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Related Experiment Videos

Predicting protein function from domain content.

Kristoffer Forslund1, Erik L L Sonnhammer

  • 1Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden. Kristoffer.Forslund@sbc.su.se

Bioinformatics (Oxford, England)
|July 2, 2008
PubMed
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Protein domain combinations predict function more accurately than traditional methods. Two new models, rule-based and probabilistic, improve Gene Ontology annotation transfer for bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein function prediction is crucial in the post-genome era.
  • Protein domains are key features for functional assignment.
  • Understanding how domain combinations encode function is vital.

Purpose of the Study:

  • To investigate the relationship between protein domain combinations and function.
  • To develop computational models for predicting protein function based on domain content.
  • To improve Gene Ontology (GO) annotation transfer.

Main Methods:

  • Developed two models: a rule-based model generalizing Pfam2GO mapping and a probabilistic model.
  • Implemented models as predictors of GO functional annotation terms.

Related Experiment Videos

  • Evaluated predictor accuracy against conventional best BLAST-hit annotation and single-domain models.

    • Main Results:

    • Both rule-based and probabilistic models improved Gene Ontology annotation transfer accuracy.
    • The probabilistic model demonstrated better performance with incomplete training datasets.
    • Predictors were more accurate and sensitive than existing methods on a large-scale dataset.
    • Identified novel functional terms predicted by domain combinations not evident from individual domains.

    Conclusions:

    • Protein domain combinations significantly contribute to specific protein functions.
    • Computational models based on domain combinations enhance protein function prediction.
    • These models offer a more sensitive and accurate approach to Gene Ontology annotation.