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Towards fully automated structure-based function prediction in structural genomics: a case study.

James D Watson1, Steve Sanderson, Alexandra Ezersky

  • 1EMBL--European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. watson@ebi.ac.uk

Journal of Molecular Biology
|February 24, 2007
PubMed
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The ProFunc server uses combined sequence and structure methods to automatically predict protein function, improving accuracy for hypothetical proteins. Automated assessment using Gene Ontology (GO) slims approaches expert manual evaluation.

Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational biology

Background:

  • Global Structural Genomics projects increase the number of proteins with unknown function.
  • Accurate and automated computational methods are needed to assign functions to these proteins.

Purpose of the Study:

  • To develop and evaluate an automated functional analysis server, ProFunc, for predicting protein function.
  • To assess the success of structure-based function prediction methods.

Main Methods:

  • ProFunc server integrates sequence-based and structure-based analyses.
  • Utilized structures solved by Midwest Center for Structural Genomics (MCSG) during the first Protein Structure Initiative (PSI).
  • Developed automated assessment using Gene Ontology (GO) schema and GO-slims.

Related Experiment Videos

Main Results:

  • Two structure-based methods showed particular success in identifying functional clues.
  • Structure-based methods identified local similarity missed by sequence-based methods.
  • ProFunc server increases hit identification chances through complementary approaches.

Conclusions:

  • Automated functional analysis is crucial for large-scale structural genomics.
  • Complementary sequence and structure-based methods enhance protein function prediction.
  • Automated GO-slim assessment provides a reliable alternative to manual evaluation.