Format

Send to

Choose Destination
See comment in PubMed Commons below
J Struct Biol. 2016 Apr;194(1):49-60. doi: 10.1016/j.jsb.2016.01.012. Epub 2016 Feb 1.

Statistical modeling and removal of lipid membrane projections for cryo-EM structure determination of reconstituted membrane proteins.

Author information

1
Department of Computer Science, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark.
2
Department of Cellular and Molecular Physiology, Yale University School of Medicine, 333 Cedar St., New Haven, CT 06520, USA; RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
3
Department of Cellular and Molecular Physiology, Yale University School of Medicine, 333 Cedar St., New Haven, CT 06520, USA.

Abstract

This paper describes steps in the single-particle cryo-EM 3D structure determination of membrane proteins in their membrane environment. Using images of the Kv1.2 potassium-channel complex reconstituted into lipid vesicles, we describe procedures for the merging of focal-pairs of exposures and the removal of the vesicle-membrane signal from the micrographs. These steps allow 3D reconstruction to be performed from the protein particle images. We construct a 2D statistical model of the vesicle structure based on higher-order singular value decomposition (HOSVD), by taking into account the structural symmetries of the vesicles in polar coordinates. Non-roundness in the vesicle structure is handled with a non-linear shape alignment to a reference, which ensures a compact model representation. The results show that the learned model is an accurate representation of the imaged vesicle structures. Precise removal of the strong membrane signals allows better alignment and classification of images of small membrane-protein particles, and allows higher-resolution 3D reconstruction.

KEYWORDS:

Higher-order singular value decomposition; Kv1.2; Liposome; Potassium channel; Single-particle reconstruction; Statistical shape modeling

PMID:
26835990
PMCID:
PMC4866491
DOI:
10.1016/j.jsb.2016.01.012
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Support Center