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A protein structure data and analysis system.

Hao Tian1, Rajshekhar Sunderraman, Irene Weber

  • 1Department of Computer Science, Georgia State University, Atlanta, GA 30303 USA (phone:404-463-2825; fax: 404-463-9812;

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
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This study introduces a novel protein structure data system for life scientists. It enables analysis of proprietary and public protein data, aiding in structure comparison and prediction.

Area of Science:

  • Structural Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Life scientists require tools to manage and analyze both public and proprietary protein structure data.
  • Existing systems may not adequately support the integration and comparison of incomplete or novel protein structures.

Purpose of the Study:

  • To design and implement a comprehensive protein structure data and analysis system.
  • To provide life scientists with a tool for managing, querying, and comparing protein structures.
  • To facilitate the analysis of proprietary data and aid in tertiary structure prediction.

Main Methods:

  • Development of a Java-based software toolkit.
  • Integration of a relational database (Oracle 9i) for data storage.
  • Implementation of functions for storing public (e.g., Protein Data Bank) and proprietary protein data.

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  • Development of comparison algorithms for atom-level structure analysis, including mutant proteins.

    • Main Results:

    • A functional system capable of storing and analyzing diverse protein structure datasets.
    • Enabled detailed comparison of protein structures, particularly useful for identifying differences in mutant proteins.
    • Provided a resource for selecting optimal templates in tertiary structure prediction.

    Conclusions:

    • The developed system effectively supports life scientists in protein structure analysis and data management.
    • The toolkit enhances the ability to compare protein structures and aids in predicting new protein structures.
    • This integrated approach streamlines the analysis of both public and private protein structural information.