DOI: 10.5281/zenodo.6500377
title: To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)

description: The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories. The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories: <strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows: <strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong> <strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong> <strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong> <strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong> <strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong> <strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel. The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV). 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files. The Python code is placed in two files: <strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook. <strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.
description: This research was funded by the Silesian University of Technology grant for young researchers (04/040/BKM21/0177) to AWJ (statute project) and by grant No. 04/040/SDU/ 10-22-01 from the Excellence in Science Initiative for A.W.-J. Patch-clamp data were obtained as part of the grant number 2016/21/B/NZ1/02769 from National Science Center of Poland (to PB). The computational power for the calculations presented in this study was funded by the grant number 2018/29/B/ST3/01892 (MRL).