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Algorithms for protein structural motif recognition

B Berger1

  • 1Mathematics Department, Massachusetts Institute of Technology, Cambridge 02139, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 1, 1995
PubMed
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This study introduces a fast correlation method to identify protein 3D structures from 1D sequences. The approach accurately distinguishes protein motifs like coiled coils and globins without errors.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Predicting protein three-dimensional (3D) structures from one-dimensional (1D) amino acid sequences is a fundamental challenge in biology.
  • Identifying specific structural motifs within protein sequences is crucial for understanding protein function and evolution.

Purpose of the Study:

  • To develop and evaluate a novel probabilistic method for identifying protein sequences that fold into known 3D structures.
  • To assess the efficiency and accuracy of this method in distinguishing between different protein structural motifs.

Main Methods:

  • A correlation method analyzing pairwise dependencies between amino acid residues at multiple distances was employed.
  • The method calculates the conditional probability of a residue belonging to a specific 3D structure.

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  • A dynamic programming approach was used to generalize the method for multiple motifs, ensuring linear time complexity.

    • Main Results:

    • The method successfully distinguished between (2-stranded) coiled-coil and non-coiled-coil domains.
    • Globins were accurately differentiated from non-globin proteins.
    • Testing on the Brookhaven X-ray crystal structure database yielded no false-positive or false-negative predictions for coiled coils.

    Conclusions:

    • The developed correlation method provides an efficient and accurate approach for identifying protein structural motifs from sequence data.
    • This probabilistic analysis holds significant potential for protein structure prediction and classification.
    • The method's linear time complexity makes it suitable for large-scale biological sequence analysis.