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Related Experiment Videos

Optimizing amino acid substitution matrices with a local alignment kernel.

Hiroto Saigo1, Jean-Philippe Vert, Tatsuya Akutsu

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. hiroto@kuicr.kyoto-u.ac.jp

BMC Bioinformatics
|May 9, 2006
PubMed
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Optimizing amino acid substitution matrices improves protein homology detection. New matrices enhance both the local alignment kernel and Smith-Waterman algorithm, with the kernel showing superior performance.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Detecting remote protein homologies via direct sequence comparison is difficult.
  • A previously developed local alignment kernel (LAK) combined with a support vector machine shows promise.
  • LAK relies on amino acid substitution matrices, which may differ from those optimized for the Smith-Waterman algorithm.

Purpose of the Study:

  • To optimize amino acid substitution matrices for the local alignment kernel.
  • To improve the performance of homology detection algorithms.

Main Methods:

  • The local alignment kernel was optimized using gradient descent with an objective function for homolog discrimination.
  • Amino acid substitution matrices and gap parameters were optimized.

Related Experiment Videos

  • The optimized matrices were evaluated with both the local alignment kernel and the Smith-Waterman algorithm.

    • Main Results:

    • The local alignment kernel is differentiable, allowing for efficient optimization via dynamic programming.
    • Optimized matrices and gap parameters improved LAK performance compared to Smith-Waterman-optimized matrices.
    • Matrices optimized for LAK also benefited the Smith-Waterman algorithm.

    Conclusions:

    • The optimization procedure yields effective substitution matrices for both LAK and Smith-Waterman.
    • The local alignment kernel achieves the best performance for protein homology detection using these optimized matrices.