Komoto Yuki, Ryu Jiho, Taniguchi Masateru
SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
Artificial Intelligence Research Center, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
Discov Nano. 2024 Jan 29;19(1):20. doi: 10.1186/s11671-024-03963-4.
Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.
断裂结(BJ)测量为各种分子的电学性质提供了深入了解,能够直接评估单分子电导。BJ方法在确定单个分子的动力学、单分子化学反应以及生物分子(如脱氧核糖核酸和核糖核酸)方面显示出应用潜力。然而,通过单分子测量获得的电导数据可能会因结内微小的结构变化而容易受到波动影响。因此,清晰识别这些分子的传导状态具有挑战性。本研究旨在开发一种精确识别传导状态轨迹的方法。我们提出了一种新颖的单分子分析方法,该方法在信号处理中采用全变差去噪(TVD),重点是将信息技术与测量的单分子数据相结合。我们成功地将此方法应用于模拟的电导轨迹,有效地对数据进行去噪,并阐明了多个传导状态。所提出的方法有助于识别明确的平台长度,并提高监督机器学习的准确性。引入的基于TVD的分析方法在阐明测量的单分子数据中的状态方面是有效的。这种方法有可能为分子结的形成、构象变化和裂解提供新的视角。