Institute of Physics, University of Silesia in Katowice, 40-007 Katowice, Poland.
Faculty of Science and Technology, University of Silesia in Katowice, 41-500 Chorzow, Poland.
Int J Mol Sci. 2021 Jan 15;22(2):840. doi: 10.3390/ijms22020840.
(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca2+-activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK). The characteristic electrophysiological features of the mitoBK channels are relatively high single-channel conductance (ca. 300 pS) and types of activating and deactivating stimuli. Nevertheless, depending on the isoformal composition of mitoBK channels in a given membrane patch and the type of auxiliary regulatory subunits (which can be co-assembled to the mitoBK channel protein) the characteristics of conformational dynamics of the channel protein can be altered. Consequently, the individual features of experimental series describing single-channel activity obtained by patch-clamp method can also vary. (2) Methods: Artificial intelligence approaches (deep learning) were used to classify the patch-clamp outputs of mitoBK activity from different cell types. (3) Results: Application of the K-nearest neighbors algorithm (KNN) and the autoencoder neural network allowed to perform the classification of the electrophysiological signals with a very good accuracy, which indicates that the conformational dynamics of the analyzed mitoBK channels from different cell types significantly differs. (4) Conclusion: We displayed the utility of machine-learning methodology in the research of ion channel gating, even in cases when the behavior of very similar microbiosystems is analyzed. A short excerpt from the patch-clamp recording can serve as a "fingerprint" used to recognize the mitoBK gating dynamics in the patches of membrane from different cell types.
(1) 背景：在这项工作中，我们专注于线粒体内膜（mitoBK）中大电导电压和 Ca2+激活钾通道（BK）的活性。mitoBK 通道的特征电生理特征是相对较高的单通道电导（约 300 pS）和激活和失活刺激的类型。然而，取决于特定膜片上 mitoBK 通道的同工型组成以及辅助调节亚基的类型（可以与 mitoBK 通道蛋白共同组装），通道蛋白构象动力学的特征可以改变。因此，通过膜片钳方法获得的描述单通道活性的实验系列的个体特征也可能有所不同。(2) 方法：人工智能方法（深度学习）用于对来自不同细胞类型的 mitoBK 活性的膜片钳输出进行分类。(3) 结果：应用 K-最近邻算法（KNN）和自动编码器神经网络允许非常准确地对电生理信号进行分类，这表明来自不同细胞类型的分析 mitoBK 通道的构象动力学显着不同。(4) 结论：我们展示了机器学习方法在离子通道门控研究中的实用性，即使在分析非常相似的微生物系统的情况下也是如此。膜片钳记录的简短摘录可以用作“指纹”，用于识别来自不同细胞类型的膜片上的 mitoBK 门控动力学。
