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Electrochemical aptamer-based (E-AB) biosensors offer a promising platform for reagentless detection of molecular targets, yet aptamer recognition can be limited by cross-reactivity, particularly for hydrophobic analytes such as steroid hormones. To investigate how cross-reactivity influences E-AB sensor performance, we use automation and machine learning to screen a library of possible interferent molecules against a steroid-binding aptamer, with progesterone serving as a physiologically relevant test case. Here, we develop a label-free E-AB sensor for progesterone detection using a methylene blue-modified aptamer anchored with a hexanethiol linker. We then used an automated electrochemistry platform to perform reproducible and high-throughput characterization of our sensor through titration and frequency mapping experiments, identifying optimal frequencies for square-wave voltammetry interrogation. Our automated platform improved experimental throughput by 3-fold that of manual experimentation and greatly improved reproducibility when characterizing our aptamer-modified electrode. Over the course of this work, we collected 20,000 voltammograms demonstrating the high-throughput capability of our platform. To evaluate the specificity of the aptamer sensor, we used our automated platform to screen an interferent scope of 40 structurally and functionally diverse molecules. We used interpretable machine learning to better understand the chemical characteristics of interferent molecules that resulted in sensor cross-reactivity, identifying hydrophobicity as a key molecular descriptor in predicting the aptamer response. We found that the aptamer was cross-reactive toward molecules with hydrophobicities similar to progesterone, irrespective of molecular structure. This cross-reactivity insight is an important consideration for the counterselection process during aptamer design.
Carroll et al. (Wed,) studied this question.