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Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical

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This study refines AlphaFold2 models using elastic network models (ENMs) and cryo-electron microscopy (cryo-EM) data. The flexible refinement method significantly improved four low-accuracy protein structure models.

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • AlphaFold2 accurately predicts protein structures, but some models require refinement.
  • Cryo-electron microscopy (cryo-EM) provides experimental structural data.
  • Elastic Network Models (ENMs) describe protein flexibility.

Purpose of the Study:

  • To develop and optimize a flexible refinement method for AlphaFold2 models using ENM normal modes.
  • To improve the accuracy of AlphaFold2 models that deviate from experimental cryo-EM data.

Main Methods:

  • Flexible refinement of AlphaFold2 models against cryo-EM maps.
  • Utilizing normal modes from ENMs as basis functions for structural displacement.
  • Systematic optimization of ENM motion model parameters.

Main Results:

  • Successfully refined four AlphaFold2 models with improved TM-scores (e.g., respiratory supercomplex from 0.52 to 0.69).
  • Identified optimal parameters including mode range (1-12), masked maps, inner product similarity, and Powell optimization.
  • Integrated optimized parameters into the ModeHunter package (version 1.4).

Conclusions:

  • Flexible refinement using ENM normal modes is effective for improving low-accuracy AlphaFold2 models.
  • Optimized parameters enhance the performance of the refinement process.
  • The ModeHunter package provides a practical tool for structural model refinement.