Abstract
(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca-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.
Highlights
Introduction nal affiliationsArtificial intelligence (AI) approaches are still gaining popularity in biological sciences due to their utility in diagnosing, managing, and designing drugs against many popular diseases [1,2,3,4,5]
In this work we investigate the activity of large-conductance voltage- and Ca2+ activated potassium channels (BK) from the inner mitochondrial membrane [7,8,9]
We are already witnessing the rapid adoption of machine-learning techniques as a useful research tool in the field of molecular biology and electrophysiology
Summary
Artificial intelligence (AI) approaches (machine learning, neural networks) are still gaining popularity in biological sciences due to their utility in diagnosing, managing, and designing drugs against many popular diseases [1,2,3,4,5]. Ion channels play an important role in many physiological processes and are considered to be drug targets [6]. In this work we investigate the activity of large-conductance voltage- and Ca2+ activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK) [7,8,9]. The BK channels were investigated in plasma-membrane [10,11,12]. In that research the name of the channel-BK was derived from its big single-channel conductance
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