. To date, this system implied reductions in the root mean square error in MNP quantification and measurement standard deviation by 92.24% and 24.83%, respectively, compared to unmodified devices, and its integration with multivariate analysis (Sure Independence Screening and Sparsifying Operator) overcame device‐to‐device variability. This work not only delivers a promising biosensor for MNP monitoring, but, more importantly, establishes a validated workflow for the intelligent discovery of functional and specific proteins. This pipeline provides a direct pathway for future integration with high‐throughput virtual screening and machine learning models, enabling the inverse design of next‐generation environmental and diagnostic sensor technologies.