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Cryo-electron Microscopy

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Updated: Feb 24, 2026

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.

Jacob B Howard¹, Akshaya Narayanasamy², Steffen Lindert²

¹Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA.

Biorxiv : the Preprint Server for Biology

|February 23, 2026

Summary

This summary is machine-generated.

This study introduces CRIM, a new method combining cryo-electron microscopy (cryo-EM) and ion mobility mass spectrometry (IM-MS) data to significantly improve the accuracy of predicting protein structures. CRIM enhances atomic coordinate prediction for proteins, especially when experimental data is limited.

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Accurate protein structures are vital for understanding cellular functions and drug discovery.
Existing methods like cryo-EM and mass spectrometry provide valuable but often incomplete structural data.
Low-resolution cryo-EM maps and ion mobility mass spectrometry (IM-MS) collisional cross-section (CCS) values lack atomistic detail.

Purpose of the Study:

To develop an integrative scoring function, CRIM (cryo-EM + IM-MS), to enhance monomeric protein structure prediction.
To leverage complementary low-resolution cryo-EM density and IM-MS CCS data as restraints within the Rosetta framework.
To improve the accuracy of atomic coordinate determination from sparse or low-resolution experimental data.

Main Methods:

Developed CRIM, a Rosetta scoring function integrating Rosetta REF2015 energy with CCS agreement (PARCS) and electron density (elec_dens_fast) terms.
Tested CRIM on simulated data for 60 monomeric proteins.
Validated CRIM on an experimental dataset of 54 proteins using available cryo-EM maps or CCS values.

Main Results:

CRIM improved protein structure prediction quality, reducing mean RMSD from 3.65 Å to 2.90 Å and increasing mean TM-score from 0.88 to 0.90 on simulated data.
On experimental data, CRIM lowered mean RMSD from 6.65 Å to 4.38 Å and raised mean TM-score from 0.73 to 0.79.
CRIM produced competitive predictions compared to AlphaFold3, outperforming it on challenging cases with sparse restraints.

Conclusions:

CRIM effectively integrates low-resolution cryo-EM and IM-MS data to improve monomeric protein structure prediction.
The method offers a practical approach for enhancing structural models when detailed experimental data is scarce.
CRIM is available in the Rosetta software suite, facilitating its use in structural biology research.