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Related Experiment Videos

A method for alpha-helical integral membrane protein fold prediction

W R Taylor1, D T Jones, N M Green

  • 1Laboratory of Mathematical Biology, National Institute for Medical Research, London, U.K.

Proteins
|March 1, 1994
PubMed
Summary

This study introduces an automated method for predicting the 3D structure of alpha-helical membrane proteins. The approach accurately identifies the correct protein fold by analyzing lipid-exposed surfaces from sequence alignments.

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Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational biophysics

Background:

  • Integral membrane proteins are crucial for cellular functions.
  • Existing methods focus on helix location prediction, neglecting automated helix packing.
  • Membrane protein structure prediction is simplified by uniform helix alignment across membranes.

Purpose of the Study:

  • To develop a fully automated method for predicting the three-dimensional structure of alpha-helical membrane proteins.
  • To accurately pack predicted helices into their native three-dimensional fold.
  • To evaluate the method's effectiveness using known membrane protein families.

Main Methods:

  • Representing all possible protein structures on a lattice for exhaustive enumeration.

Related Experiment Videos

  • Evaluating protein folds based on how well predicted exposed surfaces from multiple sequence alignments fit their positions.
  • Utilizing variable-hydrophobic (variphobic) positions to recognize lipid-exposed surfaces, analogous to solvent exposure in globular proteins.

    • Main Results:

    • The method successfully predicted the correct fold for bacteriorhodopsin and rhodopsin/opsin families.
    • Accurate prediction of lipid-exposed face angular size was critical for fold selection.
    • The correct fold was typically found within the top six candidates, despite inherent uncertainties.

    Conclusions:

    • This is the first completely automatic method for predicting membrane protein 3D structure from sequence data.
    • The method shows promise for predicting structures of seven-helix bundle proteins.
    • Further characterization is needed due to limitations in available sequence data for known structures.