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Amino acid covariation from evolutionary data can now predict transmembrane protein structures. This method accurately models complex proteins, revealing their function and dynamics from sequence alone.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Transmembrane proteins are crucial for cellular functions but challenging to structurally characterize.
  • Predicting their 3D structures from sequence alone is a significant challenge in structural biology.

Purpose of the Study:

  • To develop and validate a novel computational method for predicting transmembrane protein structures.
  • To demonstrate the utility of evolutionary information for de novo structure prediction.

Main Methods:

  • Utilizing amino acid covariation inferred from evolutionary sequence records.
  • Applying a maximum entropy approach to identify pairwise distance constraints.
  • Generating all-atom models using these constraints (EVfold_membrane).

Main Results:

  • Successfully predicted 3D structures for 11 previously unknown transmembrane proteins (up to 14 helices).
  • Achieved unprecedented accuracy in blinded de novo structure prediction for 23 transmembrane protein families.
  • Demonstrated the method's ability to predict protein oligomerization, functional sites, and conformational changes.

Conclusions:

  • Evolutionary sequence data provides powerful constraints for transmembrane protein structure prediction.
  • EVfold_membrane significantly advances the capabilities for modeling diverse and complex transmembrane proteins.
  • This approach holds promise for expanding structural modeling to a wider range of proteins.