Synthetic Biology and Bioengineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea; Department of Biosystems and Bioengineering, KRIBB School of Biotechnology, University of Science and Technology, Daejeon, 34113, Republic of Korea.
Synthetic Biology and Bioengineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea; Department of Biosystems and Bioengineering, KRIBB School of Biotechnology, University of Science and Technology, Daejeon, 34113, Republic of Korea.
Biosens Bioelectron. 2020 Dec 15;170:112670. doi: 10.1016/j.bios.2020.112670. Epub 2020 Oct 1.
Bacteria initiate complicated signaling cascades from the detection of intracellular metabolites or exogenous substances by hundreds of transcription factors, which have been widely investigated as genetically-encoded biosensors for molecular recognition. However, the limited number of transcription factors and their broad substrate specificity result in ambiguity in small molecule identification. This study presents a new small molecule fingerprinting technique using evolutionary biosensor arrays with a machine learning technique that can capture highly specific substrate signals. Employing multiple mutant transcription factors derived from a single transcription factor has effectively circumvented the limited availability of transcription factors induced by a small molecule of our interest. This method achieved up to 95.3% true positive rate for identifying small molecules, and the high-resolution protein engineering technique improved the limit of detection 75-fold. The signal trade-offs with background noises caused by the complex cellular biochemistry of mutant transcription factors enable the biosensor arrays to be more informative in terms of statistical variance. The machine learning technology, coupled with the single transcription factor-driven evolutionary biosensor array, will open new avenues for molecular fingerprinting technologies.
细菌通过数百种转录因子从检测细胞内代谢物或外源性物质开始引发复杂的信号级联反应，这些转录因子已被广泛研究作为用于分子识别的遗传编码生物传感器。然而，转录因子的数量有限及其广泛的底物特异性导致小分子识别的不明确性。本研究提出了一种使用进化生物传感器阵列和机器学习技术的新型小分子指纹识别技术，该技术可以捕获高度特异性的底物信号。使用源自单个转录因子的多个突变型转录因子有效地规避了由我们感兴趣的小分子诱导的转录因子的有限可用性。该方法实现了高达 95.3%的小分子识别的真阳性率，而高分辨率蛋白质工程技术将检测限提高了 75 倍。由于突变型转录因子复杂的细胞生物化学引起的信号与背景噪声的权衡，使生物传感器阵列在统计方差方面更具信息量。机器学习技术与单个转录因子驱动的进化生物传感器阵列相结合，将为分子指纹识别技术开辟新的途径。
