bioRxivBiophysics
Exploring the Influence of Pore Shape on Conductance and Permeation
Abstract
There are increasing numbers of ion channel structures featuring heteromeric subunit assembly, exemplified by synaptic 1{beta}B Glycine and 4{beta}2 Nicotinic receptors. These structures exhibit inherent pore asymmetry, but the relevance of this to function is unknown. Furthermore, molecular dynamics simulations performed on symmetrical homomeric channels often leads to thermal distortion whereby conformations of the resulting ensemble are also asymmetrical. When functionally annotating ion channels, researchers often rely on minimal constrictions determined via radius-profile calculations performed with computer programs, such as HOLE or CHAP, coupled with an assessment of pore hydrophobicity. However, such tools typically employ spherical probe particles, limiting their ability to accurately capture pore asymmetry. Here, we introduce an algorithm that employs ellipsoidal probe particles, enabling a more comprehensive representation of the pore geometry. Our analysis reveals that the use of non-spherical ellipsoids for pore characterization, provides a more accurate and easily interpretable depiction of conductance. To quantify the implications of pore asymmetry on conductance, we systematically investigated carbon nanotubes (CNTs) with varying degrees of pore asymmetry as model systems. The conductance through these channels shows surprising effects that would otherwise not be predicted with spherical probes. The results have broad implications not only for the functional annotation of biological ion channels, but also for the design of synthetic channel systems for use in areas such as water filtration. Furthermore, we make use of the more accurate characterization of channel pores to refine a physical conductance model to obtain a heuristic estimate for single channel conductance. The code is freely available, obtainable as pip-installable python package and provided as a webservice.
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: trajectory inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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