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© Research
Publication : Journal of structural biology

Characterization of heterogeneity in nanodisc samples using Feret signatures.

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Journal of structural biology - 01 Nov 2022

Vilela F, Bezault A, Rodriguez de Francisco B, Sauvanet C, Xu XP, Swift MF, Yao Y, Marrasi FM, Hanein D, Volkmann N,

Link to Pubmed [PMID] – 36332745

Link to DOI – S1047-8477(22)00086-710.1016/j.jsb.2022.107916

J Struct Biol 2022 Nov; (): 107916

Nanodiscs have become a popular tool in structure determination of membrane proteins using cryogenic electron microscopy and single particle analysis. However, the structure determination of small membrane proteins remains challenging. When the embedded protein is in the same size range as the nanodisc, the nanodisc can significantly contribute to the alignment and classification during the structure determination process. In those cases, it is crucial to minimize the heterogeneity in the nanodisc preparations to assure maximum accuracy in the classification and alignment steps of single particle analysis. Here, we introduce a new in-silico method for the characterization of nanodisc samples that is based on analyzing the Feret diameter distribution of their particle projection as imaged in the electron microscope. We validated the method with comprehensive simulation studies and show that Feret signatures can detect subtle differences in nanodisc morphologies and composition that might otherwise go unnoticed. We used the method to identify a specific biochemical nanodisc preparation with low size variations, allowing us to obtain a structure of the 23-kDa single-span membrane protein Bcl-xL while embedded in a nanodisc. Feret signature analysis can steer experimental data collection strategies, allowing more efficient use of high-end data collection hardware, as well as image analysis investments in studies where nanodiscs significantly contribute to the total volume of the full molecular species.

https://pubmed.ncbi.nlm.nih.gov/36332745