Selinger Allison J, Hof Fraser
Department of Chemistry, University of Victoria, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada.
Centre for Advanced Materials and Related Technology (CAMTEC), University of Victoria, 3800 Finnerty Rd., Victoria, BC V8W 2Y2, Canada.
Angew Chem Int Ed Engl. 2023 Nov 6;62(45):e202312407. doi: 10.1002/anie.202312407. Epub 2023 Sep 27.
Molecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one-pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross-assembling sensor elements with diverse analyte-binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor-sensor and sensor-analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data-rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.
超分子传感器的分子区分通常通过传感器阵列来实现，这依赖于大量分离传感元件的模式识别响应。在此，我们报告一种用于生物溶液中差异传感的新型一锅法系统化学方法。我们构建了一个由三个具有不同分析物结合和光物理性质的交叉组装传感器元件组成的自适应网络。这种强大的传感方法利用了复杂的相互连接的传感器 - 传感器和传感器 - 分析物平衡，产生每种分析物独特的超分子和光物理响应。我们表征了自适应网络对分析物作出响应的基本机制。自适应网络固有的丰富数据响应能够区分非常相似的蛋白质和蛋白质混合物，而无需依赖设计的蛋白质识别元件。我们表明，与由相同组件构建的传感器阵列相比，单一的自适应传感溶液使用更少的响应观测就能提供更好的分析物区分。我们还展示了该网络随时间适应并响应不断变化的生物溶液的能力。
