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ConFunc--functional annotation in the twilight zone.

Mark N Wass1, Michael J E Sternberg

  • 1Structural Bioinformatics Group, Biochemistry Building, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK.

Bioinformatics (Oxford, England)
|February 12, 2008
PubMed
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ConFunc, an automated protein function prediction tool, significantly outperforms BLAST and PSI-BLAST. It utilizes conserved residues and Gene Ontology annotations for improved accuracy in identifying protein functions, especially in challenging low-similarity cases.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid advancement of genome sequencing has generated a large volume of protein sequences lacking functional annotations.
  • Accurate protein function prediction is crucial for understanding biological processes and disease mechanisms.

Purpose of the Study:

  • To introduce ConFunc, an automated approach for predicting protein function using Gene Ontology (GO) annotations.
  • To evaluate ConFunc's performance against established methods like BLAST and PSI-BLAST, particularly in the 'twilight zone' of low sequence similarity.

Main Methods:

  • ConFunc employs conserved residues to generate sequence profiles for function inference.
  • It segments sequences based on GO annotations, creating sub-alignments for each GO term.

Related Experiment Videos

  • Position-specific scoring matrices are generated from conserved residues within these sub-alignments to build feature-derived profiles.

    • Main Results:

    • ConFunc significantly outperforms both BLAST and PSI-BLAST in protein function prediction.
    • It achieves higher levels of recall and precision, with a maximum precision 24% greater than BLAST.
    • For sequences with low-identity homologs, ConFunc demonstrates a six-fold increase in recall compared to BLAST at high precision levels.

    Conclusions:

    • ConFunc shows substantial potential for integration into automated genomics annotation pipelines.
    • The method offers a robust solution for predicting protein function, especially when sequence similarity is low.
    • This approach enhances the functional annotation of newly sequenced proteins.