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Data-driven determination of number of discrete conformations in single-particle cryo-EM.

Publication ,  Journal Article
Zhou, Y; Moscovich, A; Bartesaghi, A
Published in: Computer methods and programs in biomedicine
June 2022

One of the strengths of single-particle cryo-EM compared to other structural determination techniques is its ability to image heterogeneous samples containing multiple molecular species, different oligomeric states or distinct conformations. This is achieved using routines for in-silico 3D classification that are now well established in the field and have successfully been used to characterize the structural heterogeneity of important biomolecules. These techniques, however, rely on expert-user knowledge and trial-and-error experimentation to determine the correct number of conformations, making it a labor intensive, subjective, and difficult to reproduce procedure.We propose an approach to address the problem of automatically determining the number of discrete conformations present in heterogeneous single-particle cryo-EM datasets. We do this by systematically evaluating all possible partitions of the data and selecting the result that maximizes the average variance of similarities measured between particle images and the corresponding 3D reconstructions.Using this strategy, we successfully analyzed datasets of heterogeneous protein complexes, including: 1) in-silico mixtures obtained by combining closely related antibody-bound HIV-1 Env trimers and other important membrane channels, and 2) naturally occurring mixtures from diverse and dynamic protein complexes representing varying degrees of structural heterogeneity and conformational plasticity.The availability of unsupervised strategies for 3D classification combined with existing approaches for fully automatic pre-processing and 3D refinement, represents an important step towards converting single-particle cryo-EM into a high-throughput technique.

Duke Scholars

Published In

Computer methods and programs in biomedicine

DOI

EISSN

1872-7565

ISSN

0169-2607

Publication Date

June 2022

Volume

221

Start / End Page

106892

Related Subject Headings

  • Molecular Conformation
  • Medical Informatics
  • Image Processing, Computer-Assisted
  • Cryoelectron Microscopy
  • 4603 Computer vision and multimedia computation
  • 4601 Applied computing
  • 4003 Biomedical engineering
  • 0906 Electrical and Electronic Engineering
  • 0903 Biomedical Engineering
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Zhou, Y., Moscovich, A., & Bartesaghi, A. (2022). Data-driven determination of number of discrete conformations in single-particle cryo-EM. Computer Methods and Programs in Biomedicine, 221, 106892. https://doi.org/10.1016/j.cmpb.2022.106892
Zhou, Ye, Amit Moscovich, and Alberto Bartesaghi. “Data-driven determination of number of discrete conformations in single-particle cryo-EM.Computer Methods and Programs in Biomedicine 221 (June 2022): 106892. https://doi.org/10.1016/j.cmpb.2022.106892.
Zhou Y, Moscovich A, Bartesaghi A. Data-driven determination of number of discrete conformations in single-particle cryo-EM. Computer methods and programs in biomedicine. 2022 Jun;221:106892.
Zhou, Ye, et al. “Data-driven determination of number of discrete conformations in single-particle cryo-EM.Computer Methods and Programs in Biomedicine, vol. 221, June 2022, p. 106892. Epmc, doi:10.1016/j.cmpb.2022.106892.
Zhou Y, Moscovich A, Bartesaghi A. Data-driven determination of number of discrete conformations in single-particle cryo-EM. Computer methods and programs in biomedicine. 2022 Jun;221:106892.
Journal cover image

Published In

Computer methods and programs in biomedicine

DOI

EISSN

1872-7565

ISSN

0169-2607

Publication Date

June 2022

Volume

221

Start / End Page

106892

Related Subject Headings

  • Molecular Conformation
  • Medical Informatics
  • Image Processing, Computer-Assisted
  • Cryoelectron Microscopy
  • 4603 Computer vision and multimedia computation
  • 4601 Applied computing
  • 4003 Biomedical engineering
  • 0906 Electrical and Electronic Engineering
  • 0903 Biomedical Engineering
  • 0801 Artificial Intelligence and Image Processing