4.7 Article

Atomic force microscopy for quantitative understanding of peptide-induced lipid bilayer remodeling

期刊

METHODS
卷 197, 期 -, 页码 20-29

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2020.10.014

关键词

Pore forming; AFM; Scanning probe; Supported lipid bilayer; SLB

资金

  1. National Science Foundation [1709792]
  2. National Institutes of Health [R01GM120642]
  3. Division Of Materials Research
  4. Direct For Mathematical & Physical Scien [1709792] Funding Source: National Science Foundation

向作者/读者索取更多资源

Peptides that bind lipid membranes and cause leakage play important roles in cellular activities and potential therapeutic applications. The atomic force microscope (AFM) can provide precise imaging of lipid bilayer remodeling induced by membrane permeabilizing peptides to advance understanding of their mechanisms of action. High precision AFM data, coupled with robust statistical analyses, has the potential to uncover more insights into this important peptide family.
A number of peptides are known to bind lipid bilayer membranes and cause these natural barriers to leak in an uncontrolled manner. Though membrane permeabilizing peptides play critical roles in cellular activity and may have promising future applications in the therapeutic arena, significant questions remain about their mechanisms of action. The atomic force microscope (AFM) is a single molecule imaging tool capable of addressing lipid bilayers in near-native fluid conditions. The apparatus complements traditional assays by providing local topographic maps of bilayer remodeling induced by membrane permeabilizing peptides. The information garnered from the AFM includes direct visualization and statistical analyses of distinct bilayer remodeling modes such as highly localized pore-like voids in the bilayer and dispersed thinned membrane regions. Colocalization of distinct remodeling modes can be studied. Here we examine recent work in the field and outline methods used to achieve precise AFM image data. Experimental challenges and common pitfalls are discussed as well as techniques for unbiased analysis including the Hessian blob detection algorithm, bootstrapping, and the Bayesian information criterion. When coupled with robust statistical analyses, high precision AFM data is poised to advance understanding of an important family of peptides that cause poration of membrane bilayers.

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