Komoto Yuki, Ohshiro Takahito, Taniguchi Masateru
Institute of Science and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan.
Artificial Intelligence Research Center, The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan.
Nanomaterials (Basel). 2021 Mar 19;11(3):784. doi: 10.3390/nano11030784.
Cyclic adenosine monophosphate (cAMP) is an important research target because it activates protein kinases, and its signaling pathway regulates the passage of ions and molecules inside a cell. To detect the chemical reactions related to the cAMP intracellular signaling pathway, cAMP, adenosine triphosphate (ATP), adenosine monophosphate (AMP), and adenosine diphosphate (ADP) should be selectively detected. This study utilized single-molecule quantum measurements of these adenosine family molecules to detect their individual electrical conductance using nanogap devices. As a result, cAMP was electrically detected at the single molecular level, and its signal was successfully discriminated from those of ATP, AMP, and ADP using the developed machine learning method. The discrimination accuracies of a single cAMP signal from AMP, ADP, and ATP were found to be 0.82, 0.70, and 0.72, respectively. These values indicated a 99.9% accuracy when detecting more than ten signals. Based on an analysis of the feature values used for the machine learning analysis, it is suggested that this discrimination was due to the structural difference between the ribose of the phosphate site of cAMP and those of ATP, ADP, and AMP. This method will be of assistance in detecting and understanding the intercellular signaling pathways for small molecular second messengers.
环磷酸腺苷（cAMP）是一个重要的研究靶点，因为它能激活蛋白激酶，其信号通路调节离子和分子在细胞内的通过。为了检测与cAMP细胞内信号通路相关的化学反应，需要选择性地检测cAMP、三磷酸腺苷（ATP）、一磷酸腺苷（AMP）和二磷酸腺苷（ADP）。本研究利用这些腺苷家族分子的单分子量子测量，通过纳米间隙装置检测它们各自的电导。结果，在单分子水平上对cAMP进行了电学检测，并使用所开发的机器学习方法成功地将其信号与ATP、AMP和ADP的信号区分开来。发现从AMP、ADP和ATP中区分单个cAMP信号的准确率分别为0.82、0.70和0.72。当检测到十个以上信号时，这些值表明准确率为99.9%。基于对用于机器学习分析的特征值的分析，表明这种区分是由于cAMP磷酸位点的核糖与ATP、ADP和AMP的核糖之间的结构差异。该方法将有助于检测和理解小分子第二信使的细胞间信号通路。
