Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results

International Journal of Molecular Sciences

Agata Wawrzkiewicz-Jałowiecka¹  Piotr Bednarczyk²  Paulina Trybek³  Monika Richter-Laskowska⁴ 

[1] Department of Physical Chemistry and Technology of Polymers, Silesian University of Technology, 44-100 Gliwice, Poland;Department of Physics and Biophysics, Institute of Biology, Warsaw University of Life Sciences—SGGW, 02-787 Warszawa, Poland;Faculty of Science and Technology, University of Silesia in Katowice, 41-500 Chorzow, Poland;Institute of Physics, University of Silesia in Katowice, 40-007 Katowice, Poland;

关键词: K-nearest neighbors algorithm;    autoencoder;    machine learning;    mitoBK channels;    gating dynamics;   

DOI  :  10.3390/ijms22020840

来源: DOAJ

【 摘 要 】

(1) Background: In this work, we focus on the activity of large-conductance voltage- and $C{a}^{2+}$-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.

【 授权许可】

Unknown