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Protein fold recognition using sequence-derived predictions

D Fischer1, D Eisenberg

  • 1UCLA-DOE Laboratory of Structural Biology & Molecular Medicine, Molecular Biology Institute 90095-1570, USA.

Protein Science : a Publication of the Protein Society
|May 1, 1996
PubMed
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Incorporating predicted secondary structure into protein fold recognition significantly improves accuracy. This method enhances the assignment of amino acid sequences to known 3D protein folds, aiding in structural biology research.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein fold recognition is crucial for assigning unknown protein structures to known 3D folds.
  • Effective scoring of probe sequences against target structures is key for accurate fold assignment.
  • Existing methods primarily rely on amino acid sequence information.

Purpose of the Study:

  • To evaluate the utility of sequence-derived properties, specifically predicted secondary structure, in protein fold recognition.
  • To develop and test a novel sequence-structure compatibility function for improved fold assignment.
  • To assess the impact of incorporating predicted secondary structure on the accuracy of assigning sequences to known 3D folds.

Main Methods:

  • Developed a sequence-structure compatibility function combining traditional methods (e.g., 3D-1D scores) with predicted probe secondary structure.

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  • Evaluated the method using a benchmark set of proteins where 3D structures were known but ignored during assignment.
  • Applied the new method to assign folds for sequences in the SWISSPROT database.

    • Main Results:

    • Inclusion of predicted secondary structure improved protein fold assignment accuracy by approximately 25%.
    • Knowing the true secondary structure of the probe could further increase correct fold assignments by 8-32%.
    • The method successfully identified six fold assignments not detectable by standard sequence-sequence comparison methods.

    Conclusions:

    • Sequence-derived predictions, particularly secondary structure, significantly enhance protein fold recognition.
    • The developed sequence-structure compatibility function offers a more robust approach to fold assignment.
    • This method has practical applications in identifying protein folds for sequences in large databases like SWISSPROT.