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Related Experiment Videos

Non-globular domains in protein sequences: automated segmentation using complexity measures

J C Wootton1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894.

Computers & Chemistry
|September 1, 1994
PubMed
Summary

Computational methods can identify protein structures by analyzing amino acid sequence complexity. The SEG algorithm effectively distinguishes globular from non-globular protein regions, aiding in new discoveries.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Structural Biology

Background:

  • Globular protein structures are linked to high informational complexity in amino acid sequences.
  • Protein sequences in databases like SWISS-PROT show significant segments of non-randomly low complexity.
  • Existing methods for analyzing protein sequence complexity have limitations.

Purpose of the Study:

  • To develop and validate computational methods for distinguishing globular and non-globular protein regions.
  • To assess the prevalence of low-complexity sequences in protein databases.
  • To identify novel classes of non-globular sequence segments.

Main Methods:

  • Utilizing mathematically-defined measures of compositional complexity for sequence analysis.
  • Comparing statistical properties of known crystal structures with shuffled sequences.

Related Experiment Videos

  • Applying the SEG algorithm for automated partitioning of globular and non-globular regions.
  • Analyzing sequences of various known non-globular proteins (e.g., collagens, elastins, histones).

    • Main Results:

    • High informational complexity correlates with compact globular protein structures.
    • A substantial portion of SWISS-PROT entries contain low-complexity sequence segments.
    • The SEG algorithm effectively and automatically partitions protein sequences into globular and non-globular regions.
    • Analysis revealed new classes of long, non-globular segments, exemplified by the human CAN gene product.

    Conclusions:

    • Computational analysis of sequence complexity is a powerful tool for understanding protein structure.
    • The SEG algorithm offers a robust and generalizable method for protein sequence analysis.
    • This approach facilitates the discovery of functionally significant non-globular protein regions and their roles in biological processes, including disease.