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Filtering remote homologues using predicted structural information.

Katsunori Uehara1, Takeshi Kawabata, Nobuhiro Go

  • 1Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.

Protein Engineering, Design & Selection : PEDS
|August 21, 2004
PubMed
Summary

This study introduces a novel filtering method to improve protein homology detection. By integrating predicted structural features with PSI-BLAST, it enhances the identification of remote protein homologues, aiding structure and function prediction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Identifying protein homologues is crucial for predicting protein structure and function.
  • Advanced sequence search methods struggle to detect remote homologues with low sequence similarity.

Purpose of the Study:

  • To develop a simple filtering method to enhance the detection of remote protein homologues.
  • To improve the performance of PSI-BLAST by incorporating predicted structural information.

Main Methods:

  • Utilized predicted secondary structures and solvent accessibilities as filtering features.
  • Employed PSI-BLAST with a relaxed E-value threshold to generate candidate homologues.
  • Applied a linear discrimination method combining PSI-BLAST E-values and structural match significance scores.

Related Experiment Videos

  • Used an in-house neural network for predicting protein structural features.

    • Main Results:

    • The proposed filtering method significantly improved PSI-BLAST performance in identifying homologous proteins.
    • Predicted secondary structures showed a greater improvement than solvent accessibilities alone.
    • Combining secondary structures and solvent accessibilities with PSI-BLAST yielded the best results.
    • The method demonstrated effective performance using cross-validation against SCOP superfamily relationships.

    Conclusions:

    • The integration of predicted structural features offers a powerful enhancement to sequence-based homology searches.
    • This method provides an easy-to-implement, computationally efficient approach for improved protein homology detection.
    • The findings support the utility of predicted structural data in overcoming limitations of traditional sequence similarity searches.