Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Department of Materials Science & Engineering & Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Sensors (Basel). 2021 Sep 22;21(19):6335. doi: 10.3390/s21196335.
A longstanding challenge for accurate sensing of biomolecules such as proteins concerns specifically detecting a target analyte in a complex sample (e.g., food) without suffering from nonspecific binding or interactions from the target itself or other analytes present in the sample. Every sensor suffers from this fundamental drawback, which limits its sensitivity, specificity, and longevity. Existing efforts to improve signal-to-noise ratio involve introducing additional steps to reduce nonspecific binding, which increases the cost of the sensor. Conducting polymer-based chemiresistive biosensors can be mechanically flexible, are inexpensive, label-free, and capable of detecting specific biomolecules in complex samples without purification steps, making them very versatile. In this paper, a poly (3,4-ethylenedioxyphene) (PEDOT) and poly (3-thiopheneethanol) (3TE) interpenetrating network on polypropylene-cellulose fabric is used as a platform for a chemiresistive biosensor, and the specific and nonspecific binding events are studied using the Biotin/Avidin and Gliadin/G12-specific complementary binding pairs. We observed that specific binding between these pairs results in a negative Δ with the addition of the analyte and this response increases with increasing analyte concentration. Nonspecific binding was found to have the opposite response, a positive Δ upon the addition of analyte was seen in nonspecific binding cases. We further demonstrate the ability of the sensor to detect a targeted protein in a dual-protein analyte solution. The machine-learning classifier, random forest, predicted the presence of Biotin with 75% accuracy in dual-analyte solutions. This capability of distinguishing between specific and nonspecific binding can be a step towards solving the problem of false positives or false negatives to which all biosensors are susceptible.
长期以来，生物分子（如蛋白质）的准确传感一直面临一个挑战，特别是在复杂样品（如食物）中检测目标分析物时，而不会受到目标本身或样品中其他分析物的非特异性结合或相互作用的影响。每个传感器都存在这种基本缺陷，这限制了其灵敏度、特异性和寿命。为了提高信噪比，现有研究努力涉及引入额外的步骤来减少非特异性结合，这增加了传感器的成本。基于导电聚合物的化学电阻式生物传感器具有机械灵活性、成本低廉、无需标记且能够在无需纯化步骤的情况下检测复杂样品中的特定生物分子，因此非常多样化。在本文中，聚（3,4-亚乙基二氧噻吩）（PEDOT）和聚（3-噻吩乙醇）（3TE）在聚丙烯-纤维素织物上的互穿网络被用作化学电阻式生物传感器的平台，并且使用生物素/亲和素和麦醇溶蛋白/ G12 特异性互补结合对来研究特异性和非特异性结合事件。我们观察到，这些配对之间的特异性结合导致在加入分析物时产生负的Δ，并且该响应随着分析物浓度的增加而增加。非特异性结合的情况则相反，在非特异性结合的情况下，在加入分析物时会观察到正的Δ。我们进一步证明了传感器在双蛋白分析物溶液中检测靶向蛋白的能力。机器学习分类器随机森林以 75%的准确率预测双分析物溶液中生物素的存在。这种区分特异性和非特异性结合的能力可能是解决所有生物传感器都容易受到的假阳性或假阴性问题的一个步骤。
