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Protein homology detection using string alignment kernels.

Hiroto Saigo1, Jean-Philippe Vert, Nobuhisa Ueda

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan.

Bioinformatics (Oxford, England)
|February 28, 2004
PubMed
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New local alignment kernels improve remote homology detection for protein sequences. These kernels, used with support vector machines (SVMs), outperform existing methods in recognizing protein superfamilies.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Remote homology detection is crucial for understanding protein function and evolution.
  • Support vector machines (SVMs) are effective for protein superfamily recognition.
  • SVM performance relies heavily on appropriate kernel functions for sequence similarity measurement.

Purpose of the Study:

  • To develop novel string kernels tailored for biological sequences.
  • To enhance the accuracy of remote homology detection using SVMs.

Main Methods:

  • Introduction of local alignment kernels, which measure sequence similarity via gapped local alignments.
  • Integration of these kernels with SVMs for superfamily recognition tasks.
  • Benchmarking against state-of-the-art methods on a standard dataset.

Related Experiment Videos

Main Results:

  • The proposed local alignment kernels significantly improve remote homology detection.
  • The new kernels demonstrate superior performance compared to existing methods for SCOP superfamily recognition.
  • The approach effectively identifies distant evolutionary relationships between protein sequences.

Conclusions:

  • Local alignment kernels offer a powerful new tool for protein sequence analysis.
  • This method advances the field of remote homology detection.
  • The findings have implications for protein function prediction and evolutionary studies.