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Cryo-electron Microscopy01:28

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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.

Tatiana Shugaeva1, Rebecca J Howard1,2, Nandan Haloi3

  • 1Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, SE-17165, Sweden.

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|October 31, 2025
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Summary
This summary is machine-generated.

This study introduces a new method combining AI and simulations to model complex protein structures, improving accuracy for membrane proteins with multiple functional states.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Accurate atomic modeling into cryo-electron microscopy (cryo-EM) maps is essential for protein structure determination.
  • Modeling proteins with multiple functional states, especially membrane proteins, is challenging due to conformational flexibility and limited template availability.
  • Low cryo-EM map resolution hinders de novo model building for alternative protein conformations.

Purpose of the Study:

  • To develop and validate a novel computational approach for refining atomic models into cryo-EM maps, particularly for proteins exhibiting conformational transitions.
  • To enhance the accuracy of structural modeling for membrane proteins with multiple functional states where traditional methods fall short.

Main Methods:

  • Generating multiple initial protein models using stochastic subsampling of AlphaFold2's multiple sequence alignment (MSA) space.
  • Clustering the generated models using structure-based k-means to identify distinct conformational states.
  • Performing density-guided molecular dynamics (MD) simulations from representative cluster models.
  • Selecting the final atomic model based on cryo-EM map fit and overall model quality.

Main Results:

  • The proposed refinement approach significantly improved fitting accuracy compared to single starting point methods.
  • Demonstrated enhanced accuracy for three pharmacologically relevant membrane proteins (calcitonin receptor-like receptor, L-type amino acid transporter, alanine-serine-cysteine transporter) undergoing conformational changes.
  • Successfully facilitated the building of alternative functional states for these membrane proteins.

Conclusions:

  • Ensemble construction using generative artificial intelligence (AI) combined with simulation-based refinement is a powerful strategy for modeling alternative protein states.
  • This approach is particularly beneficial for understanding the functional dynamics of membrane proteins and other complex biological systems.
  • The method offers a robust solution for structure determination challenges posed by conformational heterogeneity in cryo-EM studies.