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Related Experiment Videos

Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing

Tomas Ohlson1, Varun Aggarwal, Arne Elofsson

  • 1Stockholm Bioinformatics Center, Stockholm University, SE-106 91 Stockholm, Sweden. tomasoh@sbc.su.se

BMC Bioinformatics
|July 28, 2006
PubMed
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Integrating self-organizing map (SOM) locations with predicted secondary structure improves protein sequence alignment quality. This complementary approach enhances alignments, particularly for distantly related proteins, by capturing additional structural information.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein sequence alignment is fundamental in bioinformatics for tasks like phylogenetic tree derivation and protein structure prediction.
  • Incorporating predicted secondary structure into alignment algorithms enhances performance.
  • The choice of secondary structure states can impact alignment quality, suggesting exploration of other structural features.

Purpose of the Study:

  • To investigate the utility of an unsupervised clustering method, the self-organizing map (SOM), for assigning sequence profile windows to structural states.
  • To assess the impact of these SOM-derived structural states on protein sequence alignment quality.

Main Methods:

  • Utilized an unsupervised clustering method, the self-organizing map (SOM).

Related Experiment Videos

  • Assigned sequence profile windows to distinct "structural states" using SOM.
  • Integrated SOM locations as input features into a profile-profile scoring function for sequence alignment.

    • Main Results:

    • The inclusion of SOM locations as input slightly improved the alignment quality of distantly related proteins.
    • This improvement was marginally less than that achieved by predicted secondary structure alone.
    • Combining SOM locations with predicted secondary structure yielded a further small but significant improvement in alignment quality.

    Conclusions:

    • Predicted secondary structure is known to significantly improve protein sequence alignments.
    • This study demonstrates that SOM locations offer complementary information not captured by secondary structure prediction.
    • The integration of SOM locations can further enhance alignment quality beyond the benefits of secondary structure alone.