Behera Pradipta, Singh Krishna Kumar, Pandit Subhendu, Saha Diptarka, Saini Deepak Kumar, De Mrinmoy
Department of Organic Chemistry, Indian Institute of Science, Bangalore 560012, India.
Vascular Biology Center, Augusta University, Augusta, Georgia 30912, United States.
ACS Appl Nano Mater. 2021 Apr 2;4(4):3843-3851. doi: 10.1021/acsanm.1c00244. eCollection 2021 Apr 23.
Abnormal concentrations of a specific protein or the presence of some biomarker proteins may indicate life-threatening diseases. Pattern-based detection of specific analytes using affinity-regulated receptors is one of the potential alternatives to specific antigen-antibody-based detection. In this report, we have schemed a sensor array by using various functionalized two-dimensional (2D)-MoS nanosheets and green fluorescent protein (GFP) as the receptor and the signal transducer, respectively. Two-dimensional MoS has been used as a promising candidate for recognition of the bioanalytes because of its high surface-to-volume ratio compared to those of other nanomaterials. Easy surface tunability of this material provides additional advantages to analyze the target of interest. The optimized 2D-MoS-GFP conjugates are able to discriminate 15 different proteins at 50 nM concentration with a detection limit of 1 nM. Moreover, proteins in the binary mixture and in the presence of serum were discriminated successfully. Ten different proteins in serum media at relevant concentrations were classified successfully with 100% jackknifed classification accuracy, which proves the potentiality of the above system. We have also implemented and discussed the implication of using different machine learning models on the pattern recognition problem associated with array-based sensing.
特定蛋白质的异常浓度或某些生物标志物蛋白质的存在可能表明存在危及生命的疾病。使用亲和力调节受体基于模式检测特定分析物是基于特异性抗原-抗体检测的潜在替代方法之一。在本报告中，我们设计了一种传感器阵列，分别使用各种功能化的二维（2D）-MoS纳米片和绿色荧光蛋白（GFP）作为受体和信号转导器。二维MoS由于其与其他纳米材料相比具有高的表面体积比，已被用作识别生物分析物的有前途的候选材料。这种材料易于进行表面调节，为分析感兴趣的目标提供了额外的优势。优化后的二维MoS-GFP缀合物能够在50 nM浓度下区分15种不同的蛋白质，检测限为1 nM。此外，还成功区分了二元混合物和存在血清时的蛋白质。在相关浓度下，血清介质中的10种不同蛋白质以100%的留一法分类准确率成功分类，这证明了上述系统的潜力。我们还实施并讨论了使用不同机器学习模型对基于阵列传感的模式识别问题的影响。
