AI-based atomic force microscopy image analysis allows to predict electrochemical impedance spectra of defects in tethered bilayer membranes.

ABSTRACT: Atomic force microscopy (AFM) image analysis of supported bilayers, such as tethered bilayer membranes (tBLMs) can reveal the nature of the membrane damage by pore-forming proteins and predict the electrochemical impedance spectroscopy (EIS) response of such objects. However, automated analysis involving pore detection in such images is often non-trivial and can require AI-based object detection techniques. The specific object-detection algorithm we used to determine the defect coordinates in real AFM images was a convolutional neural network (CNN). Defect coordinates allow to predict the EIS response of tBLMs populated by the pore-forming toxins using finite element analysis (FEA) modeling. We tested if the accuracy of the CNN algorithm affected the EIS spectral features sensitive to defe

SUBMITTER: Raila T 

PROVIDER: S-EPMC8783026 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature