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Matt: local flexibility aids protein multiple structure alignment.

Matthew Menke1, Bonnie Berger, Lenore Cowen

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Plos Computational Biology
|January 16, 2008
PubMed
Summary
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The Matt program introduces local flexibility into protein structure alignment, improving accuracy for distantly related proteins and distinguishing homologous structures. This computational approach enhances protein structure modeling and template library construction.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure alignment

Background:

  • Protein multiple structure alignment is computationally challenging.
  • Existing methods often struggle with proteins of distant homology.
  • Incorporating geometric flexibility can improve protein structure modeling.

Purpose of the Study:

  • Introduce Matt (Multiple Alignment with Translations and Twists), a novel protein structure alignment algorithm.
  • Evaluate Matt's performance against existing methods on benchmark datasets.
  • Assess Matt's ability to distinguish homologous from non-homologous protein structures.

Main Methods:

  • Developed Matt, an aligned fragment pair chaining algorithm allowing local flexibility (translations and rotations) between fragments.

Related Experiment Videos

  • Utilized dynamic programming for alignment assembly.
  • Tested Matt on Homstrad and SABmark benchmark datasets.
  • Calculated p-value scores based on common core length and RMSD.

    • Main Results:

    • Matt demonstrated competitive performance on Homstrad and outperformed other programs on SABmark.
    • Matt better aligns alpha-helix and beta-strand ends.
    • A p-value score effectively separated homologous from non-homologous protein structures in SABmark.
    • Matt's performance is attributed to modeling conformational states and backbone distortions.

    Conclusions:

    • Matt offers improved protein structure alignment, particularly for distantly homologous proteins.
    • The algorithm's flexibility enhances modeling of conformational variations and distortions.
    • Matt's alignments are useful for constructing structural templates and distinguishing protein homology.