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Updated: Nov 11, 2025

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
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Improving integrative 3D modeling into low- to medium-resolution electron microscopy structures with evolutionary

Caitlyn L McCafferty1, David W Taylor1,2,3, Edward M Marcotte1,2

  • 1Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, USA.

Protein Science : a Publication of the Protein Society
|March 24, 2021
PubMed
Summary

This study introduces a new method using co-evolutionary sequence data to accurately fit protein structures into electron microscopy maps. This approach enhances the modeling of complex biological structures, especially when resolution is limited.

Keywords:
electron microscopyevolutionary couplingsintegrative modelingprotein 3D structure

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Electron microscopy (EM) achieves near-atomic resolution for many protein structures.
  • Biochemical or conformational heterogeneity can limit EM structural determination to low- or medium-resolution.
  • Systematic, unbiased methods are crucial for fitting atomic models into lower-resolution EM maps.

Purpose of the Study:

  • To develop and validate a novel integrative modeling method incorporating co-evolutionary sequence data.
  • To improve the accuracy and confidence of subunit localization within heterogeneous EM structures.
  • To enable robust 3D model building for complexes with limited spatial data.

Main Methods:

  • Incorporation of co-evolution of intermolecular amino acids as distance restraints.
  • Utilizing an integrative modeling platform for building atomic models into EM maps (10-14 Å resolution).
  • Validation using known protein complexes (e.g., BAM complex) and modeling the bacterial holo-translocon.

Main Results:

  • Demonstrated successful fitting of atomic structures into medium- to low-resolution EM maps.
  • Highlighted conserved intermolecular couplings in the BAM complex despite conformational changes.
  • Generated a validated model for the bacterial holo-translocon consistent with biochemical data.

Conclusions:

  • Co-evolutionary information provides valuable distance restraints for integrative modeling.
  • This method enhances systematic and unbiased fitting of atomic models into lower-resolution EM maps.
  • The approach offers a powerful tool for structural biology when spatial data is limited.