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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Motif kernel generated by genetic programming improves remote homology and fold detection.

Tony Håndstad1, Arne J H Hestnes, Pål Saetrom

  • 1Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7052, Trondheim, Norway. tony.handstad@gmail.com

BMC Bioinformatics
|January 27, 2007
PubMed
Summary

A new GPkernel method improves protein remote homology detection by evolving motifs using genetic programming. This approach enhances classification accuracy, especially for challenging protein fold recognition tasks.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in biology

Background:

  • Protein remote homology detection is crucial in computational biology.
  • Current methods often use support vector machines with sequence motif kernels.
  • Existing motif-based methods struggle with sequences lacking suitable motifs.

Purpose of the Study:

  • To introduce an improved motif kernel for protein remote homology detection.
  • To leverage genetic programming for motif evolution.
  • To enhance classification accuracy for protein families.

Main Methods:

  • Developed the GPkernel, a novel motif kernel.
  • Utilized genetic programming to evolve discrete sequence motifs.
  • Grouped proteins based on evolutionary relations and structure to create discriminating motif sets.

Main Results:

  • The GPkernel significantly outperforms related methods on SCOP benchmarks.
  • Achieved superior results in both superfamily and fold recognition tasks.
  • Demonstrated enhanced performance compared to existing remote homology detection techniques.

Conclusions:

  • The GPkernel excels in difficult protein fold recognition problems.
  • Effectiveness stems from motif sets describing similarities within and between protein subgroups.
  • Provides a richer description of protein fold similarities and differences than prior methods.