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Evolutionary profiles from the QR factorization of multiple sequence alignments.

Anurag Sethi1, Patrick O'Donoghue, Zaida Luthey-Schulten

  • 1Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Proceedings of the National Academy of Sciences of the United States of America
|March 3, 2005
PubMed
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We developed a new algorithm for creating evolutionary profiles from protein sequences. This method improves database search performance and aids in identifying novel protein families, like a previously missed cysteinyl-tRNA synthetase (CysRS).

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Evolution

Background:

  • Constructing accurate evolutionary profiles is crucial for understanding protein family relationships and function.
  • Traditional profile generation methods can be computationally intensive and may not capture the full evolutionary diversity of homologous groups.

Purpose of the Study:

  • To present a novel algorithm for generating complete and topologically representative evolutionary profiles.
  • To enhance the efficiency and accuracy of protein sequence database searches.
  • To identify distantly related protein homologs and novel protein families.

Main Methods:

  • Multidimensional QR factorization of numerically encoded multiple sequence alignments.
  • Ordering protein sequences by linear dependence to identify a minimal basis set.

Related Experiment Videos

  • Integrating structural alignment information for distantly related protein families.
  • Utilizing structure-based profiles for homology detection.

    • Main Results:

    • The new algorithm generates smaller, evolutionarily balanced profiles with improved or comparable database search performance.
    • Structural alignments offer advantages over sequence-based methods for protein families with low sequence identity (<30%).
    • Structure-sequence integrated profiles successfully identified a putative class II cysteinyl-tRNA synthetase (CysRS) in archaea, missed by previous studies.
    • Phylogenetic analysis confirmed the monophyletic nature of the identified CysRS group.

    Conclusions:

    • The developed algorithm provides an efficient and effective method for constructing evolutionary profiles.
    • Combining structural and sequence data enhances the detection of distant homologs.
    • This approach facilitates the discovery of novel protein functions and evolutionary relationships, as exemplified by the CysRS finding.