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Information contained in protein shapes.

K Sundaram, V N Viswanadhan, R D MacElroy

    International Journal of Peptide and Protein Research
    |February 1, 1983
    PubMed
    Summary
    This summary is machine-generated.

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    Protein structure can be analyzed as an informational message using self-information content. This method quantifies structural specificity, revealing intricate patterns and highlighting high potential iron proteins for their unique three-letter dependence.

    Area of Science:

    • Structural biology
    • Bioinformatics
    • Protein structure analysis

    Background:

    • Proteins possess unique local conformational sequences at C-alpha atoms.
    • Understanding these sequences is key to deciphering protein structure and function.
    • Existing methods may not fully capture the intricate details of structural specificity.

    Purpose of the Study:

    • To develop a novel method for quantitating protein structural specificity.
    • To analyze the informational content of protein conformational sequences.
    • To identify unique structural features and dependencies within protein families.

    Main Methods:

    • Treating protein C-alpha atom conformations as an informational message string.
    • Evaluating total self-information content and per-letter self-information.

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  • Analyzing a dataset of 83 globular proteins with known structures from X-ray crystallography.
  • Main Results:

    • Information content effectively quantifies protein structural specificity.
    • The analysis reveals repeating and intricate structural features.
    • High potential iron proteins exhibit the most significant three-letter sequence dependence among analyzed proteins.

    Conclusions:

    • Self-information content provides a robust metric for protein structural characterization.
    • This approach facilitates the recognition of complex, recurring structural motifs.
    • High potential iron proteins represent a distinct class with notable sequence-structure relationships.