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Generating protein three-dimensional fold signatures using inductive logic programming.

M Turcotte1, S H Muggleton, M J Sternberg

  • 1Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, P.O. Box 123, 44 Lincoln's Inn Fields, London WC2A 3PX, UK.

Computers & Chemistry
|January 5, 2002
PubMed
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Inductive logic programming (ILP) now uses topological information to discover protein fold signatures more accurately. This approach improves rule generation and processing of all data, overcoming previous limitations.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Inductive logic programming (ILP) has been used for protein fold signature discovery.
  • Previous ILP applications faced challenges with non-structurally related secondary structures and scalability.
  • These limitations necessitated sampling of negative examples, potentially affecting accuracy.

Purpose of the Study:

  • To investigate the integration of topological information into ILP for protein fold signature discovery.
  • To address the limitations of previous ILP methods, specifically non-structural matching and scaling issues.
  • To enhance the accuracy and completeness of protein fold signature identification.

Main Methods:

  • Utilized topological information within an inductive logic programming framework.

Related Experiment Videos

  • Developed new rule representations incorporating structural topology.
  • Conducted cross-validation tests on 20 protein folds.

    • Main Results:

    • Achieved an overall estimated accuracy of 73.37+/-0.35% for protein fold signature discovery.
    • The new topological representation enabled processing of the complete dataset, eliminating the need for negative example sampling.
    • Topological information was incorporated into approximately 90% of the generated rules, with 63% detailing sheet topology.

    Conclusions:

    • The integration of topological information significantly improves ILP-based protein fold signature discovery.
    • The enhanced method overcomes previous accuracy and scalability challenges.
    • The generated rules provide insights into both overall fold architecture and local secondary structure packing.