A deep learning approach to real-time Markov modeling of ion channel gating.

ABSTRACT: The patch-clamp technique allows us to eavesdrop the gating behavior of individual ion channels with unprecedented temporal resolution. The signals arise from conformational changes of the channel protein as it makes rapid transitions between conducting and non-conducting states. However, unambiguous analysis of single-channel datasets is challenging given the inadvertently low signal-to-noise ratio as well as signal distortions caused by low-pass filtering. Ion channel kinetics are typically described using hidden Markov models (HMM), which allow conclusions on the inner workings of the protein. In this study, we present a Deep Learning approach for extracting models from single-channel recordings. Two-dimensional dwell-time histograms are computed from the idealized time series and are s

SUBMITTER: Oikonomou E 

PROVIDER: S-EPMC11608338 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature