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A new protein linear motif benchmark for multiple sequence alignment software.

Emmanuel Perrodou1, Claudia Chica, Olivier Poch

  • 1Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr

BMC Bioinformatics
|April 29, 2008
PubMed
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Detecting linear motifs (LMs) in rapidly evolving protein regions is challenging. Current alignment methods struggle with divergent sequences, hindering the discovery of new functional sites.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Linear motifs (LMs) are crucial short regulatory sequences in eukaryotic proteins, involved in various cellular functions.
  • Current LM detection relies on sequence conservation, but LMs in disordered regions evolve rapidly, complicating alignment.
  • Classical multiple sequence alignment struggles with these rapidly evolving regions, limiting new LM discovery.

Purpose of the Study:

  • To develop a benchmark for assessing multiple sequence alignment methods in detecting and aligning linear motifs.
  • To evaluate the performance of existing alignment programs on this new benchmark.

Main Methods:

  • Developed a benchmark within the BAliBASE suite for evaluating LM alignment capabilities.
  • Utilized experimentally verified functional motifs from the Eukaryotic Linear Motif (ELM) database.

Related Experiment Videos

  • Tested and compared several multiple sequence alignment programs.

    • Main Results:

    • The best alignment program correctly aligned 73% of LMs in distantly related proteins, compared to 48% for the worst.
    • Performance degraded with the inclusion of sequences containing false positive motifs.
    • LM alignment quality ranking mirrored full-length protein alignment rankings, but individual test cases showed little correlation between LM and overall alignment quality.

    Conclusions:

    • No current program reliably aligns linear motifs in distantly related sequences.
    • Identified specific challenges in aligning LMs within rapidly evolving protein regions.
    • The findings suggest avenues for improving alignment program accuracy for divergent sequences.