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Related Experiment Videos

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of

I Mihalek1, I Res, O Lichtarge

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. imihalek@bcm.tmc.edu

Bioinformatics (Oxford, England)
|November 24, 2005
PubMed
Summary

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Evolutionary and structural feedback on selection of sequences for comparative analysis of proteins.

Proteins·2006

We developed a new method to select homologous sequences for protein functional surface detection. This approach optimizes sequence selection, improving the accuracy of identifying critical protein regions like active sites.

Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Multiple sequence alignment (MSA) methods are used to identify functional protein surfaces (e.g., active sites, interfaces).
  • The impact of sequence selection on MSA analysis outcomes is often overlooked.
  • Existing methods lack optimization strategies for reliable functional surface detection.

Purpose of the Study:

  • To propose a novel sequence selection strategy for improved functional surface detection in proteins.
  • To optimize the selection of homologous sequences for enhanced accuracy in identifying functionally important regions.

Main Methods:

  • A heuristic Metropolis Monte Carlo strategy was developed for sequence selection from homologues.
  • The method utilizes the clustering of evolutionarily constrained residues to guide optimization.

Related Experiment Videos

  • The approach is demonstrated on a dataset of 50 homodimerizing enzymes with known structures, substrates, and cofactors.

    • Main Results:

    • The proposed method improves the prediction of functional surfaces compared to sequence similarity criteria.
    • It achieves prediction quality comparable to more complex, non-structure-based sequence selection methods.
    • Demonstrated effectiveness using a diverse set of enzyme examples.

    Conclusions:

    • Optimized sequence selection significantly enhances the reliability of detecting functional protein surfaces.
    • This heuristic Monte Carlo approach offers a computationally efficient and effective strategy for bioinformatics analysis.
    • The method provides a valuable tool for understanding protein function and evolution.