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Turn prediction in proteins using a pattern-matching approach.

F E Cohen, R M Abarbanel, I D Kuntz

    Biochemistry
    |January 14, 1986
    PubMed
    Summary
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    This study identifies protein turns using amino acid sequence patterns with 95% accuracy. The method accurately predicts turns in globular proteins and homologous families, aiding protein sequence analysis.

    Area of Science:

    • Biochemistry
    • Structural Biology
    • Bioinformatics

    Background:

    • Amino acid sequence patterns are crucial for understanding protein structure and function.
    • Identifying structural elements like turns in globular proteins is essential for predicting protein folding and interactions.
    • Previous methods for turn identification may lack accuracy or efficiency.

    Purpose of the Study:

    • To extend the application of amino acid sequence patterns for accurate identification of turns in globular proteins.
    • To develop and validate a computational approach for predicting protein structural features.
    • To provide a general pattern-recognition language for protein and nucleic acid sequence investigations.

    Main Methods:

    • Utilized a conservative strategy for pattern matching.

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  • Implemented a hierarchical search prioritizing stronger patterns.
  • Employed length-dependent masking to enhance accuracy.
  • Tested the method on proteins with known structures and homologous families.
  • Developed a Lisp-based computer program for sequence analysis.

    • Main Results:

    • Achieved 95% accuracy in identifying turns in globular proteins with known structures.
    • Demonstrated a 90% success rate when applying the procedure to homologous protein families.
    • The developed method shows high predictive power for protein structural turns.
    • The Lisp program offers a versatile tool for sequence pattern recognition.

    Conclusions:

    • Amino acid sequence patterns are effective for accurate turn identification in globular proteins.
    • The hierarchical search and masking strategy significantly improve prediction accuracy.
    • The developed computational approach is robust for both known structures and homologous families.
    • The pattern-recognition language has broad applicability in protein and nucleic acid sequence analysis.