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Related Experiment Videos

Knowledge-based selection of targets for structural genomics.

Dmitrij Frishman1

  • 1Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. d.frishman@gsf.de

Protein Engineering
|April 5, 2002
PubMed
Summary
This summary is machine-generated.

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This study presents a computational method for selecting microbial protein targets for structure determination. It efficiently identifies proteins with novel structural domains using genome annotation and predicted features.

Area of Science:

  • Structural Biology
  • Genomics
  • Bioinformatics

Background:

  • Structural genomics projects aim to determine protein structures for microbial pathogens.
  • Efficiently selecting rational targets is crucial for maximizing the impact of these projects.
  • Identifying proteins with unknown structural domains is a key challenge.

Purpose of the Study:

  • To develop a computational procedure for rational target selection in microbial structural genomics.
  • To identify proteins likely to possess novel structural domains.
  • To streamline the process of target selection for protein structure determination.

Main Methods:

  • Utilized the PEDANT genome database for comprehensive annotation.
  • Employed sequence clustering incorporating genomic annotation.

Related Experiment Videos

  • Filtered gene products based on predicted structural features (e.g., known 3D structures, transmembrane regions).

    • Main Results:

    • A flexible computational procedure for target selection was developed.
    • The method effectively identified proteins with potentially unknown structural domains.
    • Filtering reduced computational complexity and the need for extensive homologous family partitioning.
    • Demonstrated exemplary target selection across 32 prokaryotic genomes.

    Conclusions:

    • The described computational approach enables rational target selection for microbial protein structure determination.
    • Integrating genome annotation and predicted structural features enhances the identification of novel structural domains.
    • This method offers an efficient strategy for structural genomics projects.