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Updated: May 29, 2026

Recapitulation of an Ion Channel IV Curve Using Frequency Components
Published on: February 8, 2011
Bi-stability, hysteresis, and memory of voltage-gated lysenin channels
Daniel Fologea1, Eric Krueger, Yuriy I Mazur
1Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA. danielfologea@boisestate.edu
Abstract:
Lysenin, a 297 amino acid pore-forming protein extracted from the coelomic fluid of the earthworm E. foetida, inserts constitutively open large conductance channels in natural and artificial lipid membranes containing sphingomyelin. The inserted channels show voltage regulation and slowly close at positive applied voltages. We report on the consequences of slow voltage-induced gating of lysenin channels inserted into a planar Bilayer Lipid Membrane (BLM), and demonstrate that these pore-forming proteins constitute memory elements that manifest gating bi-stability in response to variable external voltages. The hysteresis in macroscopic currents dynamically changes when the time scale of the voltage variation is smaller or comparable to the characteristic conformational equilibration time, and unexpectedly persists for extremely slow-changing external voltage stimuli. The assay performed on a single lysenin channel reveals that hysteresis is a fundamental feature of the individual channel unit and an intrinsic component of the gating mechanism. The investigation conducted at different temperatures reveals a thermally stable reopening process, suggesting that major changes in the energy landscape and kinetics diagram accompany the conformational transitions of the channels. Our work offers new insights on the dynamics of pore-forming proteins and provides an understanding of how channel proteins may form an immediate record of the molecular history which then determines their future response to various stimuli. Such new functionalities may uncover a link between molecular events and macroscopic processing and transmission of information in cells, and may lead to applications such as high density biologically-compatible memories and learning networks.
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