Department of Chemistry, University of Texas at San Antonio, San Antonio, TX 78249.
Department of Chemistry, University of Florida, Gainesville, FL 32611-7200.
Proc Natl Acad Sci U S A. 2019 Jul 30;116(31):15386-15391. doi: 10.1073/pnas.1820713116. Epub 2019 Jul 15.
We report a conjugated polyelectrolyte fluorescence-based biosensor P-C-3 and a general methodology to evaluate spectral shape recognition to identify biomolecules using artificial intelligence. By using well-defined analytes, we demonstrate that the fluorescence spectral shape of P-C-3 is sensitive to minor structural changes and exhibits distinct signature patterns for different analytes. A method was also developed to select useful features to reduce computational complexity and prevent overfitting of the data. It was found that the normalized intensity of 3 to 5 selected wavelengths was sufficient for the fluorescence biosensor to classify 13 distinct nucleotides and distinguish as little as single base substitutions at distinct positions in the primary sequence of oligonucleotides rapidly with nearly 100% classification accuracy. Photophysical studies led to a model to explain the mechanism of these fluorescence spectral shape changes, which provides theoretical support for applying this method in complicated biological systems. Using the feature selection algorithm to measure the relative intensity of a few selected wavelengths significantly reduces measurement time, demonstrating the potential for fluorescence spectrum shape analysis in high-throughput and high-content screening.
我们报告了一种基于共轭高分子电解质的荧光生物传感器 P-C-3 以及一种利用人工智能评估光谱形状识别以鉴定生物分子的通用方法。通过使用定义明确的分析物，我们证明了 P-C-3 的荧光光谱形状对微小的结构变化敏感，并表现出不同分析物的独特特征模式。还开发了一种选择有用特征的方法，以降低计算复杂度并防止数据过度拟合。结果发现，对于荧光生物传感器来说，选择 3 到 5 个波长的归一化强度足以快速分类 13 种不同的核苷酸，并区分寡核苷酸一级序列中特定位置的单个碱基取代，分类准确率接近 100%。光物理研究提出了一个解释这些荧光光谱形状变化机制的模型，为将该方法应用于复杂的生物系统提供了理论支持。使用特征选择算法测量少数选定波长的相对强度可显著缩短测量时间，表明荧光光谱形状分析在高通量和高内涵筛选中具有潜力。
