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Related Experiment Videos

Prediction of protein function from protein sequence and structure.

James C Whisstock1, Arthur M Lesk

  • 1Department of Biochemistry and Molecular Biology, Victorian Bioinformatics Consortium, Monash University, Clayton Campus, ARC Centre for Structural and Functional Microbial Genetics, Victoria, Australia.

Quarterly Reviews of Biophysics
|March 20, 2004
PubMed
Summary

Predicting protein function from genomic data is challenging. New methods combining sequence, structure, and comparative genomics offer improved, though not foolproof, insights into gene product roles.

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Area of Science:

  • Genomics
  • Proteomics
  • Bioinformatics

Background:

  • Genome sequencing yields many proteins with unknown functions.
  • Understanding protein function is crucial for deciphering biological processes and disease mechanisms.
  • Current annotation methods often rely solely on sequence similarity, which can be insufficient.

Purpose of the Study:

  • To review the state-of-the-art in protein function prediction.
  • To describe the difficulties and successes in assigning function to gene products.
  • To explore various computational and experimental approaches for function prediction.

Main Methods:

  • Sequence and 3D structure analysis for identifying homologous proteins.
  • Comparative genomics and analysis of protein-protein interaction patterns.

Related Experiment Videos

  • Inferring function from conserved patterns within protein families.

    • Main Results:

    • Structure-based homology detection can succeed where sequence-based methods fail.
    • Proteins with high sequence or structural similarity can exhibit different functions.
    • Multi-faceted approaches, including comparative genomics, enhance prediction accuracy.
    • Protein function can be context-dependent and multi-faceted, posing a definitional challenge.

    Conclusions:

    • Protein function prediction remains a complex challenge, despite advances in genomics and structural biology.
    • Integrating diverse data types (sequence, structure, interactions, comparative genomics) is key to improving prediction.
    • The inherent ambiguity and multi-functionality of proteins necessitate sophisticated prediction strategies.