Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results.
Journal:
International journal of molecular sciences
PMID:
33467711
Abstract
(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.
Authors
Keywords
Algorithms
Animals
Artificial Intelligence
Endothelium
Female
Fibroblasts
Hippocampus
Ion Channel Gating
Kinetics
Large-Conductance Calcium-Activated Potassium Channels
Machine Learning
Mitochondria
Neural Networks, Computer
Patch-Clamp Techniques
Potassium Channels
Pregnancy
Pregnancy, Animal
Protein Conformation
Rats
Rats, Wistar