Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM

View ORCID ProfileAli Punjani, David J. Fleet
doi: https://doi.org/10.1101/2021.04.22.440893
Ali Punjani
1University of Toronto
2Vector Institute
3Structura Biotechnology Inc.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ali Punjani
  • For correspondence: apunjani@structura.bio fleet@cs.toronto.edu
David J. Fleet
1University of Toronto
2Vector Institute
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: apunjani@structura.bio fleet@cs.toronto.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Single particle cryo-EM excels in determining static structures of biological macromolecules such as proteins. However, many proteins are dynamic, with their motion inherently linked to their function. Recovering the continuous motion and detailed 3D structure of flexible proteins from cryo-EM data has remained an open challenge. We introduce 3D Flexible Refinement (3DFlex), a motion-based deep neural network model of continuous heterogeneity. 3DFlex directly exploits the knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to conserve mass and preserve local geometry. From 2D image data, the 3DFlex model jointly learns a single canonical 3D map, latent coordinate vectors that specify positions on the protein’s conformational landscape, and a flow generator that, given a latent position as input, outputs a 3D deformation field. This deformation field convects the canonical map into appropriate conformations to explain experimental images. Applied to experimental data, 3DFlex learns non-rigid motion spanning several orders of magnitude while preserving high-resolution details of secondary structure elements. Further, 3DFlex resolves canonical maps that are improved relative to conventional refinement methods because particle images contribute to the maps coherently regardless of the conformation of the protein in the image. Together, the ability to obtain insight into motion in macromolecules, as well as the ability to resolve features that are usually lost in cryo-EM of flexible specimens, will provide new insight and allow new avenues of investigation into biomolecular structure and function.

Competing Interest Statement

The novel aspects of the method presented are described in a provisional patent application.

Footnotes

  • https://cryosparc.com/3dflex

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted April 22, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM
Ali Punjani, David J. Fleet
bioRxiv 2021.04.22.440893; doi: https://doi.org/10.1101/2021.04.22.440893
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM
Ali Punjani, David J. Fleet
bioRxiv 2021.04.22.440893; doi: https://doi.org/10.1101/2021.04.22.440893

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biophysics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4682)
  • Biochemistry (10357)
  • Bioengineering (7670)
  • Bioinformatics (26330)
  • Biophysics (13523)
  • Cancer Biology (10683)
  • Cell Biology (15438)
  • Clinical Trials (138)
  • Developmental Biology (8497)
  • Ecology (12820)
  • Epidemiology (2067)
  • Evolutionary Biology (16851)
  • Genetics (11399)
  • Genomics (15478)
  • Immunology (10616)
  • Microbiology (25207)
  • Molecular Biology (10220)
  • Neuroscience (54463)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4342)
  • Plant Biology (9243)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2557)
  • Systems Biology (6780)
  • Zoology (1466)